Deep Learning with Pytorch

6 months duration

9 modules

Updated Feb 20, 2026

Data & Analytics

Course Overview

Get to know what this course is all about and what you'll learn

Course Description

Master neural networks, CNNs, Transformers, and Large Language Models from scratch. A comprehensive 6 months program that takes you from Python basics to building production-ready AI systems with PyTorch.

What You'll Learn

This comprehensive program takes you from Classical Machine Learning through to cutting-edge Large Language Models. Starting with traditional ML (scikit-learn), you'll understand why deep learning exists before diving into neural networks with PyTorch. The curriculum spans 9 phases: Classical ML Foundations, Math & PyTorch Basics, Core Deep Learning Mechanics, PyTorch Abstractions, Core Architectures (CNNs, RNNs, Transformers), Practical Deployment, NLP, Generative Networks (VAEs, GANs, Diffusion), and LLMs.

By completion, you'll build and train ML models from scratch, implement computer vision and NLP solutions, fine-tune Large Language Models using LoRA and RAG, and deploy models to production.

Ideal for software developers, data analysts, and Python programmers entering AI. Weekend classes (online) suit working professionals. Prerequisites: Basic Python and high school math.

Course Curriculum

9 modules • Learn at your own pace • Hands-on experience

Course Modules

This foundational module covers traditional machine learning before diving into deep learning. You'll master the complete ML workflow using scikit-learn: data preparation, model building, evaluation, and improvement. Topics include supervised vs unsupervised learning, regression models (Linear, Polynomial), classification models (Logistic Regression, Decision Trees, Random Forests, SVM), clustering (K-Means), and essential evaluation metrics (Accuracy, Precision, Recall, F1, ROC-AUC). By understanding classical ML first, you'll appreciate why and when deep learning is needed.

What you'll learn

Understand the ML landscape: supervised vs unsupervised learning, regression vs classification
Prepare data properly: train/validation/test splits, cross-validation, feature scaling
Build and train regression models: Linear Regression, Polynomial Regression, Ridge, Lasso
Build and train classification models: Logistic Regression, Decision Trees, Random Forests, SVM
Evaluate models using appropriate metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC
Diagnose and fix overfitting/underfitting using regularization and hyperparameter tuning
Master the scikit-learn workflow: pipelines, preprocessing, and model persistence

Build the mathematical intuition and PyTorch skills essential for deep learning.

This module covers linear algebra (vectors, matrices, tensors), calculus (derivatives, chain rule, gradients), probability distributions, and comprehensive PyTorch tensor operations including GPU computing.

You'll also master automatic differentiation with PyTorch's autograd system.

What you'll learn

Perform vector and matrix operations essential for neural networks
Master PyTorch tensor creation, manipulation, and GPU computing
Understand and use automatic differentiation with autograd
Build computation graphs and control gradient flow

Build neural networks from the ground up before using any framework abstractions.

This module covers artificial neurons, activation functions, forward propagation, the backpropagation algorithm, gradient flow challenges (vanishing/exploding gradients), weight initialization, and complete training loop implementation with loss functions, optimizers, and learning rate scheduling.

What you'll learn

Build neural networks from scratch without using nn.Module
Implement and understand backpropagation using the chain rule
Diagnose and solve vanishing/exploding gradient problems
Master the complete training loop with proper loss functions and optimizers

Master PyTorch's core abstractions for building neural networks efficiently.

This module covers nn.Module internals, custom module creation, parameters vs buffers, module composition patterns (Sequential, ModuleList, ModuleDict), hooks for debugging, and comprehensive coverage of optimizers and regularization techniques including Dropout and Batch Normalization.

What you'll learn

Build custom nn.Module classes with proper structure
Compose modules using Sequential, ModuleList, and ModuleDict
Apply regularization techniques to prevent overfitting
Use normalization layers effectively in different contexts

Master the fundamental deep learning architectures that power modern AI.

This module covers Convolutional Neural Networks for computer vision, Recurrent Neural Networks for sequential data, Attention Mechanisms that revolutionized the field, and Transformers—the architecture behind GPT and BERT.

What you'll learn

Build CNN architectures including ResNet with skip connections
Implement RNNs, LSTMs, and GRUs for sequence processing
Master attention mechanisms and multi-head attention
Build complete Transformer encoder-decoder from scratch

Bridge the gap from experiments to production systems.

This module covers transfer learning strategies for leveraging pre-trained models, fine-tuning techniques, and comprehensive model deployment including optimization, export formats, API development, and containerization.

What you'll learn

Apply transfer learning and fine-tuning to real problems
Optimize models for production deployment
Build REST APIs for model inference
Deploy models using Docker containers

Master natural language processing with deep learning.

This module covers text representation (tokenization, embeddings), sequence-to-sequence models for translation and summarization, and modern NLP architectures including BERT and GPT with hands-on use of the Hugging Face ecosystem.

What you'll learn

Implement tokenization and use pre-trained embeddings
Build sequence-to-sequence models with attention
Understand BERT and GPT architectures
Fine-tune pre-trained models using Hugging Face

Create AI that generates new content.

This module covers autoencoders and VAEs for learning latent representations, GANs for adversarial generation, and diffusion models—the technology behind Stable Diffusion and modern image generation.

What you'll learn

Build VAEs and understand latent space representations
Implement GAN training and diagnose common issues
Understand diffusion models and denoising training
Generate images using learned generative models

Work with state-of-the-art language models.

This module covers LLM architecture internals (GPT, LLaMA), efficient fine-tuning techniques (LoRA, QLoRA), Retrieval-Augmented Generation for production applications, and a comprehensive capstone project to demonstrate your skills.

What you'll learn

Understand GPT and LLaMA architectures in detail
Fine-tune LLMs efficiently using LoRA and QLoRA
Build RAG pipelines for production applications
Complete an end-to-end AI project from design to deployment