Master Deep Learning and Generative AI with PyTorch

Welcome to the repository for "Master Deep Learning and Generative AI with PyTorch". This repository serves as a structured learning path covering fundamental to advanced deep learning concepts using PyTorch.

📚 Course Overview

This course covers the following topics in-depth:

Master-Deep-Learning-and-Generative-AI-with-PyTorch/
│── README.md
│── torch/                   # Basics of PyTorch
│── linear_regression/        # Implementing Linear Regression in PyTorch
│── activation_functions/     # All Activation Functions Implementations
│── loss_and_cost_functions/  # Understanding Loss & Cost Functions
│── optimizers/               # Different Optimization Algorithms
│── projects/                 # Hands-on Deep Learning Projects
│── improve_nn_performance/   # Techniques to Improve Neural Networks
│── nlp/                      # Natural Language Processing with PyTorch
│── gen_ai_models/            # Generative AI Models (GANs, VAEs, Transformers)
│── transformers/             # Transformer Models (BERT, GPT, etc.)
│── vision_transformer/       # Vision Transformer (ViT) Implementations

🔥 Core Concepts (torch/)

• Introduction to PyTorch tensors, operations, and autograd
• Loading datasets with torchvision
• Building basic neural networks

📊 Linear Regression (linear_regression/)

• Implementing Linear Regression from scratch
• Using PyTorch's nn.Linear for regression models
• Loss functions and optimization for regression tasks

⚡ Activation Functions (activation_functions/)

• Implementing and understanding activation functions:
  • Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax, GELU, SiLU, etc.
• Properties and characteristics of activation functions
• Choosing the right activation function for your model

🎯 Loss and Cost Functions (loss_and_cost_functions/)

• Understanding Mean Squared Error (MSE), Cross-Entropy, Huber Loss, etc.
• Implementing loss functions in PyTorch
• Visualizing loss functions

🚀 Optimizers (optimizers/)

• Implementing Gradient Descent, Adam, RMSProp, AdaGrad, etc.
• Comparison of different optimization techniques
• Learning rate scheduling and fine-tuning

💡 Hands-on Projects (projects/)

• Implementing end-to-end deep learning projects
• Classification, regression, and real-world applications
• Computer vision and NLP-based projects

⚙️ Improving Neural Network Performance (improve_nn_performance/)

• Weight initialization techniques (Xavier, He, etc.)
• Batch Normalization, Dropout, Regularization techniques
• Gradient clipping, learning rate scheduling, early stopping

📝 Natural Language Processing (NLP) (nlp/)

• Text preprocessing: Tokenization, Stemming, Lemmatization
• Text vectorization: One-Hot Encoding, TF-IDF, Word2Vec
• Implementing NLP models using PyTorch

🎨 Generative AI Models (gen_ai_models/)

• Implementing Variational Autoencoders (VAEs)
• Building Generative Adversarial Networks (GANs)
• Diffusion models and text-to-image generation

🤖 Transformers (transformers/)

• Attention mechanism and self-attention
• Implementing Transformer models like BERT, GPT, T5
• Pretrained Transformers and Fine-tuning

👀 Vision Transformer (vision_transformer/)

• Understanding Vision Transformer (ViT)
• Implementing Vision Transformer using PyTorch
• Image classification using Transformer-based models

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💻 Prerequisites

• Basic programming knowledge (preferably in Python)
• High school-level mathematics (linear algebra, calculus)
• No prior AI/ML experience needed—this course covers everything from scratch!

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📌 Who Should Join This Course?

• AI & Deep Learning Beginners
• Software Developers transitioning to AI/ML
• Data Scientists looking to master PyTorch

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🔗 Course Access

If you want the complete guided learning experience, enroll in the course on Udemy:
Master Deep Learning and Generative AI with PyTorch in Hindi