Learn Together, Grow Together

Probabilities and Statistics Concept Needed for GenerativeAI

Transformer Implementation in PyTorch

Course Overview This course is designed to provide a comprehensive understanding of the Transformer architecture and its implementation using PyTorch. Transformers have revolutionized deep learning, especially in natural language processing (NLP) and computer vision. They form the foundation of powerful models like BERT, GPT, and Vision Transformers (ViTs). Through a hands-on, step-by-step approach, this course will guide you from the fundamental concepts of self-attention to building a fully functional Transformer model from scratch. You will gain both theoretical knowledge and practical coding skills, enabling you to apply Transformers to a wide range of deep learning tasks. By the end of this course, you will have an in-depth understanding of how Transformers process information, how to train and optimize them effectively, and how to leverage PyTorch to build state-of-the-art models. What You Will Learn Introduction to Transformers Evolution of deep learning architectures: From RNNs to LSTMs to Transformers Why Transformers outperform traditional sequence models Real-world applications of Transformers in NLP, vision, and beyond Mathematical Foundations Understanding self-attention and dot-product attention Multi-head attention: Enhancing the learning capacity The role of positional encoding in Transformers Building Blocks of a Transformer Layer normalization and residual connections Feedforward layers and activation functions Encoder-Decoder structure in Transformers Hands-on Implementation in PyTorch Setting up the environment and dependencies Implementing self-attention and multi-head attention from scratch Constructing the Transformer Encoder and Decoder layers Training a Transformer Model Preparing data for NLP tasks (tokenization, batching, and padding) Training a Transformer for machine translation or text generation Fine-tuning Transformers on custom datasets Optimization and Performance Tuning Choosing the right loss functions and optimizers (e.g., AdamW) Implementing learning rate scheduling (e.g., warm-up and cosine decay) Handling overfitting with dropout and regularization Extending to Advanced Applications Implementing and fine-tuning pre-trained Transformers (e.g., BERT, GPT) Using Transformers for non-NLP tasks (e.g., Vision Transformers, time-series forecasting) Distributed training for large-scale Transformer models

GPT 2/3 Implementation in PyTorch

GPT-2/GPT-3 model , which is a deep learning-based language model developed by OpenAI. These models belong to the Transformer architecture and are designed for natural language processing (NLP) tasks such as text generation, summarization, translation, and more. GPT-2 was an earlier version capable of generating coherent and contextually relevant text. GPT-3 is a more advanced version with 175 billion parameters , making it significantly more powerful in understanding and generating human-like text. Both models use self-attention mechanisms and large-scale training on internet text to predict and generate text based on input prompts. They are widely used in chatbots, AI assistants, and various NLP applications.

DeepSeek R1 Model and Code Analysis

DeepSeek R1: Model and Code Analysis DeepSeek R1 is an open-weight large language model (LLM) released by DeepSeek , designed to enhance reasoning, coding, and general-purpose natural language understanding. It follows recent advancements in transformer-based architectures and is particularly optimized for high-quality inference, making it a strong competitor in AI-driven applications. Key Features of DeepSeek R1 Model Architecture : Transformer-based model, optimized for efficient and scalable reasoning tasks. Code Understanding : Strong performance in code completion, generation, debugging, and refactoring . Natural Language Processing : Advanced comprehension in question-answering, summarization, and knowledge retrieval . Performance : Benchmarked against leading open-source models, showing competitive results in various NLP and coding benchmarks. Use Cases AI-powered coding assistants (e.g., autocompletion, error fixing, documentation generation). Chatbots and Virtual Assistants for advanced reasoning. Automated Content Creation for generating coherent and structured text.

AI Agent Building Using LangGraph

In this course, you’ll learn how to build powerful AI agents using LangGraph, a framework that enables multi-agent collaboration, decision-making, and dynamic workflows. By leveraging graph-based architectures, you’ll explore how AI agents can plan, communicate, and interact autonomously.