LLM Engineering:Understanding & Deploying Transformer models (Udemy.com)

AI with LLMs and Transformers (A-Z) isn't just a course; it's a transformative experience that arms learners with the expertise, practical skills, and innovation-driven mindset needed to navigate and lead in the ever-evolving landscape of Artificial Intelligence.

Why Take This Course?

• Hands-on, project-based learning with real-world applications

• Step-by-step guidance on training, fine-tuning, and deploying models

• Covers both theory and practical implementation

• Learn from industry professionals with deep AI expertise

• Gain the skills to build and deploy custom AI solutions

• Understand challenges and solutions in large-scale AI deployment

• Enhance problem-solving skills through real-world AI case studies

What You'll Learn:

Section 1: Introduction ( Understanding Transformers) :

1. Explore Transformer's Pipeline Module:

   • Understand the step-by-step process of how data flows through a Transformer model, gaining insights into the model's internal workings.

2. High-Level Understanding of Transformers Architecture:

   • Grasp the overarching architecture of Transformers, including the key components that define their structure and functionality.

3. What are Language Models:

   • Gain an understanding of language models, their significance in natural language processing, and their role in the broader field of artificial intelligence.

   
   

Section 2: Transformers Architecture

1. Input Embedding:

   • Learn the essential concept of transforming input data into a format suitable for processing within the Transformer model.

2. Positional Encoding:

   • Explore the method of adding positional information to input embeddings, a crucial step for the model to understand the sequential nature of data.

3. The Encoder and The Decoder:

   • Dive into the core components of the Transformer architecture, understanding the roles and functionalities of both the encoder and decoder.

4. Autoencoding LM - BERT, Autoregressive LM - GPT, Sequence2Sequence LM - T5:

   • Explore different types of language models, including their characteristics and use cases.

5. Tokenization:

   • Understand the process of breaking down text into tokens, a foundational step in natural language processing.

Section 3: Text Classification

1. Fine-tuning BERT for Multi-Class Classification:

   • Gain hands-on experience in adapting pre-trained models like BERT for multi-class classification tasks.

2. Fine-tuning BERT for Sentiment Analysis:

   • Learn how to fine-tune BERT specifically for sentiment analysis, a common and valuable application in NLP.

3. Fine-tuning BERT for Sentence-Pairs:

   • Understand the process of fine-tuning BERT for tasks involving pairs of sentences.

Section 4: Question Answering

1. QA Intuition:

   • Develop an intuitive understanding of question-answering tasks and their applications.

2. Build a QA System Based Amazon Reviews

3. Implement Retriever Reader Approach

4. Fine-tuning transformers for question answering systems

5. Table QA
   

Section 5: Text Generation

1. Greedy Search Decoding, Beam Search Decoding, Sampling Methods:

   • Explore different decoding methods for generating text using Transformer models.

2. Train Your Own GPT:

   • Acquire the skills to train your own Generative Pre-trained Transformer model for creative text generation.

Section 6: Text Summarization

1. Introduction to GPT2, T5, BART, PEGASUS:

   • Understand the characteristics and applications of different text summarization models.

2. Evaluation Metrics - Bleu Score, ROUGE:

   • Learn the metrics used to evaluate the effectiveness of text summarization, including Bleu Score and ROUGE.

3. Fine-Tuning PEGASUS for Dialogue Summarization:

   • Gain hands-on experience in fine-tuning PEGASUS specifically for dialogue summarization.

Section 7: Build Your Own Transformer From Scratch

1. Build Custom Tokenizer:

   • Construct a custom tokenizer, an essential component for processing input data in your own Transformer.

2. Getting Your Data Ready:

   • Understand the importance of data preparation and how to format your dataset for training a custom Transformer.

3. Implement Positional Embedding, Implement Transformer Architecture:

   • Gain practical skills in implementing positional embedding and constructing the entire Transformer architecture from scratch.

   
   

4. 
   

Section 8: Deploy the Transformers Model in the Production Environment

1. Model Optimization with Knowledge Distillation and Quantization:

   • Explore techniques for optimizing Transformer models, including knowledge distillation and quantization.

2. Model Optimization with ONNX and the ONNX Runtime:

   • Learn how to optimize models using the ONNX format and runtime.

3. Serving Transformers with Fast API, Dockerizing Your Transformers APIs:

   • Acquire the skills to deploy and serve Transformer models in production environments using Fast API and Docker.

Becoming a Transformer Maestro:

By the end of the course:

1. 1. Learners will possess an intimate understanding of how Transformers function, making them true Transformer maestros capable of navigating the ever-evolving landscape of AI innovation.