Master Deep Learning and Generative AI with PyTorch – From Beginner to AI Researcher

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About Course

This course is a complete, end-to-end journey into Deep Learning, Generative AI, and Large Language Models using PyTorch.
You start from the foundations of mathematics and neural networks and progress all the way to research-driven implementations of Transformers, Vision Transformers, Generative Models, NLP systems, and Computer Vision architectures.
Every concept is explained from first principles and reinforced with hands-on PyTorch coding, enabling you to build, understand, and customize AI models confidently.
This course also covers advanced topics like BERT, GPT, LLaMA, Swin Transformers, Stable Diffusion, and multimodal AI systems.

What Will You Learn?

  • Master PyTorch from beginner level to AI research-grade development
  • Understand deep learning from fundamentals to complex architectures
  • Implement neural networks from scratch without black-box shortcuts
  • Build real-world projects in regression, classification, NLP, CV, and generative AI
  • Learn all activation functions, loss functions, and optimizers with PyTorch
  • Develop strong intuition for backpropagation, gradients, and training dynamics
  • Implement Transformer-based architectures and Vision Transformers from research papers
  • Build and understand large language models and multimodal AI systems
  • Work with text, image, and generative models used in cutting-edge AI research
  • Create portfolio-ready projects aligned with industry and research standards

Course Content

Programming & Data Science Fundamentals for AI
This section covers all the essential tools you need to start your journey in AI and Deep Learning. You’ll build a strong foundation in Python programming and core data science libraries including NumPy, Pandas, Matplotlib, and Seaborn. You’ll learn how to write clean Python code, work with arrays and datasets, perform data analysis, and visualize results effectively—skills that are mandatory before moving into machine learning and deep learning with PyTorch.

  • intro
    03:00
  • PYTHON Full Course for Beginners
    16:43:34
  • Python Numpy Full Tutorial For Beginners
    04:33:00
  • PANDAS Full Course with PRACTICAL
    01:42:05
  • Matplotlib Full Tutorial
    04:11:06
  • Python SEABORN Tutorial
    03:58:27
  • GIT Full Tutorial for Beginners
    02:48:56
  • Git and GitHub Tutorial for Beginners
    01:14:17

Core Deep Learning Concepts – From Perceptron to Backpropagation
Dive into the most important foundations of deep learning. This section covers Perceptrons, Multi-Layer Perceptrons (MLPs), forward propagation, and backpropagation, giving you the practical understanding and skills to build neural networks from scratch. Each lecture is hands-on and explained step-by-step, so you can apply these concepts directly in PyTorch projects.

PyTorch: From Fundamentals to AI Research-Level Development
Master PyTorch from first principles, starting with tensors and autograd, and progressing to research-grade model development. Learn how to write clean, scalable, and experiment-ready code used in real AI labs and research teams. By the end, you’ll be able to read papers, implement architectures, and run serious AI experiments in PyTorch.

Hands-On Project: Linear Regression with PyTorch
Apply your deep learning knowledge with a real-world Linear Regression project using PyTorch. This section walks you through data preparation, model building, training, and evaluation, giving you practical experience and confidence to implement AI models from scratch.

Mathematics for Artificial Intelligence
Learn the essential mathematics that powers artificial intelligence. This section covers linear algebra, calculus, probability, statistics, and discrete math, giving you the foundation to understand algorithms, machine learning, and AI models. Each concept is explained practically, so you can apply it to real-world AI problems with confidence.

All Activation Functions in Deep Learning – Explained with PyTorch
Learn and implement all major activation functions used in deep learning. This section covers: Linear, Threshold, Sigmoid, Tanh, Softmax, ReLU, LeakyReLU, Parametric ReLU (PReLU), ELU, SELU, Swish, Softplus, and Mish, with step-by-step explanations and practical PyTorch coding. By the end, you’ll know when and how to use each function in your neural networks to build efficient, high-performing AI models.

All Loss & Cost Functions in Deep Learning – Explained with PyTorch
Master all key loss and cost functions used in neural networks and AI. This section covers: Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Squared Error (RMSE), Root Mean Squared Log Error (RMSLE), Huber Loss (Smooth L1), Log-Cosh Loss, Binary Cross-Entropy (BCE), Categorical Cross-Entropy, and other important cost functions, with step-by-step PyTorch implementations. You’ll learn how each function works, when to use it, and how it impacts model performance.

All Optimizers in Deep Learning – Explained with PyTorch
Learn all major optimization algorithms that make neural networks train effectively. This section covers: Gradient Descent (Batch, Stochastic, Mini-Batch), Exponentially Weighted Moving Average (EWMA), SGD with Momentum, Nesterov Accelerated Gradient (NAG), AdaGrad, RMSProp, and Adam (Adaptive Moment Estimation) — all explained theoretically and implemented step-by-step in PyTorch. By the end, you’ll know how to select and apply the right optimizer to achieve faster convergence and better model performance.

Hands-On Project: Logistic Regression with PyTorch
Build practical skills with a Logistic Regression project in PyTorch. You’ll learn data preprocessing, model creation, training, and evaluation, giving you hands-on experience and a portfolio-ready AI project to showcase your machine learning expertise.

Hands-On Project: Classification with Neural Networks in PyTorch
Get practical experience building classification models using neural networks in PyTorch. This project walks you through data preparation, model building, training, and evaluation, giving beginners hands-on skills and a portfolio-ready project to showcase real AI expertise.

Improving Neural Network Performance
Learn how to optimize and stabilize your neural networks for better performance. This section covers: Vanishing & Exploding Gradients Overfitting & Underfitting Regularization Techniques: L1, L2, Elastic Net Dropout for Robust Models All Key Normalizations: Batch, Layer, Group, Instance, RMS, and input normalization All concepts are explained theoretically and implemented step-by-step in PyTorch, giving you hands-on experience to build efficient and high-performing AI models.

Natural Language Processing (NLP) – Sentiment Analysis, LSTM & Seq2Seq Models
Master NLP techniques with hands-on PyTorch projects. This section covers: Sentiment Analysis using Word Embeddings and Neural Bag of Words Recurrent Neural Networks (LSTM) Sequence-to-Sequence (Seq2Seq) Models You’ll learn how to process text, build NLP models, and implement real-world applications, gaining practical experience to handle AI language tasks and build portfolio-ready projects.

Implementing Transformers from Scratch in PyTorch (Research-Driven Approach)
Implement the Transformer architecture line-by-line in PyTorch while systematically studying the original research paper. Translate mathematical formulations—self-attention, multi-head attention, positional encoding, and normalization—directly into working code. This section builds true architectural understanding, preparing you for LLM research, model scaling, and advanced AI engineering roles.

Computer Vision with PyTorch: From Fundamentals to Modern Architectures
Learn how machines see, understand, and reason about images using PyTorch. This section covers core computer vision concepts, feature learning, and modern deep learning approaches used in real-world systems. Build a strong foundation for image classification, detection, segmentation, and vision-based AI research.

Vision Transformer (ViT): From Research Paper to PyTorch Implementation
Implement the Vision Transformer from scratch in PyTorch by rigorously studying the original research paper. Translate patch embeddings, positional encoding, self-attention, and classification heads from theory into clean, modular code. Gain a deep research-level understanding of how Transformers replace CNNs in modern computer vision systems.

Generative Models – From Fundamentals to Advanced Architectures
Learn how AI systems create new data instead of just predicting it. This section builds strong intuition behind modern generative models, training strategies, and real-world use cases. A foundation that prepares you for advanced Generative AI, image synthesis, text generation, and future models.

BERT From Scratch in PyTorch – Research-Grade Implementation
In this section, you will implement BERT (Bidirectional Encoder Representations from Transformers) completely from scratch using PyTorch, without relying on high-level libraries. You’ll start by understanding the original BERT research paper and its core ideas, then translate each concept into clean, modular code. You will build every major component step by step, including token embeddings, segment embeddings, positional encodings, multi-head self-attention, encoder stacks, and layer normalization. You will also implement Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) to understand how BERT is pre-trained in real research and industry settings. By the end of this section, you won’t just use BERT—you’ll fully understand how it works internally, preparing you for LLM research, model fine-tuning, custom transformer architectures, and advanced NLP engineering roles. What You’ll Build A full BERT encoder architecture in PyTorch Masked Language Modeling (MLM) training pipeline Next Sentence Prediction (NSP) objective A research-ready BERT implementation you can extend or fine-tune

Stable Diffusion From Scratch in PyTorch
In this section, you will build Stable Diffusion from scratch using PyTorch, starting from diffusion theory to a working text-to-image generation pipeline. You’ll understand forward and reverse diffusion processes, noise schedules, and how latent diffusion models drastically reduce computational cost. You will implement core components including UNet denoising networks, text conditioning, CLIP-style embeddings, and sampling strategies used in modern generative AI systems. This section gives you a research-level understanding of how tools like Stable Diffusion and Midjourney work internally.

Swin Transformer From Scratch – Hierarchical Vision Transformers
This section focuses on implementing the Swin Transformer (Shifted Window Transformer) from scratch in PyTorch. You’ll learn how Swin introduces hierarchical feature learning, window-based self-attention, and shifted windows to make transformers scalable for high-resolution vision tasks. You will code window attention, patch merging, shifted window mechanisms, and full Swin blocks step by step. By the end, you’ll understand why Swin Transformers outperform CNNs and vanilla ViTs in real-world computer vision systems.

LLaMA 2 From Scratch – Large Language Model Engineering
In this section, you will implement LLaMA 2 from scratch using PyTorch, gaining deep insight into how modern open-source large language models are designed and trained. You’ll study architectural optimizations such as RMSNorm, Rotary Positional Embeddings (RoPE), grouped-query attention, and efficient transformer blocks. You will build a decoder-only transformer architecture suitable for large-scale language modeling and research experimentation. This section prepares you for LLM fine-tuning, scaling laws, and advanced NLP research.

PaliGemma – Multimodal Vision–Language Model From Scratch
This section explores PaliGemma, a multimodal vision-language model, implemented from scratch in PyTorch. You’ll learn how visual encoders and language models are fused to enable image understanding, visual question answering, and multimodal reasoning. You will integrate vision embeddings with transformer-based language models, handle cross-modal attention, and understand how multimodal LLMs power modern AI assistants. This section builds a strong foundation for Vision-Language research and multimodal AI systems.

GPT From Scratch – Decoder-Only Transformer Architecture
In this section, you will implement GPT (Generative Pre-trained Transformer) from scratch in PyTorch. You’ll start with causal self-attention and masking, then build transformer decoder blocks exactly as described in the original research. You’ll understand autoregressive language modeling, token prediction, and text generation pipelines. By the end, you’ll have a fully working GPT model that you can train, fine-tune, and extend for research or production use.

U-Net From Scratch in PyTorch – Backbone of Diffusion Models
In this section, you will implement U-Net from scratch using PyTorch, the backbone architecture behind diffusion models, image segmentation, and generative vision systems. You will build encoder-decoder pathways, skip connections, downsampling and upsampling blocks, and understand why U-Net is critical for tasks requiring precise spatial information. This section directly supports advanced topics like Stable Diffusion, medical imaging, and image-to-image translation.

And Lot more is Coming.

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