Description:

This course provides a practical introduction to deep learning using two of the most widely used frameworks: TensorFlow and PyTorch. Participants will learn how to build, train, and evaluate neural network models using both frameworks while understanding their differences and use cases.

Through hands-on labs and demonstrations, learners will implement deep learning models for tasks such as image classification and basic natural language processing. The course emphasizes practical skills, enabling participants to choose and apply the right framework for their projects.

Duration: 

2 Days

Course Code: BDT133

Learning Objectives:

After this course, you will be able to:

1. Understand the fundamentals of deep learning frameworks
2. Compare TensorFlow and PyTorch architectures and use cases
3. Build and train neural network models using both frameworks
4. Work with datasets and preprocess data for deep learning
5. Evaluate model performance and improve accuracy
6. Implement basic deep learning applications

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