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A Hands-On Introduction to Embeddings and Transformer Architecture

Struggling to understand the complexities of embeddings and transformer architecture?

Dive into our comprehensive course and unlock the secrets to mastering these fundamental concepts. Gain practical experience and enhance your understanding to leverage embeddings and transformer architecture effectively in your projects.

Intermediate

 | 1 Course | 

6 Hours

In collaboration with
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Earn a verified certificate from University of New Mexico Continuing Education.
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Why Embeddings

Master the foundations of NLP:
  • Understand and explore common vectorization techniques.
  • Dive into Word2Vec and semantic encoding for word and sentence representations.
  • Uncover the key concepts behind Transformer models and attention mechanisms.
Practical Skills in Text Embedding and Similarity Measures:
  • Create TF-IDF embeddings and calculate sentence similarity metrics.
  • Analyze text through self-attention for contextual understanding.
  • Implement semantic search methods beyond simple keyword matching.
Expand your skillset and open new opportunities:
  • Gain valuable skills for text analysis, machine translation, and more.
  • Enhance your understanding of large language models (LLMs).
  • Stand out in the job market with in-demand NLP expertise.
Foundational and Advanced Vectorization Knowledge::
  • Explore advanced concepts like multilingual search and embedding limitations.
  • Learn strategies to overcome challenges and enhance semantic search accuracy.
  • Stay ahead of the curve in the rapidly evolving field of NLP.
Join a community of learners:
  • Access to expert instructors and supportive peers.
  • Engage in interactive exercises and real-world applications.
  • Receive personalized feedback and guidance.
Exploration of Transformer Models and Semantic Search:
  • Master Transformers, empower search, unlock NLP expertise
  • Decode text data, build smarter search, shape NLP future.
  • Dive into Transformers, revolutionize search, conquer NLP challenges

Curriculum

Introduction to Embeddings and Vectorization Techniques

  • Review of Classical Techniques
  • Vectorization Techniques:
    • Binary/One-Hot Encoding
    • Count-Based Methods
    • Term Frequency-Inverse Document Frequency (TF-IDF)

Advanced Vectorization and Local Context Capture

  • N-grams: Concept and Applications
  • Challenges with Capturing Local Context

Semantic Encoding Techniques

  • Overview of Word2Vec:
    • Concept and Development
    • Dense Word Embeddings
  • Applications of Word2Vec in Text Analytics and NLP Tasks

Diving Deep into Text Embeddings

  • Understanding Word and Sentence Embeddings
  • Text Similarity Measures:
    • Dot Product
    • Cosine Similarity
    • Inner Product

Hands-On Exercise: Working with Embeddings

  • Creating TF-IDF Embeddings on a Document Corpus
  • Calculating Similarity Between Sentences Using Cosine Similarity and Dot Product

Exploring Large Language Models and Transformer Architecture

  • Introduction to Transformer Models and Attention Mechanisms:
    • Encoder-Decoder Structure
    • Transformer Networks: Tokenization, Embedding, Positional Encoding, and Transformer Blocks
    • Attention Mechanisms:
      • Self-Attention
      • Multi-Head Attention
  • Hands-On Exercise: Understanding Attention Mechanisms
    • Self-Attention for Contextual Word Analysis

Semantic Search: Beyond Lexical Limitations

  • Understanding and Implementing Semantic Search:
    • Introduction and Importance
    • Differentiating Semantic from Lexical Search
    • Using Text Embeddings for Semantic Search
  • Exploring Advanced Concepts in Semantic Search:
    • Multilingual Search
    • Limitations and Challenges of Embeddings and Similarity
    • Enhancing Semantic Search Beyond Embeddings and Similarity

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Frequently Asked Questions

Will I get a certificate upon completion?

Yes, you will get a certified upon completion.

How many Continuing Education Credits (CEUs) will I receive?

Yes, you will receive 0.6 CEU’s after completing the course. You will be able to request a transcript from the University of New Mexico by paying a fee of ten US Dollars.

How long does the course take to complete?

Our quickest learners have completed the course as quickly as in 6 hours.

What software and subscriptions are included in the registration fee?

An assortment of software subscriptions worth $500, enhancing the value of the program.

A repository of practice materials.

A 12-month unlimited access to all learning resources, allowing for continuous learning and review.

Are there prerequisites for enrollment?

No specific prerequisites are required. The course is tailored to accommodate participants with varying levels of programming knowledge, providing a comprehensive learning path.

Can I get support and guidance after the course?

Yes, Data Science Dojo provides ongoing support and guidance to the course participants even after the program ends. This includes access to a community of fellow participants and instructors who can help answer questions and provide further assistance.

If I have questions during the live instructor-led sessions or while working on homework?

Yes, our live instructor-led sessions are interactive. During these sessions, students are encouraged to ask questions, and our instructors respond without rushing. Additionally, discussions within the scope of the topic being taught are actively encouraged. We understand that questions may arise during homework, and to assist with that, we have a discord channel to help unblock students between sessions. Rest assured, you won't have to figure everything out by yourself – we are committed to providing the support you need for a successful learning experience.

Are the live sessions recorded for viewing later?

Yes, each live session is recorded and made available to students for review a few days after it has taken place.

How do I access the learning portal?

Once you are registered for the program, you will receive a few emails from us. One of those emails will contain steps to create your learning portal account and access the program content.

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