Overview

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 applicationsStep-by-step guidance on training, fine-tuning, and deploying modelsCovers both theory and practical implementationLearn from industry professionals with deep AI expertiseGain the skills to build and deploy custom AI solutionsUnderstand challenges and solutions in large-scale AI deploymentEnhance problem-solving skills through real-world AI case studiesWhat You'll Learn:Section 1: Introduction ( Understanding Transformers):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.High-Level Understanding of Transformers Architecture:Grasp the overarching architecture of Transformers, including the key components that define their structure and functionality.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 ArchitectureInput Embedding:Learn the essential concept of transforming input data into a format suitable for processing within the Transformer model.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.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.Autoencoding LM - BERT, Autoregressive LM - GPT, Sequence2Sequence LM - T5:Explore different types of language models, including their characteristics and use cases.Tokenization:Understand the process of breaking down text into tokens, a foundational step in natural language processing.Section 3: Text ClassificationFine-tuning BERT for Multi-Class Classification:Gain hands-on experience in adapting pre-trained models like BERT for multi-class classification tasks.Fine-tuning BERT for Sentiment Analysis:Learn how to fine-tune BERT specifically for sentiment analysis, a common and valuable application in NLP.Fine-tuning BERT for Sentence-Pairs:Understand the process of fine-tuning BERT for tasks involving pairs of sentences.Section 4: Question AnsweringQA Intuition:Develop an intuitive understanding of question-answering tasks and their applications.Build a QA System Based Amazon ReviewsImplement Retriever Reader ApproachFine-tuning transformers for question answering systemsTable QASection 5: Text GenerationGreedy Search Decoding, Beam Search Decoding, Sampling Methods:Explore different decoding methods for generating text using Transformer models.Train Your Own GPT:Acquire the skills to train your own Generative Pre-trained Transformer model for creative text generation.Section 6: Text SummarizationIntroduction to GPT2, T5, BART, PEGASUS:Understand the characteristics and applications of different text summarization models.Evaluation Metrics - Bleu Score, ROUGE:Learn the metrics used to evaluate the effectiveness of text summarization, including Bleu Score and ROUGE.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 ScratchBuild Custom Tokenizer:Construct a custom tokenizer, an essential component for processing input data in your own Transformer.Getting Your Data Ready:Understand the importance of data preparation and how to format your dataset for training a custom Transformer.Implement Positional Embedding, Implement Transformer Architecture:Gain practical skills in implementing positional embedding and constructing the entire Transformer architecture from scratch.Section 8: Deploy the Transformers Model in the Production EnvironmentModel Optimization with Knowledge Distillation and Quantization:Explore techniques for optimizing Transformer models, including knowledge distillation and quantization.Model Optimization with ONNX and the ONNX Runtime:Learn how to optimize models using the ONNX format and runtime.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: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.Learners will be able to translate theoretical knowledge into hands-on skillsUnderstand how to fine-tune models for specific needs using your own datasets.By the end of this course, you will have the expertise to create, train, and deploy AI models, making a significant impact in the field of artificial intelligence.

Skills

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