Introduction

PyTorch has become one of the most popular deep learning frameworks, known for its flexibility, dynamic computation graphs, and ease of use. This course provides a comprehensive, hands-on approach to developing and deploying deep learning models using PyTorch.

Participants will gain expertise in building neural networks, optimizing models, implementing advanced deep learning architectures, and deploying AI solutions in real-world applications. The course also covers cutting-edge topics such as transformers, generative models, and MLOps for production-ready AI.

Course Objectives

By the end of this course, participants will be able to:

• Understand fundamental deep learning concepts and their implementation in PyTorch.
• Build and train neural networks for image, text, and structured data processing.
• Work with CNNs, RNNs, LSTMs, Transformers, and GANs for advanced AI applications.
• Optimize model performance using hyperparameter tuning and regularization.
• Deploy PyTorch models using TorchScript, ONNX, and cloud platforms.
• Implement MLOps practices for monitoring, retraining, and CI/CD integration.

Who Should Attend?

This course is ideal for:

• Data scientists developing deep learning models.
• AI/ML engineers working on production AI systems.
• Researchers in computer vision, NLP, and reinforcement learning.
• Software engineers integrating AI into applications.
• Cloud architects designing scalable AI solutions.

Day-by-Day Course Breakdown

Day 1: Introduction to Deep Learning with PyTorch

Fundamentals of Deep Learning & PyTorch

• Understanding deep learning and neural networks
• Introduction to PyTorch: Tensors, Autograd, and Computational Graphs
• Setting up the PyTorch development environment

Building & Training Neural Networks

• Defining models using torch.nn and training with torch.optim
• Data preprocessing using torchvision and torchtext
• Hands-on lab: Training a simple deep learning model with PyTorch

Day 2: Convolutional Neural Networks (CNNs) for Computer Vision

CNN Architecture & Training

• Understanding convolutional layers, pooling, and activation functions
• Implementing CNNs for image classification
• Using pretrained models (ResNet, VGG, EfficientNet) with transfer learning
• Hands-on lab: Image classification using CNNs in PyTorch

Day 3: Natural Language Processing (NLP) with PyTorch

Recurrent Neural Networks (RNNs) & Transformers

• Understanding RNNs, LSTMs, and GRUs for sequential data
• Implementing NLP models for text generation, translation, and sentiment analysis
• Introduction to Transformers and BERT in PyTorch
• Hands-on lab: Training an NLP model using LSTMs and Transformers

Day 4: Generative Models & Model Optimization

Generative Adversarial Networks (GANs) & Variational Autoencoders (VAEs)

• Fundamentals of GANs: Generator & Discriminator training
• Implementing StyleGAN, CycleGAN, and VAEs in PyTorch
• Hands-on lab: Training a GAN to generate synthetic images

Hyperparameter Tuning & Model Optimization

• Implementing dropout, batch normalization, and learning rate scheduling
• Using PyTorch Lightning for streamlined model training
• Hands-on lab: Optimizing model performance with PyTorch Lightning

Day 5: Model Deployment & MLOps

Deploying PyTorch Models

• Exporting models with TorchScript & ONNX for deployment
• Running models on edge devices with PyTorch Mobile
• Deploying models in production using AWS SageMaker, Google Cloud AI, and Azure ML
• Hands-on lab: Deploying a PyTorch model as an API

Capstone Project: End-to-End Deep Learning Solution

• Participants will build, optimize, and deploy a complete deep learning application
• Final presentations and peer reviews

Conclusion & Certification

At the end of the course, participants will receive a Certificate of Completion, demonstrating their expertise in PyTorch for Deep Learning.

This course integrates practical hands-on labs, real-world case studies, and industry best practices, equipping learners with the skills to develop and deploy AI solutions at scale.