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deep learning

Data Science Dojo
Ayesha Saleem
| January 19

Neural networks, a cornerstone of modern artificial intelligence, mimic the human brain’s ability to learn from and interpret data. Let’s break down this fascinating concept into digestible pieces, using real-world examples and simple language.

What is a neural network?

Imagine a neural network as a mini-brain in your computer. It’s a collection of algorithms designed to recognize patterns, much like how our brain identifies patterns and learns from experiences. For instance, when you show numerous pictures of cats and dogs, it learns to distinguish between the two over time, just like a child learning to differentiate animals.

The structure of neural networks

Think of a it as a layered cake. Each layer consists of nodes, similar to neurons in the brain. These layers are interconnected, with each layer responsible for a specific task. For example, in facial recognition software, one layer might focus on identifying edges, another on recognizing shapes, and so on, until the final layer determines the face’s identity.

How do neural networks learn?

Learning happens through a process called training. Here, the network adjusts its internal settings based on the data it receives. Consider a weather prediction model: by feeding it historical weather data, it learns to predict future weather patterns.

Backpropagation and gradient descent

These are two key mechanisms in learning. Backpropagation is like a feedback system – it helps the network learn from its mistakes. Gradient descent, on the other hand, is a strategy to find the best way to improve learning. It’s akin to finding the lowest point in a valley – the point where the network’s predictions are most accurate.

Practical application: Recognizing hand-written digits

A classic example is teaching a neural network to recognize handwritten numbers. By showing it thousands of handwritten digits, it learns the unique features of each number and can eventually identify them with high accuracy.


Learn more about hands on deep learning using Python in cloud

Architecture of neural networks

Neural networks work by mimicking the structure and function of the human brain, using a system of interconnected nodes or “neurons” to process and interpret data. Here’s a breakdown of their architecture:


Large language model bootcamp


Basic structure: A typical neural network consists of an input layer, one or more hidden layers, and an output layer.

    • Input layer: This is where the network receives its input data.
    • Hidden layers: These layers, located between the input and output layers, perform most of the computational work. Each layer consists of neurons that apply specific transformations to the data.
    • Output layer: This layer produces the final output of the network.

Neurons: The fundamental units of a neural network, neurons in each layer are interconnected and transmit signals to each other. Each neuron typically applies a mathematical function to its input, which determines its activation or output.

Weights and biases: Connections between neurons have associated weights and biases, which are adjusted during the training process to optimize the network’s performance.

Activation functions: These functions determine whether a neuron should be activated or not, based on the weighted sum of its inputs. Common activation functions include sigmoid, tanh, and ReLU (Rectified Linear Unit).

Learning process: Neural networks learn through a process called backpropagation, where the network adjusts its weights and biases based on the error of its output compared to the expected result. This process is often coupled with an optimization algorithm like gradient descent, which minimizes the error or loss function.

Types of neural networks: There are various types of neural network architectures, each suited for different tasks. For example, Convolutional Neural Networks (CNNs) are used for image processing, while Recurrent Neural Networks (RNNs) are effective for sequential data like speech or text.



Applications of neural networks

They have a wide range of applications in various fields, revolutionizing how tasks are performed and decisions are made. Here are some key real-world applications:

  1. Facial recognition: Neural networks are used in facial recognition technologies, which are prevalent in security systems, smartphone unlocking, and social media for tagging photos.
  2. Stock market prediction: They are employed in predicting stock market trends by analyzing historical data and identifying patterns that might indicate future market behavior.
  3. Social media: Neural networks analyze user data on social media platforms for personalized content delivery, targeted advertising, and understanding user behavior.
  4. Aerospace: In aerospace, they are used for flight path optimization, predictive maintenance of aircraft, and simulation of aerodynamic properties.
  5. Defense: They play a crucial role in defense systems for surveillance, autonomous weapons systems, and threat detection.
  6. Healthcare: They assist in medical diagnosis, drug discovery, and personalized medicine by analyzing complex medical data.
  7. Computer vision: They are fundamental in computer vision for tasks like image classification, object detection, and scene understanding.
  8. Speech recognition: Used in voice-activated assistants, transcription services, and language translation applications.
  9. Natural language processing (NLP): Neural networks are key in understanding, interpreting, and generating human language in applications like chatbots and text analysis.

These applications demonstrate the versatility and power of neural networks in handling complex tasks across various domains.


In summary, neural networks process input data through a series of layers and neurons, using weights, biases, and activation functions to learn and make predictions or classifications. Their architecture can vary greatly depending on the specific application.

They are a powerful tool in AI, capable of learning and adapting in ways similar to the human brain. From voice assistants to medical diagnosis, they are reshaping how we interact with technology, making our world smarter and more connected.

Izma Aziz
Izma Aziz
| September 13


The evolution of the GPT Series culminates in ChatGPT, delivering more intuitive and contextually aware conversations than ever before.


What are chatbots?  

AI chatbots are smart computer programs that can process and understand users’ requests and queries in voice and text. It mimics and generates responses in a human conversational manner. AI chatbots are widely used today from personal assistance to customer service and much more. They are assisting humans in every field making the work more productive and creative. 

Deep learning And NLP

Deep Learning and Natural Language Processing (NLP) are like best friends in the world of computers and language. Deep Learning is when computers use their brains, called neural networks, to learn lots of things from a ton of information.

NLP is all about teaching computers to understand and talk like humans. When Deep Learning and NLP work together, computers can understand what we say, translate languages, make chatbots, and even write sentences that sound like a person. This teamwork between Deep Learning and NLP helps computers and people talk to each other better in the most efficient manner.  

Chatbots and ChatGPT
Chatbots and ChatGPT

How are chatbots built? 

Building Chatbots involves creating AI systems that employ deep learning techniques and natural language processing to simulate natural conversational behavior.

The machine learning models are trained on huge datasets to figure out and process the context and semantics of human language and produce relevant results accordingly. Through deep learning and NLP, the machine can recognize the patterns from text and generate useful responses. 

Transformers in chatbots 

Transformers are advanced models used in AI for understanding and generating language. This efficient neural network architecture was developed by Google in 2015. They consist of two parts: the encoder, which understands input text, and the decoder, which generates responses.

The encoder pays attention to words’ relationships, while the decoder uses this information to produce a coherent text. These models greatly enhance chatbots by allowing them to understand user messages (encoding) and create fitting replies (decoding).

With Transformers, chatbots engage in more contextually relevant and natural conversations, improving user interactions. This is achieved by efficiently tracking conversation history and generating meaningful responses, making chatbots more effective and lifelike. 


Large language model bootcamp

GPT Series – Generative pre trained transformer 

 GPT is a large language model (LLM) which uses the architecture of Transformers. I was developed by OpenAI in 2018. GPT is pre-trained on a huge amount of text dataset. This means it learns patterns, grammar, and even some reasoning abilities from this data. Once trained, it can then be “fine-tuned” on specific tasks, like generating text, answering questions, or translating languages.

This process of fine-tuning comes under the concept of transfer learning. The “generative” part means it can create new content, like writing paragraphs or stories, based on the patterns it learned during training. GPT has become widely used because of its ability to generate coherent and contextually relevant text, making it a valuable tool in a variety of applications such as content creation, chatbots, and more.  

The advent of ChatGPT: 

ChatGPT is a chatbot designed by OpenAI. It uses the “Generative Pre-Trained Transformer” (GPT) series to chat with the user analogously as people talk to each other. This chatbot quickly went viral because of its unique capability to learn complications of natural language and interactions and give responses accordingly.

ChatGPT is a powerful chatbot capable of producing relevant answers to questions, text summarization, drafting creative essays and stories, giving coded solutions, providing personal recommendations, and many other things. It attracted millions of users in a noticeably short period. 

ChatGPT’s story is a journey of growth, starting with earlier versions in the GPT series. In this blog, we will explore how each version from the series of GPT has added something special to the way computers understand and use language and how GPT-3 serves as the foundation for ChatGPT’s innovative conversational abilities. 

Chat GPT Series evolution
Chat GPT Series evolution


GPT-1 was the first model of the GPT series developed by OpenAI. This innovative model demonstrated the concept that text can be generated using transformer design. GPT-1 introduced the concept of generative pre-training, where the model is first trained on a broad range of text data to develop a comprehensive understanding of language. It consisted of 117 million parameters and produced much more coherent results as compared to other models of its time. It was the foundation of the GPT series, and it paved a path for advancement and revolution in the domain of text generation. 


GPT-2 was much bigger as compared to GPT-1 trained on 1.5 billion parameters. It makes the model have a stronger grasp of the context and semantics of real-world language as compared to GPT-1. It introduces the concept of “Task conditioning.” This enables GTP-2 to learn multiple tasks within a single unsupervised model by conditioning its outputs on both input and task information.

GPT-2 highlighted zero-shot learning by carrying out tasks without prior examples, solely guided by task instructions. Moreover, it achieved remarkable zero-shot task transfer, demonstrating its capacity to seamlessly comprehend and execute tasks with minimal or no specific examples, highlighting its adaptability and versatile problem-solving capabilities. 

As the ChatGPT model was getting more advanced it started to have new qualities of writing long creative essays, answering complex questions instead of just predicting the next word. So, it was becoming more human-like and attracted many users for their day-to-day tasks. 


GPT-3 was trained on an even larger dataset and has 175 billion parameters. It gives a more natural-looking response making the model conversational. It was better at common sense reasoning than the earlier models. GTP-3 can not only generate human-like text but is also capable of generating programming code snippets providing more innovative solutions. 

GPT-3’s enhanced capacity, compared to GPT-2, extends its zero-shot and few-shot learning capabilities. It can give relevant and accurate solutions to uncommon problems, requiring training on minimal examples or even performing without prior training.  

Instruct GPT: 

An improved version of GPT-3 also known as InstructGPT(GPT-3.5) produces results that align with human expectations. It uses a “Human Feedback Model” to make the neural network respond in a way that is according to real-world expectations.

It begins by creating a supervised policy via demonstrations on input prompts. Comparison data is then collected to build a reward model based on human-preferred model outputs. This reward model guides the fine-tuning of the policy using Proximal Policy Optimization.

Iteratively, the process refines the policy by continuously collecting comparison data, training an updated reward model, and enhancing the policy’s performance. This iterative approach ensures that the model progressively adapts to preferences and optimizes its outputs to align with human expectations. The figure below gives a clearer depiction of the process discussed. 

Training language models
From Research paper ‘Training language models to follow instructions with human feedback’

GPT-3.5 stands as the default model for ChatGPT, while the GPT-3.5-Turbo Model empowers users to construct their own custom chatbots with similar abilities as ChatGPT. It is worth noting that large language models like ChatGPT occasionally generate responses that are inaccurate, impolite, or not helpful.

This is often due to their training in predicting subsequent words in sentences without always grasping the context. To remedy this, InstructGPT was devised to steer model responses toward better alignment with user preferences.


Read more –> FraudGPT: Evolution of ChatGPT into an AI weapon for cybercriminals in 2023


GPT-4 and beyond: 

After GTP-3.5 comes GPT-4. According to some resources, GPT-4 is estimated to have 1.7 trillion parameters. These enormous number of parameters make the model more efficient and make it able to process up to 25000 words at once.

This means that GPT-4 can understand texts that are more complex and realistic. The model has multimodal capabilities which means it can process both images and text. It can not only interpret the images and label them but can also understand the context of images and give relevant suggestions and conclusions. The GPT-4 model is available in ChatGPT Plus, a premium version of ChatGPT. 

So, after going through the developments that are currently done by OpenAI, we can expect that OpenAI will be making more improvements in the models in the coming years. Enabling it to handle voice commands, make changes to web apps according to user instruction, and aid people in the most efficient way that has never been done before. 

Watch: ChatGPT Unleashed: Live Demo and Best Practices for NLP Applications 


This live presentation from Data Science Dojo gives more understanding of ChatGPT and its use cases. It demonstrates smart prompting techniques for ChatGPT to get the desired responses and ChatGPT’s ability to assist with tasks like data labeling and generating data for NLP models and applications. Additionally, the demo acknowledges the limitations of ChatGPT and explores potential strategies to overcome them.  

Wrapping up: 

ChatGPT developed by OpenAI is a powerful chatbot. It uses the GPT series as its neural network, which is improving quickly. From generating one-liner responses to generating multiple paragraphs with relevant information, and summarizing long detailed reports, the model is capable of interpreting and understanding visual inputs and generating responses that align with human expectations.

With more advancement, the GPT series is getting more grip on the structure and semantics of the human language. It not only relies on its training information but can also use real-time data given by the user to generate results. In the future, we expect to see more breakthrough advancements by OpenAI in this domain empowering this chatbot to assist us in the most effective manner like ever before. 


Learn to build LLM applications                                          

Ruhma Khawaja author
Ruhma Khawaja
| September 8

Artificial Intelligence (AI) and Predictive Analytics are revolutionizing the way engineers approach their work. This article explores the fascinating applications of AI and Predictive Analytics in the field of engineering. We’ll dive into the core concepts of AI, with a special focus on Machine Learning and Deep Learning, highlighting their essential distinctions.

By the end of this journey, you’ll have a clear understanding of how Deep Learning utilizes historical data to make precise forecasts, ultimately saving valuable time and resources.

Predictive analytics and AI
Predictive analytics and AI

Different Approaches to Analytics

In the realm of analytics, there are diverse strategies: descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics involves summarizing historical data to extract insights into past events. Diagnostic analytics goes further, aiming to uncover the root causes behind these events. In engineering, predictive analytics takes center stage, allowing professionals to forecast future outcomes, greatly assisting in product design and maintenance. Lastly, prescriptive analytics recommends actions to optimize results.


Large language model bootcamp

AI: Empowering Engineers

Artificial Intelligence isn’t about replacing engineers; it’s about empowering them. AI provides engineers with a powerful toolset to make more informed decisions and enhance their interactions with the digital world. It serves as a collaborative partner, amplifying human capabilities rather than supplanting them.

AI and Predictive Analytics: Bridging the Gap

AI and Predictive Analytics are two intertwined yet distinct fields. AI encompasses the creation of intelligent machines capable of autonomous decision-making, while Predictive Analytics relies on data, statistics, and machine learning to forecast future events accurately. Predictive Analytics thrives on historical patterns to predict forthcoming outcomes.


Read more –> Data Science vs AI – What is 2023 demand for?


Navigating Engineering with AI

Before AI’s advent, engineers employed predictive analytics tools grounded in their expertise and mathematical models. While these tools were effective, they demanded significant time and computational resources.

However, with the introduction of Deep Learning in 2018, predictive analytics in engineering underwent a transformative revolution. Deep Learning, an AI subset, quickly analyzes vast datasets, delivering results in seconds. It replaces complex algorithms with neural networks, streamlining and accelerating the predictive process.

The Role of Data Analysts

Data analysts play a pivotal role in predictive analytics. They are the ones who spot trends and construct models that predict future outcomes based on historical data. Their expertise in deciphering data patterns is indispensable in making accurate forecasts.

Machine Learning and Deep Learning: The Power Duo

Machine Learning (ML) and Deep Learning (DL) are two critical branches of AI that bring exceptional capabilities to predictive analytics. ML encompasses a range of algorithms that enable computers to learn from data without explicit programming. DL, on the other hand, focuses on training deep neural networks to process complex, unstructured data with remarkable precision.

Turbocharging Predictive Analytics with AI

The integration of AI into predictive analytics turbocharges the process, dramatically reducing processing time. This empowerment equips design teams with the ability to explore a wider range of variations, optimizing their products and processes.

In the domain of heat exchanger applications, AI, particularly the NCS AI model, showcases its prowess. It accurately predicts efficiency, temperature, and pressure drop, elevating the efficiency of heat exchanger design through generative design techniques.






Predictive Analytics


Artificial Intelligence

Definition Uses historical data to identify patterns and predict future outcomes. Uses machine learning to learn from data and make decisions without being explicitly programmed.
Goals To predict future events and trends. To automate tasks, improve decision-making, and create new products and services.
Techniques Uses statistical models, machine learning algorithms, and data mining. Uses deep learning, natural language processing, and computer vision.
Applications Customer behavior analysis, fraud detection, risk assessment, and inventory management. Self-driving cars, medical diagnosis, and product recommendations.
Advantages Can be used to make predictions about complex systems. Can learn from large amounts of data and make decisions that are more accurate than humans.
Disadvantages Can be biased by the data it is trained on. Can be expensive to develop and deploy.
Maturity Well-established and widely used. Still emerging, but growing rapidly.

Realizing the Potential: A Use Case

  1. Healthcare:
    • AI aids medical professionals by prioritizing and triaging patients based on real-time data.
    • It supports early disease diagnosis by analyzing medical history and statistical data.
    • Medical imaging powered by AI helps visualize the body for quicker and more accurate diagnoses.
  2. Customer Service:
    • AI-driven smart call routing minimizes wait times and ensures customers’ concerns are directed to the right agents.
    • Online chatbots, powered by AI, handle common customer inquiries efficiently.
    • Smart Analytics tools provide real-time insights for faster decision-making.
  3. Finance:
    • AI assists in fraud detection by monitoring financial behavior patterns and identifying anomalies.
    • Expense management systems use AI for categorizing expenses, aiding tracking and future projections.
    • Automated billing streamlines financial processes, saving time and ensuring accuracy.

Machine Learning (ML):


  1. Social Media Moderation:
    • ML algorithms help social media platforms flag and identify posts violating community standards, though manual review is often required.
  2. Email Automation:
    • Email providers employ ML to detect and filter spam, ensuring cleaner inboxes.
  3. Facial Recognition:
    • ML algorithms recognize facial patterns for tasks like device unlocking and photo tagging.

Predictive Analytics:


  1. Predictive Maintenance:
    • Predictive analytics anticipates equipment failures, allowing for proactive maintenance and cost savings.
  2. Risk Modeling:
    • It uses historical data to identify potential business risks, aiding in risk mitigation and informed decision-making.
  3. Next Best Action:
    • Predictive analytics analyzes customer behavior data to recommend the best ways to interact with customers, optimizing timing and channels.

Business Benefits:

The combination of AI, ML, and predictive analytics offers businesses the capability to:

  • Make informed decisions.
  • Streamline operations.
  • Improve customer service.
  • Prevent costly equipment breakdowns.
  • Mitigate risks.
  • Optimize customer interactions.
  • Enhance overall decision-making through clear analytics and future predictions.

These technologies empower businesses to navigate the complex landscape of data and derive actionable insights for growth and efficiency.

Enhancing Supply Chain Efficiency with Predictive Analytics and AI

The convergence of predictive analytics and AI holds the key to improving supply chain forecast accuracy, especially in the wake of the pandemic. Real-time data access is critical for every resource in today’s dynamic environment. Consider the example of the plastic supply chain, which can be disrupted by shortages of essential raw materials due to unforeseen events like natural disasters or shipping delays. AI systems can proactively identify potential disruptions, enabling more informed decision-making.

AI is poised to become a $309 billion industry by 2026, and 44% of executives have reported reduced operational costs through AI implementation. Let’s delve deeper into how AI can enhance predictive analytics within the supply chain:

1. Inventory Management:

Even prior to the pandemic, inventory mismanagement led to significant financial losses due to overstocking and understocking. The lack of real-time inventory visibility exacerbated these issues. When you combine real-time data with AI, you move beyond basic reordering.

Technologies like Internet of Things (IoT) devices in warehouses offer real-time alerts for low inventory levels, allowing for proactive restocking. Over time, AI-driven solutions can analyze data and recognize patterns, facilitating more efficient inventory planning.

To kickstart this process, a robust data collection strategy is essential. From basic barcode scanning to advanced warehouse automation technologies, capturing comprehensive data points is vital. When every barcode scan and related data is fed into an AI-powered analytics engine, you gain insights into inventory movement patterns, sales trends, and workforce optimization possibilities.

2. Delivery Optimization:

Predictive analytics has been employed to optimize trucking routes and ensure timely deliveries. However, unexpected events such as accidents, traffic congestion, or severe weather can disrupt supply chain operations. This is where analytics and AI shine.

By analyzing these unforeseen events, AI can provide insights for future preparedness and decision-making. Route optimization software, integrated with AI, enables real-time rerouting based on historical data. AI algorithms can predict optimal delivery times, potential delays, and other transportation factors.

IoT devices on trucks collect real-time sensor data, allowing for further optimization. They can detect cargo shifts, load imbalances, and abrupt stops, offering valuable insights to enhance operational efficiency.

Turning Data into Actionable Insights

The pandemic underscored the potency of predictive analytics combined with AI. Data collection is a cornerstone of supply chain management, but its true value lies in transforming it into predictive, actionable insights. To embark on this journey, a well-thought-out plan and organizational buy-in are essential for capturing data points and deploying the appropriate technology to fully leverage predictive analytics with AI.

Wrapping Up

AI and Predictive Analytics are ushering in a new era of engineering, where precision, efficiency, and informed decision-making reign supreme. Engineers no longer need extensive data science training to excel in their roles. These technologies empower them to navigate the complex world of product design and decision-making with confidence and agility. As the future unfolds, the possibilities for engineers are limitless, thanks to the dynamic duo of AI and Predictive Analytics.


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Ruhma Khawaja author
Ruhma Khawaja
| June 1

Machine learning courses are not just a buzzword anymore; they are reshaping the careers of many people who want their breakthrough in tech. From revolutionizing healthcare and finance to propelling us towards autonomous systems and intelligent robots, the transformative impact of machine learning knows no bounds.

Safe to say that the demand for skilled machine learning professionals is skyrocketing, and many are turning to online courses to upskill and stay competitive in the job market. Fortunately, there are many great resources available for those looking to dive into the world of machine learning.

If you are interested in learning more about machine learning courses, there are many free ones available online.

Machine learning courses
Machine learning courses

Top free machine learning courses

Here are 9 free machine learning courses from top universities that you can take online to upgrade your skills: 

1. Machine Learning with TensorFlow by Google AI

This is a beginner-level course that teaches you the basics of machine learning using TensorFlow, a popular machine-learning library. The course covers topics such as linear regression, logistic regression, and decision trees.

2. Machine Learning for Absolute Beginners by Kirill Eremenko and Hadelin de Ponteves

This is another beginner-level course that teaches you the basics of machine learning using Python. The course covers topics such as supervised learning, unsupervised learning, and reinforcement learning.

3. Machine Learning with Python by Andrew Ng

This is an intermediate-level course that teaches you more advanced machine-learning concepts using Python. The course covers topics such as deep learning and reinforcement learning.

4. Machine Learning for Data Science by Carlos Guestrin

This is an intermediate-level course that teaches you how to use machine learning for data science tasks. The course covers topics such as data wrangling, feature engineering, and model selection.

5. Machine Learning for Natural Language Processing by Christopher Manning, Jurafsky and Schütze

This is an advanced-level course that teaches you how to use machine learning for natural language processing tasks. The course covers topics such as text classification, sentiment analysis, and machine translation.

6. Machine Learning for Computer Vision by Andrew Zisserman

This is an advanced-level course that teaches you how to use machine learning for computer vision tasks. The course covers topics such as image classification, object detection, and image segmentation.

7. Machine Learning for Robotics by Ken Goldberg

This is an advanced-level course that teaches you how to use machine learning for robotics tasks. The course covers topics such as motion planning, control, and perception.

8. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy

This is a graduate-level course that teaches you machine learning from a probabilistic perspective. The course covers topics such as Bayesian inference and Markov chain Monte Carlo methods.

9. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

This is a graduate-level course that teaches you deep learning. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.

Are you interested in machine learning, data science, and analytics? Take the first step by enrolling in our comprehensive data science course

Each course is carefully crafted and delivered by world-renowned experts, covering everything from the fundamentals to advanced techniques. Gain expertise in data analysis, deep learning, neural networks, and more. Step up your game and make accurate predictions based on vast datasets.

Decoding the popularity of ML among students and professional 

Among the wave of high-paying tech jobs, there are several reasons for the growing interest in machine learning, including: 

  1. High Demand: As the world becomes more data-driven, the demand for professionals with expertise in machine learning has grown. Companies across all industries are looking for people who can leverage machine-learning techniques to solve complex problems and make data-driven decisions. 
  2. Career Opportunities: With the high demand for machine learning professionals comes a plethora of career opportunities. Jobs in the field of machine learning are high-paying, challenging, and provide room for growth and development. 
  3. Real-World Applications: Machine learning has numerous real-world applications, ranging from fraud detection and risk analysis to personalized advertising and natural language processing. As more people become aware of the practical applications of machine learning, their interest in learning more about the technology grows. 
  4. Advancements in Technology: With the advances in technology, access to machine learning tools has become easier than ever. There are numerous open-source machine-learning tools and libraries available that make it easy for anyone to get started with machine learning. 
  5. Intellectual Stimulation: Learning about machine learning can be an intellectually stimulating experience. Machine learning involves the study of complex algorithms and models that can make sense of large amounts of data. 

Enroll yourself in these courses now

In conclusion, if you’re looking to improve your skills, taking advantage of these free machine learning courses from top universities is a great way to get started. By investing the time and effort required to complete these courses, you’ll be well on your way to building a successful career in this exciting and rapidly evolving field.

Data Science Dojo
Ali Mohsin
| August 3

Data Science Dojo has launched  Jupyter Hub for Deep Learning using Python offering to the Azure Marketplace with pre-installed Deep Learning libraries and pre-cloned GitHub repositories of famous Deep Learning books and collections which enables the learner to run the example codes provided.

What is Deep Learning?

Deep learning is a subfield of machine learning and artificial intelligence (AI) that mimics how people gain specific types of knowledge. Deep learning algorithms are incredibly complex and the structure of these algorithms, where each neuron is connected to the other and transmits information, is quite similar to that of the nervous system.

Also, there are different types of neural networks to address specific problems or datasets, for example, Convolutional neural networks (CNNs) and Recurrent neural networks (RNNs).

While in the field of Data Science, which also encompasses statistics and predictive modeling, deep learning contains a key component. This procedure is made quicker and easier by deep learning, which is highly helpful for data scientists who are tasked with gathering, processing, and interpreting vast amounts of data.

Deep Learning using Python

Python, a high-level programming language that was created in 1991 and has seen a rise in popularity, is compatible with deep learning, which has contributed to its development. While several languages, including C++, Java, and LISP, can be used with deep learning, Python continues to be the preferred option for millions of developers worldwide.

Additionally, data is the essential component in all deep learning algorithms and applications, both as training data and as input. Python is a great tool to employ for managing large volumes of data for training your deep learning system, inputting input, or even making sense of its output because it is primarily used for data management, processing, and forecasting.

PRO TIP: Join our 5-day instructor-led Python for Data Science training to enhance your deep learning skills.

deep learning

Challenges for individuals

Individuals who want to upgrade their path from Machine Learning to Deep Learning and want to start with it usually lack the resources to gain hands-on experience with Deep Learning. A beginner in Deep Learning also faces compatibility issues while installing libraries.

What we provide

Jupyter Hub for Deep Learning using Python solves all the challenges by providing you an effortless coding environment in the cloud with pre-installed Deep Learning python libraries which reduces the burden of installation and maintenance of tasks hence solving the compatibility issues for an individual.

Moreover, this offer provides the user with repositories of famous authors and books on Deep Learning which contain chapter-wise notebooks with some exercises which serve as a learning resource for a user in gaining hands-on experience with Deep Learning.

The heavy computations required for Deep Learning applications are not performed on the user’s local machine. Instead, they are performed in the Azure cloud, which increases responsiveness and processing speed.

Listed below are the pre-installed python libraries related to Deep learning and the sources of repositories of Deep Learning books provided by this offer:

Python libraries:

  • NumPy
  • Matplotlib
  • Pandas
  • Seaborn
  • TensorFlow
  • Tflearn
  • PyTorch
  • Keras
  • Scikit Learn
  • Lasagne
  • Leather
  • Theano
  • D2L
  • OpenCV


  • GitHub repository of book Deep Learning with Python 2nd Edition, by author François Chollet.
  • GitHub repository of book Hands-on Deep Learning Algorithms with Python, by author Sudharsan Ravichandran.
  • GitHub repository of book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, by author Geron Aurelien.
  • GitHub repository of collection on Deep Learning Models, by author Sebastian Raschka.


Jupyter Hub for Deep Learning using Python provides an in-browser coding environment with just a single click, hence providing ease of installation. Through this offer, a user can work on a variety of Deep Learning applications self-driving cars, healthcare, fraud detection, language translations, auto-completion of sentences, photo descriptions, image coloring and captioning, object detection, and localization.

This Jupyter Hub for Deep Learning instance is ideal to learn more about Deep Learning without the need to worry about configurations and computing resources.

The heavy resource requirement to deal with large datasets and perform the extensive model training and analysis for these applications is no longer an issue as heavy computations are now performed on Microsoft Azure which increases processing speed.

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data.

We are therefore adding a free Jupyter Notebook Environment dedicated specifically to Deep Learning using Python. Install the Jupyter Hub offer now from the Azure Marketplace, your ideal companion in your journey to learn data science!

Try Now!

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