Deep Learning with TensorFlow

This program introduces deep learning concepts and practical model development using TensorFlow. It focuses on building neural networks for solving complex problems such as image recognition, natural language processing, and predictive analytics. Participants will gain hands-on experience in designing and training deep learning models.

Introduction to Deep Learning

• What is deep learning and how it works

• Neural networks fundamentals

• Difference between ML and deep learning

• Real-world applications

• Overview of TensorFlow ecosystem

TensorFlow Basics

• Introduction to TensorFlow framework

• Building simple neural networks

• Understanding tensors and operations

• Model structure and architecture

• Training process overview

Neural Network Design

• Layers and activation functions

• Forward and backward propagation

• Loss functions and optimization

• Model architecture design

• Improving network performance

Convolutional & Recurrent Networks

• CNNs for image processing

• RNNs for sequential data

• Applications in vision and NLP

• Feature extraction techniques

• Real-world deep learning use cases

Model Training & Optimization

• Training deep learning models

• Handling overfitting and regularization

• Hyperparameter tuning

• Performance evaluation

• Deploying deep learning models