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Artificial intelligence (AI), machine learning (ML), and data science have become some of the most significant topics of discussion in today’s technological era. In one of the speakers’ sessions on the ‘Future of Data and AI’, several experts in these fields came together to discuss the latest advancements and how they are using them in their everyday work. 

Introduction of panelists 

The session starts with Hamza, a research science manager at Google, introducing himself and explaining how he runs a few ML models and helps build models that can predict user abuse. Hamza works in the trust and safety group within search, where they prioritize the protection of users. 

Generative AI: Trends, Ethics and Societal Impact – Watch the complete session  

The other experts introduce themselves as well. Batool, who has experience working as an AI scientist at amazon, focused on dialogue machines and natural language understanding.

Meanwhile, Francesca, a principal data scientist manager at Microsoft, leads teams of data scientists and ML scientists, working on internal problems at Microsoft. Raja, the founder, and chief data scientist at Data Science Dojo, has been working in data science before it was even called data science. 

Use of Generative AI 

The conversation then shifts to the use of generative AI, which has been used in the field of data science and ML for a while. Francesca explains that there are three main categories where generative AI is used every day in her work.  

The first is generating natural language, which includes summarization, translation, and question-answering systems. The second is an image and video generation, which has applications in industries like gaming and advertising. The third is generating music, which can be used for composing, arranging, and creating personalized music. 

A deeper understanding of the current state of the field 

The experts then discuss the latest advancements in these fields. Raja emphasizes the importance of the latest advancements in deep learning, specifically transformers, in NLP tasks. He also mentions the development of large-scale language models like GPT-3, which can perform tasks like translation, summarization, and question-answering. 

Matul discusses how chatbots have evolved from rule-based systems to data-driven systems, where they can use data to train and improve their performance. This includes using natural language processing to understand and respond to user queries more effectively. 

Francesca highlights the importance of democratizing AI and making it accessible to all people, regardless of their technical background. This involves developing user-friendly tools that can be used by people without technical expertise, which can be used to address common business problems. 

Generative AI – The impact of ground-breaking generative AI technologies 

Open AI has brought about a major transformation in the field of artificial intelligence (AI), data science, and machine learning. One of the most significant contributions of open AI is its generative AI capabilities that help in generating code, images, and troubleshooting bugs. These capabilities are particularly useful for data scientists who need to deploy and operationalize their machine-learning applications. 

Generating code from one programming language to another is one of the three main categories where generative AI applications have been seeing a lot of demand. Another popular application of generative AI is in generating images, especially for use cases such as generating images from text descriptions. 

For data scientists like the speaker, who work mostly in the AI, data science, and machine learning space, most of their work is done on the cloud. With open AI, data scientists can now access pre-trained generative AI models and customize them with their data. They can also use built-in tools to detect and mitigate any biases or unfair dynamics that may exist in their applications. 

Open AI has made accessing these tools easier through the open AI studio, where one can build AI models and deploy them faster. The speaker has found this to be a privileged situation and has been using generative AI for various communication purposes such as spot-checking, rephrasing, and creating snippets for social media posts. 

Human intelligence in conjunction with AI 

While AI has brought about a significant change in the field of content creation, the speaker warns against relying solely on AI. Human intelligence should be used in conjunction with AI to create the best results. AI is just another tool that should be used with caution, as a few wrong jumps can take you in the wrong direction. 

The other speakers in the panel discussion also shared their experiences with generative AI. One of them is writing a book that covers popular machine learning algorithms using fiction. While, until a few years back, his biggest concern was hiring graphic designers and concept artists, now, with generative AI, he can create his book’s graphics on his own. 

Generative AI’s impact on creative work  

Generative AI is impacting creative work and work in general in many ways. In creative industries, such as marketing, graphic design, animation, and content creation, generative AI is a valuable tool that allows for faster and more efficient production of high-quality content. It can also democratize access to expensive resources like models for photo shoots, making them more accessible to smaller designers. 

In other industries, such as manufacturing, healthcare, and energy, generative AI can also be used to improve efficiency and productivity. For example, it can be used to design new products, optimize manufacturing processes, and analyze medical images. 

Overall, generative AI has the potential to impact work across many different industries, and its adoption is likely to continue to grow as more businesses discover its benefits. While it may not eliminate jobs, it will likely change the nature of work in many industries, requiring workers to learn new skills to work effectively with these tools. 

Read about 12 must-have AI tools to revolutionize your work 

Francesca, emphasizes the importance of considering the ethical implications of working with AI systems, not just generative AI. She has a checklist of principles that she follows, such as fairness, reliability, safety, privacy and security, inclusiveness, accountability, and transparency, which are industry standards developed by tech companies. While principles are essential to keep in mind, Francesco also suggests that tools such as interpretML and fair AI can be leveraged to understand the impact of data on predictions and results better.  

OpenAI and generative AI have many benefits, such as improving content quality, variety, and personalization. However, to ensure these benefits follow ethical principles, the model life cycle, which starts with data collection, pre-processing, model building, and tuning model parameters and ends with prediction and interpretation, must involve humans in all stages.

Generative AI in healthcare and energy

AI in healthcare

There are many exciting ways that generative AI is being used to tackle important problems in the fields of healthcare and energy. One area where generative AI is being used in healthcare is in the creation of medical images such as X-rays and MRIs. With the help of generative AI, researchers can generate high-quality medical images that can help in the diagnosis and treatment of various medical conditions. 

It is also being used to develop new drugs and treatments. With the help of deep learning algorithms, researchers can analyze large amounts of medical data to identify new drug candidates and develop personalized treatment plans for patients. 

In the field of energy, generative AI is being used to optimize energy systems and reduce energy consumption. For example, AI models can be trained to predict energy usage patterns and adjust energy supply, accordingly, reducing waste and increasing efficiency. 

Another area where generative AI is being used is in the creation of virtual environments for training purposes. With the help of generative AI, researchers can create realistic virtual environments that can be used to train individuals in various fields such as medicine, engineering, and military training. This can help to reduce the risk of accidents and injuries during training and improve overall safety. 

Generative AI and government regulations

Overall, the role of the government in regulating the use of generative AI to create content is a highly debated topic. Some believe that the government should intervene to prevent monopolies from happening and to fund open-source projects to democratize data. Others argue that too much regulation could stifle innovation and competition.  

It is essential to strike a balance between promoting innovation and protecting consumers’ interests. Legislation and regulations could be created to define what constitutes fair use and set standards for the ethical use of AI, such as the AI bill of rights. Ultimately, governments will act following the general culture and society’s values in their region, making laws that align with what is considered acceptable. 

Closing of the session – Generative AI  

In conclusion, AI, ML, and data science have become vital to our daily lives, with advancements in these fields impacting various industries. With the continuous development of new technology, it is essential to keep up to date with the latest trends and advancements to stay competitive in the industry. The experts in the session provided valuable insights into the latest advancements and how they are using them in their everyday work. As these fields continue to evolve, it will be exciting to see what new advancements will come next. 

Data analytics is the driving force behind innovation, and staying ahead of the curve has never been more critical. That is why we have scoured the landscape to bring you the crème de la crème of data analytics conferences in 2023.  

Data analytics conferences provide an essential platform for professionals and enthusiasts to stay current on the latest developments and trends in the field. By attending these conferences, attendees can gain new insights, and enhance their skills in data analytics.

These events bring together experts, practitioners, and thought leaders from various industries and backgrounds to share their experiences and best practices. Such conferences also provide an opportunity to network with peers and make new connections.  

Data analytics conferences to look forward to

In 2023, there will be several conferences dedicated to this field, where experts from around the world will come together to share their knowledge and insights. In this blog, we will dive into the top data analytics conferences of 2023 that data professionals and enthusiasts should add to their calendars.

Strata Data Conference   

The Strata Data Conference is one of the largest and most comprehensive data conferences in the world. It is organized by O’Reilly Media and will take place in San Francisco, CA in 2023. It is a leading event in data analytics and technology, focusing on data and AI to drive business value and innovation. The conference brings together professionals from various industries, including finance, healthcare, retail, and technology, to discuss the latest trends, challenges, and solutions in the field of data analytics.   

This conference will bring together some of the leading data scientists, engineers, and executives from across the world to discuss the latest trends, technologies, and challenges in data analytics. The conference will cover a wide range of topics, including artificial intelligence, machine learning, big data, cloud computing, and more. 

Big Data & Analytics Innovation Summit  

The Big Data & Analytics Innovation Summit is a premier conference that brings together experts from various industries to discuss the latest trends, challenges, and solutions in data analytics. The conference will take place in London, England in 2023 and will feature keynotes, panel discussions, and hands-on workshops focused on topics such as machine learning, artificial intelligence, data management, and more.  

Attendees can attend keynote speeches, technical sessions, and interactive workshops, where they can learn about the latest technologies and techniques for collecting, processing, and analyzing big data to drive business outcomes and make informed decisions. The connection between the Big Data & Analytics Innovation Summit and data analytics lies in its focus on the importance of big data and the impact it has on businesses and industries. 

Predictive Analytics World   

Predictive Analytics World is among the leading data analytics conferences that focus specifically on the applications of predictive analytics. It will take place in Las Vegas, NV in 2023. Attendees will learn about the latest trends, technologies, and solutions in predictive analytics and gain valuable insights into this field’s future.  

At PAW, attendees can learn about the latest advances in predictive analytics, including techniques for data collection, data preprocessing, model selection, and model evaluation. For the unversed, Predictive analytics is a branch of data analytics that uses historical data, statistical algorithms, and machine learning techniques to make predictions about future events. 

AI World Conference & Expo   

The AI World Conference & Expo is a leading conference focused on artificial intelligence and its applications in various industries. The conference will take place in Boston, MA in 2023 and will feature keynote speeches, panel discussions, and hands-on workshops from leading AI experts, business leaders, and data scientists. Attendees will learn about the latest trends, technologies, and solutions in AI and gain valuable insights into this field’s future.  

The connection between the AI World Conference & Expo and data analytics lies in its focus on the importance of AI and data in driving business value and innovation. It highlights the significance of AI and data in enhancing business value and innovation. The event offers attendees an opportunity to learn from leading experts in the field, connect with other professionals, and stay informed about the most recent developments in AI and data analytics. 

Data Science Summit   

Last on the data analytics conference list we have the Data Science Summit. It is a premier conference focused on data science applications in various industries. The meeting will take place in San Diego, CA in 2023 and feature keynote speeches, panel discussions, and hands-on workshops from leading data scientists, business leaders, and industry experts. Attendees will learn about the latest trends, technologies, and solutions in data science and gain valuable insights into this field’s future.  

Special mention – Future of Data and AI

Hosted by Data Science Dojo, Future of Data and AI is an unparalleled opportunity to connect with top industry leaders and stay at the forefront of the latest advancements. Featuring 20+ industry experts, the two-day virtual conference offers a diverse range of expert-level knowledge and training opportunities.

Don’t worry if you missed out on the Future of Data and AI Conference! You can still catch all the amazing insights and knowledge from industry experts by watching the conference on YouTube.

Bottom line

In conclusion, the world of data analytics is constantly evolving, and it is crucial for professionals to stay updated on the latest trends and developments in the field. Attending conferences is one of the most effective ways to stay ahead of the game and enhance your knowledge and skills.  

The 2023 data analytics conferences listed in this blog are some of the most highly regarded events in the industry, bringing together experts and practitioners from all over the world. Whether you are a seasoned data analyst, a new entrant in the field, or simply looking to expand your network, these conferences offer a wealth of opportunities to learn, network, and grow.

So, start planning and get ready to attend one of these top conferences in 2023 to stay ahead of the curve. 

In this blog, we will share the list of leading data science conferences across the world to be held in 2023. This will help you to learn and grow your career in data science, AI, and machine learning.

The Future of Data and AI conference hosted by Data Science Dojo is an upcoming event aimed at exploring the advancements and innovations in the field of artificial intelligence and data. The conference is scheduled on March 1st-2nd, 2023 and it is expected to bring together experts from the industry, academia, and government to share their insights and perspectives on the future direction of AI and data technologies.

Attendees can expect to learn about the latest trends and advancements in AI and data, such as machine learning, deep learning, big data, and cloud computing. They will also have the opportunity to hear from leading experts in the field and engage in discussions and debates on the ethical, social, and economic implications of these technologies.

Here are the reasons you must not miss Future of Data and AI conference

In addition to the keynote speeches and panel discussions, the conference will also feature hands-on workshops and tutorials, where attendees can learn and apply new skills and techniques related to AI and data. The conference is an excellent opportunity for professionals, researchers, students, and anyone interested in the future of AI and data to network, exchange ideas, and build relationships with others in the field.

2. AAAI Conference on Artificial Intelligence – Washington DC, United States 

The AAAI Conference on Artificial Intelligence (AAAI) is a leading conference in the field of artificial intelligence research. It is held annually in Washington, DC and attracts researchers, practitioners, and students from around the world to present and discuss their latest work.  

The conference features a wide range of topics within AI, including machine learning, natural language processing, computer vision, and robotics, as well as interdisciplinary areas such as AI and law, AI and education, and AI and the arts. It also includes tutorials, workshops, and invited talks by leading experts in the field. The conference is organized by the Association for the Advancement of Artificial Intelligence (AAAI), which is a non-profit organization dedicated to advancing AI research and education. 

3. Women in Data Science (WiDS) – California, United States 

Women in Data Science (WiDS) is an annual conference held at Stanford University, California, United States and other locations worldwide. The conference is focused on the representation, education, and achievements of women in the field of data science. WiDS is designed to inspire and educate data scientists worldwide, regardless of gender, and support women in the field.  

The conference is a one-day technical conference that provides an opportunity to hear about the latest data science related research, and applications in various industries, as well as to network with other professionals in the field.

The conference features keynote speakers, panel discussions, and technical presentations from prominent women in the field of data science. WiDS aims to promote gender diversity in the tech industry, and to support the career development of women in data science. 

4. Gartner Data and Analytics Summit – Florida, United States 

The Gartner Data and Analytics Summit is an annual conference that is held in Florida, United States. The conference is organized by Gartner, a leading research and advisory company, and is focused on the latest trends, strategies, and technologies in data and analytics.  

The conference brings together business leaders, data analysts, and technology professionals to discuss the latest trends and innovations in data and analytics, and how they can be applied to drive business success.  

The conference features keynote presentations, panel discussions, and breakout sessions on topics such as big data, data governance, data visualization, artificial intelligence, and machine learning. Attendees also have the opportunity to meet with leading vendors and solutions providers in the data and analytics space, and network with peers in the industry.  

The Gartner Data and Analytics Summit is considered as a leading event for professionals in the data and analytics field. 

 5. ODSC East – Boston, United States 

ODSC East is a conference on open-source data science and machine learning held annually in Boston, United States. The conference features keynote speeches, tutorials, and training sessions by leading experts in the field, as well as networking opportunities for attendees.  

The conference covers a wide range of topics in data science, including machine learning, deep learning, big data, data visualization, and more. It is designed for data scientists, developers, researchers, and practitioners looking to stay up-to-date on the latest advancements in the field and learn new skills.  

6. AI and Big Data Expo North America – California, United States 

AI and Big Data Expo North America is a technology event that focuses on artificial intelligence (AI) and big data. The conference takes place annually in Santa Clara, California, United States. The event is for enterprise technology professionals seeking to explore the latest innovations, implementations, and strategies in AI and big data.  

The event features keynote speeches, panel discussions, and networking opportunities for attendees to connect with leading experts and industry professionals. The conference covers a wide range of topics, including machine learning, deep learning, big data, data visualization, and more.  

7. The Data Science Conference – Chicago, United States 

The Data Science Conference is an annual data science conference held in Chicago, United States. The conference focuses on providing a space for analytics professionals to network and learn from one another without being prospected by vendors, sponsors, or recruiters.  

The conference is by professionals for professionals and the material presented is substantial and relevant to the data science practitioner. It is the only sponsor-free, vendor-free, and recruiter-free data science conference℠. The conference covers a wide range of topics in data science, including artificial intelligence, machine learning, predictive modeling, data mining, data analytics and more. 

Enroll yourself in Data Science Bootcamp to grow your career

8. Machine Learning Week – Las Vegas, United States 

Machine Learning Week is a large conference that focuses on the commercial deployment of machine learning. It is set to take place in Las Vegas, United States, with the venue being the Red Rock Casino Resort Spa. The conference will have seven tracks of sessions, with six co-located conferences that attendees can register to attend: PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, PAW Climate and Deep Learning World. 

9. International Conference on Mass Data Analysis of Images and Signals – New York, United States 

The International Conference on Mass Data Analysis of Images and Signals (MDA) is a yearly conference that focuses on various applications of Artificial Intelligence and Pattern Recognition in fields such as Medicine, Biotechnology, Food Industries and Dietetics, Biometry, Agriculture, Drug Discovery, and System Biology.  

The conference is not limited to these specific topics and welcomes research from other related fields as well. The conference has been held on a yearly basis 

10. International Conference on Data Mining (ICDM) – New York, United States 

The International Conference on Data Mining (ICDM) is an annual conference held in New York, United States that focuses on the latest research and developments in the field of data mining. The conference brings together researchers and practitioners from academia, industry, and government to present and discuss their latest research findings, ideas, and applications in data mining. The conference covers a wide range of topics, including machine learning, data mining, big data, data visualization, and more. 

11. International Conference on Machine Learning and Data Mining (MLDM) – New York, United States 

International Conference on Machine Learning and Data Mining (MLDM) is an annual conference held in New York, United States. The conference focuses on the latest research and developments in the field of machine learning and data mining. The conference brings together researchers and practitioners from academia, industry, and government to present and discuss their latest research findings, ideas, and applications in machine learning and data mining.  

The conference covers a wide range of topics, including machine learning, data mining, big data, data visualization, and more. The conference is considered a premier forum for researchers and practitioners to share their latest research, ideas and development in machine learning and data mining and related areas. 

12. AI in Healthcare Summit – Boston, United States 

AI in Healthcare Summit is an annual event that takes place in Boston, United States. The summit focuses on showcasing the opportunities of advancing methods in AI and machine learning (ML) and their impact across healthcare and medicine.

The event features a global line-up of experts who will present the latest ML tools and techniques that are set to revolutionize healthcare applications, medicine and diagnostics. Attendees will have the opportunity to discover the AI methods and tools that are set to revolutionize healthcare, medicine and diagnostics, as well as industry applications and key insights. 

13. Big Data and Analytics Summit – Ontario, Canada

The Big Data and Analytics Summit is an annual conference held in Ontario, Canada. The conference focuses on connecting analytics leaders to the latest innovations in big data and analytics as the world adapts to new business realities after the global pandemic. Businesses need to innovate in products, sales, marketing and operations and big data is now more critical than ever to make this happen and help organizations thrive in the future. The conference features leading industry experts who will discuss the latest trends exploding across the big data landscape, including security, architecture and transformation, cloud migration, governance, storage, AI and ML and so much more.
 

14. Deep Learning Summit – Montreal, Canada

The Deep Learning Summit is an annual conference held in Montreal, Canada. The conference focuses on providing attendees access to multiple stages to optimize cross-industry learnings and collaboration.

Attendees can solve shared problems with like-minded attendees during round table discussions, Q&A sessions with speakers or schedule 1:1 meeting. The conference also provides an opportunity for attendees to connect with other attendees during and after the summit and build new collaborations through interactive networking sessions. 

15. Enterprise AI Summit – Montreal, Canada 

The Enterprise AI Summit is an annual conference that takes place in Montreal, Canada. The conference is organized by RE-WORK LTD, and it is scheduled for November 1-2, 2023. The conference will feature the Deep Learning Summit and Enterprise AI Summit as part of the Montreal AI Summit.

The conference is an opportunity for attendees to learn about the latest advancements in AI and Machine Learning and how it can be applied in the enterprise. The conference is a 2-day event that features leading industry experts who will share their insights and experiences on AI and ML in the enterprise 

16. Extraction and Knowledge Management Conference (EGC) – Lyon, France 

The Extraction and Knowledge Management Conference (EGC) is an annual event that brings together researchers and practitioners from various disciplines related to data science and knowledge management. The conference will be held on the Berges du Rhône campus of the Université Lumière Lyon 2, from January 16 to 20, 2023. The conference provides a forum for researchers, students, and professionals to present their research results and exchange ideas and discuss future challenges in knowledge extraction and management. 

17. Women in AI and Data Reception – London, United Kingdom 

The Women in AI and Data Reception is an event organized by RE•WORK in London, United Kingdom that takes place on January 24th, 2023. The conference aims to bring together leading female experts in the field of artificial intelligence and machine learning to discuss the impact of this rapidly advancing technology on various sectors such as finance, retail, manufacturing, transport, healthcare and security. Attendees will have the opportunity to hear from these experts, establish new connections and network with peers 

18. Chief Data and Analytics Officers (CDAO) – London, United Kingdom 

The Chief Data and Analytics Officers (CDAO) conference is an annual event organized by Corinium Global Intelligence, which brings together senior leaders from the data and analytics space. The conference is focused on the acceleration of the adoption of data, analytics and AI in order to generate decision advantages across various industries.

The conference will take place on September 13-14, 2023, in Washington D.C. and will include sessions on latest trends, strategies, and best practices for data and analytics, as well as networking opportunities for attendees. 

19. International Conference on Pattern Recognition Applications and Methods (ICPRAM) – Lisbon, Portugal 

The International Conference on Pattern Recognition Applications and Methods (ICPRAM) is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning. It will be held in Lisbon, Portugal and submissions for abstracts and doctoral consortium papers are due on January 2, 2023.

Registration to ICPRAM also allows free access to the ICAART conference as a non-speaker. It is a annual event where researchers can exchange ideas and discuss future challenges in pattern recognition and machine learning
 

20. AI in Finance Summit – London, United Kingdom 

The AI in Finance Summit, taking place in London, United Kingdom, is an event that brings together leaders in the financial industry to discuss the latest advancements and innovations in artificial intelligence and its applications in finance. Attendees will have the opportunity to hear from experts in the field, network with peers, and learn about the latest trends and technologies in AI and finance. The summit will cover topics such as investment, risk management, fraud detection, and more 

21. The Martech Summit – Hong Kong 

The Martech Summit is an event that brings together the best minds in marketing technology from a range of industries through a number of diverse formats and engaging events. The conference aims to bring together people in senior leadership roles, such as C-suites, Heads, and Directors, to learn and network with industry experts.

The MarTech Summit series includes various formats such as The MarTech Summit, The Virtual MarTech Summit, Virtual MarTech Spotlight, and The MarTech Roundtable. 

22. AI and Big Data Expo Europe – Amsterdam, Netherlands 

The AI and Big Data Expo Europe is an event that takes place in Amsterdam, Netherlands. The event is scheduled to take place on September 26-27, 2023, at the RAI, Amsterdam. It is organized by Encore Media.

The event will explore the latest innovations within AI and Big Data in 2023 and beyond and covers the impact AI and Big Data technologies have on many industries including manufacturing, transport, supply chain, government, legal and more. The conference will also showcase next generation technologies and strategies from the world of Artificial Intelligence.  

23. International Symposium on Artificial Intelligence and Robotics (ISAIR) – Beijing, China 

The International Symposium on Artificial Intelligence and Robotics (ISAIR) is a platform for young researchers to share up-to-date scientific achievements in the field of Artificial Intelligence and Robotics. The conference is organized by the International Society for Artificial Intelligence and Robotics (ISAIR), IEEE Big Data TC, and SPIE. It aims to provide a comprehensive conference focused on the latest research in Artificial Intelligence, Robotics and Automation in Space.
 

24. The Martech Summit – Jakarta, Indonesia 

The Martech Summit – Jakarta, Indonesia is a conference organized by BEETC Ltd that brings together the best minds in marketing technology from a range of industries through a number of diverse formats and engaging events. The conference aims to provide a platform for attendees to learn about the latest trends and innovations in marketing technology, with an agenda that includes panel discussions, keynote presentations, fireside chats, and more.
 

25. Web Search and Data Mining (WSDM) – Singapore 

The 16th ACM International WSDM Conference will be held in Singapore on February 27 to March 3, 2023. The conference is a highly selective event that includes invited talks and refereed full papers. The conference focuses on publishing original and high-quality papers related to search and data mining on the Web. The conference is organized by the WSDM conference series and is a platform for researchers to share their latest scientific achievements in this field.
 

26. Machine Learning Developers Summit – Bangalore, India 

The Machine Learning Developers Summit (MLDS) is a 2-day conference that focuses on machine learning innovation. Attendees will have direct access to top innovators from leading tech companies who will share their knowledge on the software architecture of ML systems, how to produce and deploy the latest ML frameworks, and solutions for business use cases. The conference is an opportunity for attendees to learn how machine learning can add potential to their business and gain best practices from cutting-edge presentations 

 

Read more about Machine Learning conferences in Asia

 

27. CISO Malaysia – Kuala Lumpur, Malaysia 

CISO Malaysia 2023 is a conference designed for Chief Information Security Officers (CISOs), Chief Security Officers (CSOs), Directors, Heads, Managers of Cyber and Information Security, and cybersecurity practitioners from across sectors in Malaysia. The conference will be held on February 14, 2023, in Kuala Lumpur, Malaysia. It aims to provide a platform for attendees to get inspired, make new contacts and learn how to uplift their organization’s security program to meet the requirements set by the government and citizens.   

Which data science conferences would you like to participate in? 

In conclusion, data science and AI conferences are an invaluable opportunity to stay up to date with the latest developments in the field, network with industry leaders and experts, and gain valuable insights and knowledge. These are some of the top conferences in the field and offer a wide range of topics and perspectives. Whether you are a researcher, practitioner, or student, these conferences are a valuable opportunity to further your understanding of data science and AI and advance your career.  

Additionally, there are many other conferences out there that might be specific to a certain industry or region, it’s important to research and find the one that fits your interest and needs. Attending these conferences is a great way to stay ahead of the curve and make meaningful connections within the data science and AI community. 

In this blog, we will have a look at the list of top 10 Machine Learning Demos offered by Data Science Dojo that will provide ease to use ML (Machine Learning) techniques free.  

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At Data Science Dojo, we publish blogs every day to keep our audience updated and informed about current industry trends. Here are the top 10 blogs of 2022, based on page views/traffic. Have a look at the top 10 blogs that you should not miss reading before the year-end.

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In this article, we will be highlighting the top 10 discussions of the year that have garnered a lot of attention from learners at Data Science Dojo. 

Every year Data Science Dojo serves a lot of students from different geographics and academic backgrounds with its detailed courses and informative sessions. All these students have one single goal; to be equipped with enough baseline understanding of data science that they can learn and become experts eventually on their own.

In order to expedite the process and make it more collaborative, we make use of different resources, and our discussion platform is one of the most effective ones. 

1. Operators in query matching   

Operators are an important aspect of query matching, as they allow users to specify the type of relationship, they are seeking between different search terms. The ‘LIKE’ operator is one of the most used ones and helps the user in separating out data with specific characteristics. This Q/A will help you understand the LIKE operator and will also provide you with a code runner to practice as well. 

Check out the discussion here: Operators in query matching 

2. Alias command 

The alias command is an important tool for improving the efficiency and organization of command line workflows. It is widely used by system administrators, developers, and other users who work with the command line on a regular basis. This short snippet on Alias Command will give you a basic understanding of what Alias command can do and how it can help you improve the organization of your code.

Check out the discussion here: Alias command

3. Data wrangling in Python

 Data wrangling, also known as data munging or data preparation, is the process of cleaning, organizing, and transforming raw data into a format that is more suitable for analysis and visualization.

This is a crucial step in the data science process, as it helps to ensure that the data is in a usable and accurate form before it is analyzed. If you are looking for the shortest crash course on data wrangling, this discussion is for you, and it comes with some very easy hands-on practice exercises as well.

Check out the discussion here: Data wrangling in Python

4. Clauses of SELECT query

The SELECT clause is a crucial part of the SQL language, as it is used to specify the columns or expressions that should be included in the results of a query. The SELECT clause is the first command that is taught to SQL students. If you want to take the first step towards learning SQL, this short article is the best way to take that first step. 

Check out the discussion here: Clauses of SELECT query

5. JOIN and its different types 

Now that you have learned SELECT, the JOIN is the next step. The JOIN clause is a key component of the SQL language, as it allows users to combine data from multiple tables in a single query.

The JOIN clause is a powerful tool for working with data in SQL, and a good understanding of its various clauses is essential for anyone working with data in a SQL database. But before you take on a SQL database, read our small but concise intro to the JOIN clause in this Q/A. 

Check out the discussion here: JOIN and its different types

6. Lambda functions 

Lambda functions, also known as anonymous functions, are a powerful tool in programming languages that support them, including Python, C#, and JavaScript. They allow developers to create small, self-contained functions that can be passed as arguments to other functions or used to define the behavior of other objects. 

Use our code runner in this discussion and create your first lambda function today. 

Check out the discussion here: Lambda functions

7. Matplotlib 

Matplotlib is a powerful and widely used library for creating data visualizations in Python. The ability to effectively plot and visualize data is an important skill for any data scientist or analyst and knowing the fundamentals of plotting with matplotlib is essential for anyone working with data in Python.

If you are a Python beginner and want to use it to create effective visualizations, this Q/A can get you started right away.

Check out the discussion here: Matplotlib

8. Sub-query

A subquery is a query that is nested within another query, and it is used to retrieve data that is used in the outer query. If you are struggling with the idea of nesting and how you can use it more effectively, visit this discussion and get answers instantly. 

Check out the discussion here: Sub-query 

9. Break, continue, and pass 

The break, continue, and pass statements are control flow statements. These are commonly used in programming languages such as Python, C, and Java.

The ability to use break, continue, and pass statements is an important skill for any programmer, as it allows them to control the flow of their code and implement the desired logic and behavior more effectively. If you want to start using the break, continue and pass statements in your code today but need to practice before you give it a go, this Q/A is for you. 

Check out the discussion here: Break, continue, and pass

10.Window functions 

Window functions, also known as OVER functions, are a powerful tool in SQL that allow users to perform calculations or aggregations on a set of rows, rather than on the entire table.

The ability to use window functions is an important skill for anyone working with data in a SQL database and understanding their different types and uses can help users to more effectively and efficiently retrieve and manipulate data.

If these sound like the skills that you need, visit this discussion and start practicing right away. 

Check out the discussion: Window functions

Learn from our discussion platform

These discussions cover a wide range of topics, but we make sure that these topics cover fundamentals so that they become the first step towards our students’ data science learning journey. 

Whether you’re a beginner or a seasoned data scientist, these discussions will provide you with a better understanding of the fundamental concepts along with a bit of hands-on practice, thanks to our embedded code runner. 

Data Science Dojo strongly believes in learning and growing together as a community, for that reason last year we conducted several community events for you all. Although all the events were helpful in their way, there are top 10 events that received the highest number of sign-ups, are: 

(more…)

There are several informative data science podcasts out there right now, giving you everything you need to stay up to date on what’s happening. We previously covered many of the best podcasts in this blog, but there are lots more that you should be checking out. Here are 10 more excellent podcasts to try out. 

1. Analytics Power Hour 

Every week hosts, Michael Helbling, Tin Wilson, and Moe Kiss cover a different analytics topic that you may want to know about. The show was founded on the premise that the best discussions always happen at drinks after a conference or show. 

Recent episodes have covered topics like analytics job interviews, data as a product, and owning vs. helping in analytics. There are a lot to learn here, so they’re well worth a listen. 

2. DataFramed

This podcast is hosted by DataCamp, and in it, you’ll get interviews with some of the top leaders in data. “These interviews cover the entire range of data as an industry, looking at its past, present, and future. The guests are from both the industry and academia sides of the data spectrum too” says Graham Pierson, a tech writer at Ox Essays and UK Top Writers.   

There are lots of episodes to dive into, such as ones on building talent strategy, what makes data training programs successful, and more. 

3. Lex Fridman Podcast

If you want a bigger picture of data science, then listen to this show. The show doesn’t exclusively cover data science anymore, but there’s plenty here that will give you what you’re looking for. 

You’ll find a broader view of data, covering how data fits in with our current worldview. There are interviews with data experts so you can get the best view of what’s happening in data right now. 

4. The Artists of Data Science

This podcast is geared toward those who are looking to develop their career in data science. If you’re just starting, or are looking to move up the ladder, this is for you. There’s lots of highly useful info in the show that you can use to get ahead. 

There are two types of episodes that the show releases. One is advice from experts, and the others are ‘happy hours, where you can send in your questions and get answers from professionals. 

5. Not So Standard Deviations

This podcast comes from two experts in data science. Roger Peng is a professor of biostatistics at John Hopkins School of Public Health, and Hilary Parker is a data scientist at Stitch Fix. They cover all the latest industry news while bringing their own experience to the discussion.

Their recent episodes have covered subjects like QR codes, the basics of data science, and limited liability algorithms. 

Find out other exciting  18 Data Science podcasts

6. Gradient Dissent  

Released twice a month, this podcast will give you all the ins and outs of machine learning, showing you how this tech is used in real-life situations. That allows you to see how it’s being used to solve problems and create solutions that we couldn’t have before. 

Recent episodes have covered high-stress scenarios, experience management, and autonomous checkouts. 

7. In Machines We Trust

This is another podcast that covers machine learning. It describes itself as covering ‘the automation of everything, so if that’s something you’re interested in, you’ll want to make sure you tune in. 

“You’ll get a sense of what machine learning is being used for right now, and how it impacts our daily lives,” says Yvonne Richards, a data science blogger at Paper Fellows and Boom Essays. The episodes are around 30 mins long each, so it won’t take long to listen and get the latest info that you’re looking for. 

8. More or Less

This podcast covers the topic of statistics through noticeably short episodes, usually 8 minutes or less each. You’ll get episodes that cover everything you could ever want to know about statistics and how they work.   

For example, you can find out how many swimming pools of vaccines would be needed to give everyone a dose, see the one in two cancers claim debunked, and how data science has doubled life expectancy. 

9. Data Engineering Podcast

This show is for anyone who’s a data engineer or is hoping to become one in the future. You’ll find lots of useful info in the podcast, including the techniques they use, and the difficulties they face. 

Ensure you listen to this show if you want to learn more about your role, as you’ll pick up a lot of helpful tips. 

10. Data viz Today

This show doesn’t need a lot of commitment from you, as they release 30 min episodes monthly. The podcast covers data visualization, and how this helps to tell a story and get the most out of data no matter what industry you work in. 

Share with us exciting Data Science podcasts

These are all great podcasts that you can check out to learn more about data science. If you want to know more, you can check out Data Science Dojo’s informative sessions on YouTube. If we missed any of your favorite podcasts, do share them with us in the comments!

These interviews cover the entire range of data as an industry, looking at its past, present, and future. The guests are from both the industry and academia sides of the data spectrum too, says Graham Pierson, a tech writer at Academized.

In this blog, we have gathered the top 10 machine learning books. Learning this subject is a challenge for beginners. Take your learning experience one step ahead with these top-rated ML books on Amazon. 

1. Machine Learning: 4 Books in 1

Machine Learning: 4 Books in 1 is a complete guide for beginners to master the basics of Python programming and understand how to
build artificial intelligence through data science. This book includes four books: Introduction to Machine Learning, Python Programming for
Beginners, Data Science for Beginners, and Artificial Intelligence for Beginners. It covers everything you need to know about machine learning, including supervised and unsupervised learning, regression and classification, feature engineering, model selection, and more. Muhammad Junaid – Marketing manager, BTIP

With clear explanations and practical examples, this book will help you quickly learn the essentials of machine learning and start building your own AI applications.

2. Mathematics for Machine Learning

Mathematics for Machine Learning is a tool that helps you understand the mathematical foundations of machine learning, so that you
can build better models and algorithms. It covers topics such as linear algebra, probability, optimization, and statistics. With this book, you
will be able to learn the mathematics needed to develop machine learning models and algorithms. Daniel – Founder, Gadget FAQs

This book is excellent for brushing up your mathematics knowledge required for ML. It is very concise while still providing enough details to help readers determine important parts. This is the go-to if you need to review some concepts or brush up on my knowledge in general.

This book is not recommended if you have absolutely no prior math experience though as it can be hard to digest and sometimes, they would skip parts here and there in proofs and examples. Especially for the probability section, the concepts will be very hard to grasp without prior knowledge

3. Linear Algebra and Optimization for Machine Learning

This textbook provides a comprehensive introduction to linear algebra and optimization, two fundamental topics in machine learning. It
covers both theory and applications and is suitable for students with little or no background in mathematics. Allan McNabb, VP – Image Building Media

The book begins with a review of basic linear algebra, before moving on to more advanced topics such as matrix decompositions, eigenvalues and eigenvectors, singular value decomposition, and least squares methods. Optimization techniques are then introduced, including gradient descent, Newton’s Method, conjugate gradient methods, and interior point methods.

4. The Hundred-Page Machine Learning Book

If we have to teach machine learning to someone in juts few weeks, it is a lot better not to bother starting from scratch, instead hand over this book to the learners, because no doubt Andriy Burkov does a better job than we could do to quickly teach this vast subject in a limited time.

The book has a litany of rave reviews from some of the biggest names in tech, with scores more five-star reviews to boot, and you can see why. Burkov keeps his lessons concise and as easy to understand as possible given the subject matter, but still drills down into the details where necessary. Overall, the book excels at linking together complicated and sometimes seemingly unrelated concepts into a coherent whole. Peter, CEO and founder – Lantech

The book is very well organized, giving the reader an introduction and discussion on the mathematical notation used, a well written chapter that discusses several quite common algorithms, talks about best practices (like feature engineering, breaking up the data into multiple sets, and tuning the model’s hyperparameters), digs deeper into supervised learning, discusses unsupervised learning, and gives you a taste of a variety of other related topics.

This is a well-rounded book, far more so than most books I’ve read on machine learning or artificial intelligence. After reading through this, you will feel like you can competently discuss the subject, read one of the simpler machine learning research papers, and not be totally lost on the mathematics involved. The language used is concise and reads very well, showing very tight editing

5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

It’s good for new programmers without over-simplifying. I’d recommend it for really getting into practice exercises. It’s a book you need to take your time with, but you’ll learn a lot from it. One thing observed by the learners of this book as a con is that the quality of the print varies, but the quality of its content makes it more than worth it. Chris Martinez – Founder of Idiomatic

6. Machine Learning for Absolute Beginners by Oliver Theobald

Machine Learning is easy only when you have the right teacher and an appropriate reference book. Most of us fail to understand the importance of simple concepts that help us understand complex ones. Therefore, I recommend using Oliver Theobald’s *Machine Learning for Absolute Beginners *as the base reference book. Layla Acharya – Owner at Edwize

This book uses simple language to explain to the reader and teaches Machine learning from the scratch. Although non-technical people will find this book more relatable, people wanting to make a career in the machine learning field can benefit equally. It also has good references that can help a person who wants to learn like an expert.

7. Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain Gugger

This book is very well-rated and it’s helped me a lot in understanding the basics of deep learning.

The main reason readers suggest this book is because it’s very accessible and easy to follow. As the authors themselves say, you don’t need a PhD to understand and use the concepts in the book, and it follows a top-down approach (starting with the applications and working backwards to the theory). So, you’ll first have fun with building cool applications and then gradually learn the underlying theory as you go. Ed Shway – Owner & Writer at ByteXD.com

Fast AI have kept updating their courses and library, so you might want to check out their website (https://www.fast.ai/) for the latest and greatest Just this July they released a latest version of the course that the book is associated with (https://course.fast.ai/).

Furthermore, the book also comes in a free online version https://github.com/fastai/fastbook. Since the *Fast AI team put all this effort and made every resource available for free, you can be sure they’re in it for the love of the game and to help the community*, rather than to make a quick buck. So, this book is definitely worth your time.

The first practical applications it teaches you is in computer vision – you’ll build an image classifier, which you can use to tell apart different
kinds of images. For example, you can use it to distinguish between different kinds of animals. It will be very easy to follow along and build
this classifier yourself.

8. Bayesian Reasoning and Machine Learning by David Barber

It’s a real must-have for beginners interested in deepening their knowledge of machine learning in an engaging way. The book covers topics such as dynamic and probabilistic models, approximate interference, graphical models, Naive Bayes algorithms, and more. What makes it worth checking out is the fact that the book is full of examples and exercises, which makes it a hands-on guide full of useful practice rather than dry theoretical frameworks. Marcin Gwizdala – Chief Technical Officer – Tidio

For relative beginners, Bayesian techniques began in the 1700s to model how a degree of belief should be modified to account for new evidence. The techniques and formulas were largely discounted and ignored until the modern era of computing, pattern recognition and AI, now machine learning.

The formula answers how the probabilities of two events are related when represented inversely, and more broadly, gives a precise mathematical model for the inference process itself (under uncertainty), where deductive reasoning and logic becomes a subset (under certainty, or when values can resolve to 0/1 or true/false, yes/no etc. In “odds” terms (useful in many fields including optimal expected utility functions in decision theory), posterior odds = prior odds * the Bayes Factor.

9. Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools by Eli Stevens, Luca Antiga, Thomas Viehmann

This book provides a good and fairly complete description of the basic principles and abstractions of one of the most popular frameworks for
Machine Learning – PyTorch.

It’s great that this book is written by the creator and key contributors of PyTorch, unlike many books that claim to be a definitive treatise, it is not overloaded with non-essential details, the emphasis is on making the book practical. The book gives a reader a deep understanding of the framework and methods for building and training models on it (with advanced best practices) describing what is under the hood. Vitalii Kudelia, TUTU – Machine Learning Scientist

There is an example of solving a real-world problem in this book, it analyzes the problem of searching for malignant tumors on a computer
diagram with an analysis of approaches, possible errors, options for improvements, and provides code examples.

It also includes options for translating the model into production, using the models in other programming languages, and on mobile devices.
As a result, the book is highly useful for understanding and mastering the framework. Mastering PyTorch helps not only in computer vision, but also in other areas of deep learning, such as, for example, natural language processing.

10. Introduction to Machine Learning by Ethem Alpaydin

This comprehensive text covers everything from the basics of linear algebra to more advanced topics like support vector machines. In addition to being an excellent resource for students, Alpaydin’s book is also very accessible for practitioners who want to learn more about this exciting field. Rajesh Namase – Co-Founder and Tech Blogger

For learners, this is the best book for machine learning for a number of reasons. First, the book provides a clear and concise introduction to the basics of machine learning. Second, it covers a wide range of topics in machine learning, including supervised and unsupervised learning, feature selection, and model selection.

Third, the book is well-written and easy to understand. Finally, the book includes exercises and solutions at the end of each
chapter, which is extremely helpful for readers who want to learn more about machine learning.

Share more machine learning books with us 

If you have read any other interesting machine learning books, share with us in the comments below and let us help the learners to begin with computer vision. 

What can be a better way to spend your days listening to interesting bits about trending AI and Machine learning topics? Here’s a list of the 10 best AI and ML podcasts.

1. The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Artificial intelligence and machine learning are fundamentally altering how organizations run and how individuals live. It is important to discuss the latest innovations in these fields to gain the most benefit from technology. The TWIML AI Podcast outreaches a large and significant audience of ML/AI academics, data scientists, engineers, tech-savvy business, and IT (Information Technology) leaders, as well as the best minds and gather the best concepts from the area of ML and AI.  

The podcast is hosted by a renowned industry analyst, speaker, commentator, and thought leader Sam Charrington. Artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science, and other technologies are discussed. 

2. The AI Podcast

One individual, one interview, one account. This podcast examines the effects of AI on our world. The AI podcast creates a real-time oral history of AI that has amassed 3.4 million listens and has been hailed as one of the best AI and machine learning podcasts. They always bring you a new story and a new 25-minute interview every two weeks. Consequently, regardless of the difficulties, you are facing in marketing, mathematics, astrophysics, paleo history, or simply trying to discover an automated way to sort out your kid’s growing Lego pile, listen in and get inspired. 

3. Data Skeptic

Data Skeptic launched as a podcast in 2014. Hundreds of interviews and tens of millions of downloads later, we are a widely recognized authoritative source on data science, artificial intelligence, machine learning, and related topics. 

Data Skeptic runs in seasons. By speaking with active scholars and business leaders who are somehow involved in our season’s subject, we probe it. 

We carefully choose each of our visitors using a system internally. Since we do not cooperate with PR firms, we are unable to reply to the daily stream of unsolicited submissions. Publishing quality research to the arxiv is the greatest approach to getting on the show. It is crawled. We will locate you. 

Data Skeptic is a boutique consulting company in addition to its podcast. Kyle participates directly in each project our team undertakes. Our work primarily focuses on end-to-end machine learning, cloud infrastructure, and algorithmic design. 

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches. 

Pro-tip: Enroll in the data science boot camp today to learn the basics of the industry

4. Podcast.ai 

Podcast.ai is entirely generated by artificial intelligence. Every week, they explore a new topic in-depth, and listeners can suggest topics or even guests and hosts for future episodes. Whether you are a machine learning enthusiast, just want to hear your favorite topics covered in a new way or even just want to listen to voices from the past brought back to life, this is the podcast for you.

The podcast aims to put incremental advances into a broader context and consider the global implications of developing technology. AI is about to change your world, so pay attention. 

5. The Talking Machines

Talking machines is a podcast hosted by Katherine Gorman and Neil Lawrence. The objective of this show is to bring you clear conversations with experts in the field of machine learning, insightful discussions of industry news, and useful answers to your questions. Machine learning is changing the questions we can ask of the world around us, here we explore how to ask the best questions and what to do with the answers. 

6. Linear Digressions

If you are interested in learning about unusual applications of machine learning and data science. In each episode of linear digressions, your hosts explore machine learning and data science through interesting apps. Ben Jaffe and Katie Malone host the show, they assure themselves to produce the most exciting additions in the industry such as AI-driven medical assistants, open policing data, causal trees, the grammar of graphics and a lot more.  

7. Practical AI: Machine Learning, Data Science

Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, businesspeople, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs (Generative adversarial networks), MLOps (machine learning operations) (machine learning operations), AIOps, and more).

The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you! 

8. Data Stories

Enrico Bertini and Moritz Stefaner discuss the latest developments in data analytics, visualization, and related topics. The data stories podcast consists of regular new episodes on a range of discussion topics related to data visualization. It shares the importance of data stories in different fields including statistics, finance, medicine, computer science, and a lot more to name. The podcast’s hosts Enrico and Moritz invite industry leaders, experienced professionals, and instructors in data visualization to share the stories and the importance of representation of data visuals into appealing charts and graphs. 

9. The Artificial Intelligence Podcast

The Artificial intelligence podcast is hosted by Dr. Tony Hoang. This podcast talks about the latest innovations in the artificial intelligence and machine learning industry. The recent episode of the podcast discusses text-to-image generator, Robot dog, soft robotics, voice bot options, and a lot more.  

10. Learning Machines 101

Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. The objective of this podcast series is to inform students and instructors about the advanced technologies introduced by AI and the following: 

•  How do these devices work? 
• Where do they come from? 
• How can we make them even smarter? 
• And how can we make them even more human-like? 

Have we missed any of your favorite podcasts?

 Do not forget to share in comments the names of your most favorite AI and ML podcasts. Read this amazing blog if you want to know about Data Science podcasts.

In this blog, we have gathered the top 7 computer vision books. Learning this subject is a challenge for beginners. Take your learning experience one step ahead with these seven computer vision books. Explore a range of topics, from Computer vision to Python. 

1. Learning openCV 4 computer vision with Python 3 book by Joe Minichino and Joseph Howse: 

This book will teach you how to create a computer vision system using Python. You will learn how to use the OpenCV library, which is a cross-platform library that has been used in many research and commercial projects. Joe and Joseph in this book introduces computer vision and OpenCV with Python programming language. 

Both novices and seasoned pros alike will find something of use in this book’s extensive coverage of the subject of CV. It explains how to use Open CV 4 and Python 3 across several platforms to execute tasks like image processing and video analysis and comprehension. Machine learning algorithms and their many uses will be covered in this course. With these ideas in hand, you may design your image and video object detectors!  ~ Adam Crossling, Marketing manager at Zenzero 

2. Multiple view geometry in computer vision book by Richard Hartley: 

This book discusses the use of geometry and algebra in image reconstruction, with applications to computer vision. In this book, Richard discusses the geometry of images and how they are processed in this area. The book covers topics such as image formation, camera models, image geometry, and shape from shading. 

The main goal of this book is to provide a comprehensive introduction to computer vision by focusing on the geometric aspects of images. This article describes a wide variety of tactics, from traditional to innovative, to make it very evident when particular approaches are being employed.  

Camera projection matrices, basic matrices (which project an image into 2D), and the trifocal tensor are all introduced, along with their algebraic representations, in this book. It explains how to create a 3D model using a series of photographs taken at various times or in different sequences.  

3. Principles, algorithms, applications, learning book by E. R. Davies: 

New developments in technology have given rise to an exciting academic discipline: computer vision. The goal of this field is to understand information about objects and their environment by creating a mathematical model from digital images or videos, which can be used to extract meaningful data for analysis or classification purposes.  

This book teaches its readers not just the basics of the subject but also how it may be put to use and gives real-world scenarios in which it might be of benefit.  

4. Deep learning for vision systems by Mohamed Elgendy: 

This book should be the go-to text for anyone looking to learn about how machine learning works in AI (Artificial Intelligence) and, fundamentally, how the computer sees the world. By using only the simplest algebra a high school student would be able to understand, they can demonstrate some overly complicated topics within the AI engineering world.  

Learn about deep learning using Python

Hands-on deep learning using Python in Cloud

Through illustrations as well as Elgendy’s expertise, the book is the most accurate yet simplest way to understand computer vision for the modern day. ~ Founder & CEO of Lantech 

5. Digital image processing by Rafael C. GONZALES and Richard E. Woods: 

Image processing is one of the topics that form the core of Computer Vision and DIP by Gonzalez is one of the leading books on the topic. It provides the user with a detailed explanation of not just the basics like feature extraction and image morphing but also more advanced concepts like wavelets and superpixels. It is good for both beginners and people who need to refresh their basics.

It also comes with MATLAB exercises to help the reader understand the concepts practically. Senior Machine Learning Developer, AltaML  Rafael C. GONZALES and Richard E. Woods wrote this book to provide an introduction to digital image processing for undergraduate students and professionals who are interested in this field.

The book covers the fundamentals of image formation, sampling and quantization, the design of analog-to-digital converters, image enhancement techniques such as filtering and edge detection, image compression techniques such as JPEG and MPEG, digital watermarking techniques for copyright protection purposes and more advanced topics like fractal analysis in texture synthesis. 

6. Practical machine learning for computer vision: End-to-end machine by Martin Görner, Ryan Gillard, and Valliappa Lakshmanan: 

Learning for Images. This tutorial shows how to extract information from images using machine learning models. ML (Machine Learning) engineers and data scientists will learn how to use proven ML techniques such as classification, object detection, autoencoders, image generation, counting, and captioning to solve a variety of image problems.  

You will find all aspects of deep learning from start to finish, including dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Valliappa Lakshmanan, Martin Görner, and Ryan Gillard of Google show how to use robust ML architecture to develop accurate and explainable computer vision ML models and put them into large-scale production in a flexible and maintainable manner. You will learn how to use TensorFlow or Keras to design, train, evaluate, and predict models. Senior IT Director at Propnex 

Further, this book provides a great introduction to deep end-to-end learning for computer vision, including how to design, train, and deploy models. You will learn how to select appropriate models for various tasks, preprocess images for better learnability, and incorporate responsible AI best practices. The book also covers how to monitor and manage image models after deployment. You will also learn how to put your models into large-scale production using robust ML architecture. The authors are Google engineers with extensive experience in the field, so you can be confident you are learning from the best. – Will Cannon, CEO, and Founder of Uplead   

7. Computer vision by Richard Szeliski:  

This book is all about algorithms and applications. This book is perfect for undergraduate students in computer science as it aims to provide a comprehensive course in computer vision. It is also known as the bible of computer vision. The focus of this book is on the algorithm, application, and techniques for image processing and recognition in CV.

It also helps one to get an understanding of the real-based applications and further discuss the implementation and practical challenges of techniques in computer vision. Co-Founder at Twiz LLC 

If you are interested in teaching senior-level courses in this subject, then this book is for you as it can help you to learn more techniques and enhance your knowledge about computer vision. 

Share more computer vision books with us 

If you have read any other interesting computer vision book, share with us in the comments below and let us help the learners to begin with computer vision. 

Are you interested in learning more about IoT? Do you want to network with people working in IoT? Here is a list of 10 IoT conferences and events that can help you learn more about the new research and developments, and help you network and meet recruiters or project owners.

The ongoing development of the Internet of Things (IoT) is a major driver of digital transformation and cutting-edge latest innovations.

Data processing, data visualization, and many other techniques are just a few of the innovative technologies that may be combined to create new possibilities and solutions that demand improved integration and collaboration. 

1. 4th Asia IoT Technologies Conference– Beijing, China

Scheduled to be held in Beijing, China, from 6th to 8th January 2023, the Asia IoT Technologies Conference, sponsored by Beijing Huaxia Rongzhi Institute of Blockchain (BJIB), co-sponsored by Beijing University of Technology (China) and the Faculty of Information Technology (BJUT, China). 

The conference will focus on core technologies and IoT solutions and IoT applications to promote the integration of IoT and the economy for industrial and economic purposes. 

The conference features a broad range of programs and talks on the latest developments in the field. The main aim of the conference is to deepen the understanding of the masses and take the necessary actions to accelerate the adoption of IoT with an emphasis on diverse topics across the IoT landscape. 

More details regarding the conference can be found here. 

2. International Conference on Innovations in Data Analytics ICIDA – Kolkata, India 

Taking place on November 29-30, 2022, the International Conference on Innovations in Data Analytics ICIDA will be organized by International Knowledge Research Foundation in collaboration with Eminent College of Management and Technology (ECMT), West Bengal, India. 

The main aim of the conference is to bring together innovators, academics, and business specialists in the fields of Computing and Communication at one place. The conference also aims to inspire young scholars to learn newly created avenues of research at an international academic forum. 

More details regarding the conference can be found here. 

3. Asia IoT Business Platform – Southeast Asia 

Taking place in different cities in Southeast Asia from October to December, the Asia IoT Business Platform aims to serve public and private organizations to enable their access and exchange of knowledge on development and innovation in the B2B sector. 

The conferences help create partnerships within the tech and IoT sectors and help provide better collaborations between public and private organizations. 

The AIBP conferences and exhibitions also promote market research and access gained via the creation and implementation of business growth strategies. 

You can learn more about the conferences here. 

4. IoT India Expo – India 

The IoT India Expo will be held from the 27th to the 29th of March 2023 and will feature numerous companies working in IT, enabling them to enter a new market more quickly and with more accurate data through the adoption of modern technologies. 

For anyone in the IT sector, the event is a good place to network and talk about the future of technology because it is the premier enterprise event for IoT, Blockchain, AI, Big Data, Cyber Security, and Cloud. 

More details regarding the expo can be found here. 

5. Cloud Expo Asia – Marina Bay Sands, Singapore 

One of the leading IoT events in Asia, Cloud Expo Asia Is expected to be held from 12th to 13th October 2022. 

The main aim of the event is to connect people from academia and professionals with experts in the field to find sustainable solutions and services that can help accelerate digital transformation. 

With multiple conferences, shows, speaker sessions, and much more lined up, the event focuses on a large variety of topics. 

More details can be found here. 

6. IEEE 8th World Forum on Internet of Things (WF-IoT) – Yokohama, Japan 

One of the events organized by the Multi-Society IEEE IoT Initiative WF-IOT will take place from the 26th of October till the 11th of November. 

The conference highlights the latest developments in IoT, business, and private and public sectors. 

The main aim of the forum is to promote the development and promotion of IoT for society’s and humanity’s benefit, as well as to promote the ethical and responsible use of IoT applications and solutions to improve human lives. 

The theme of the event this year is ‘Sustainability and the Internet of Things.’ 

More details can be found here.

7. Internet of Things World, Asia – Marina Bay Sands, Singapore 

From edge computing to digital transformations, the Internet of Things World covers all aspects of IoT. The event is expected to be held tentatively in October. 

The main aim of the event is always to teach professionals, industrialists, and academics, the importance of monetizing IoT and how it can effectively impact business models. 

More details can be found here.

8. EAI International Conference on Industrial Networks and Intelligent Systems – Vietnam 

The International Conference on Industrial Networks and Intelligent Systems focuses on the current state of AI and 6G Convergence in Models, Technologies, and Applications related to IoT. 

The conference also highlights any issues pertaining to IoT Networks for Smart Cities, Next Generation Networks Infrastructures, and Optical Spectrum–LiFi. 

Further details can be found here.

9. International Conference on Internet of Medical Things (ICIMT) – UAE 

The International Conference on the Internet of Medical Things (ICIMT) focuses on exchanging experiences and research findings within the Internet of Medical Things. 

The conference gives researchers, practitioners, and educators a world-class interdisciplinary forum on which to present and debate the most recent advancements, concerns, and trends on the Internet of Medical Things. 

More details can be found here.

10. IEEE International Conference on IoT and Blockchain Technology – India 

The conference that led to revolutionizing the IoT industry, reevaluating business structures, and IoT, covers interoperability, data, and service mashups. 

Moreover, the development of open platforms and standardization across technological levels is also focused on throughout the conference. 

More details regarding the conference can be found here. 

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