Program Structure

Module 1: Introduction to PyTorch

• What is PyTorch? Overview and key features for AI and machine learning.
• PyTorch architecture: Tensors, graphs, and sessions.
• Installing and setting up PyTorch for different platforms (CPU/GPU environments).

Module 2: Building Machine Learning Models with PyTorch

• Supervised learning: Building regression and classification models using PyTorch.
• Model evaluation and tuning: Accuracy, loss functions, and optimization techniques.
• Hands-on: Implementing a basic machine learning model using PyTorch and evaluating performance.

Module 3: Deep Learning with PyTorch

• Introduction to neural networks and their components: layers, activation functions, and weights.
• Building a simple feedforward neural network (FNN) using PyTorch.
• Optimizing deep learning models using backpropagation and gradient descent.

Module 4: Convolutional Neural Networks (CNNs) in PyTorch

• Understanding CNNs: Convolutional layers, pooling, and fully connected layers.
• Building a CNN for image classification using PyTorch.
• Regularization techniques: Dropout, data augmentation, and batch normalization.

Module 5: Recurrent Neural Networks (RNNs) and LSTMs in PyTorch

• Introduction to RNNs and their applications in sequential data processing.
• Building an LSTM (Long Short-Term Memory) network for time-series forecasting or text generation.
• Optimizing RNNs and LSTMs for tasks such as text generation or stock price prediction.

Module 6: Transfer Learning with PyTorch

• What is transfer learning and why it’s important in deep learning.
• Using pre-trained models in PyTorch for image classification, NLP, and feature extraction.
• Fine-tuning pre-trained models for new tasks and datasets.

Module 7: PyTorch for Natural Language Processing (NLP)

• Text preprocessing and tokenization techniques in PyTorch.
• Building a text classification model using PyTorch and embedding layers.
• Sequence-to-sequence models for machine translation or text summarization.

Module 8: Reinforcement Learning with PyTorch

• Introduction to reinforcement learning: Markov Decision Processes (MDPs), rewards, and actions.
• Building reinforcement learning models using Q-learning and Deep Q Networks (DQNs) in PyTorch.
• Evaluating reinforcement learning agents in various environments.

Module 9: Model Deployment with PyTorch

• Saving and loading PyTorch models for inference.
• Deploying PyTorch models to production with PyTorch Serving.
• Serving models in the cloud using TensorFlow Lite and TensorFlow.js for edge devices.

Module 10: Advanced Topics in PyTorch

• Transfer learning and fine-tuning pre-trained models for faster development.
• Generative models in PyTorch: Generative Adversarial Networks (GANs) and Autoencoders.
• Introduction to PyTorch 2.x: Eager execution and Keras API integration.

Final Project

• Build and deploy a real-world AI model using PyTorch for an industry problem (e.g., image classification, time-series forecasting, or NLP tasks).
• Optimize and evaluate the model for real-world performance and deployment.
• Example projects: AI-powered image classifier, text sentiment analysis model, or time-series forecasting application.