100% OFF- Mastering AI with Transformers and LLMs

Mastering AI With Transformers and LLMs for NLP Applications 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.

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.
   2. Learners will be able to translate theoretical knowledge into hands-on skills
   3. Understand 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.