Developers, data scientists, AI/ML enthusiasts, and students interested in hands-on deep learning using popular frameworks

Basic knowledge of Python programming and fundamental machine learning concepts; familiarity with linear algebra is recommended

Course Outline:

Module 1: Introduction to Deep Learning Frameworks

1. Overview of TensorFlow and PyTorch
2. Key differences and use cases
3. Setting up development environments

Module 2: Neural Network Basics in Practice

1. Building simple neural networks
2. Understanding tensors and operations
3. Forward pass and loss computation

Module 3: Working with TensorFlow (Keras)

1. Building models using Keras API
2. Training and evaluating models
3. Saving and loading models

Module 4: Working with PyTorch

1. Tensors and autograd
2. Building models using PyTorch
3. Training loops and optimization

Module 5: Data Handling and Preprocessing

1. Loading datasets (images, text)
2. Data transformation and normalization
3. Using DataLoader and pipelines

Module 6: Model Evaluation and Improvement

1. Metrics and validation techniques
2. Avoiding overfitting
3. Hyperparameter tuning basics

Module 7: Building Real-World Applications

1. Image classification example
2. Intro to NLP tasks
3. Comparing TensorFlow vs PyTorch implementations