Deep Learning with TensorFlow

Overview

Explore TensorFlow's fundamentals and create deep learning models through interactive discussions. Gain practical skills to develop and implement AI in your applications.

Syllabus

• Introduction to Deep Learning and TensorFlow
Overview of Deep Learning
Introduction to TensorFlow
Setting up the TensorFlow environment
• TensorFlow Basics
Tensors and Operations
Graphs and Sessions
Variables and Placeholders
• Neural Networks with TensorFlow
Deep Neural Networks (DNN)
Activation Functions
Loss Functions and Optimization
• Data Handling
Importing and Preprocessing Data
Dataset APIs and Pipelines
Data Augmentation Techniques
• Convolutional Neural Networks (CNNs)
CNN Architecture
Feature Maps and Pooling
Building CNNs with TensorFlow
• Recurrent Neural Networks (RNNs)
Understanding RNNs and LSTMs
Sequence Data Processing
Implementing RNNs in TensorFlow
• Advanced Deep Learning Models
Transfer Learning Techniques
Generative Adversarial Networks (GANs)
Autoencoders and Unsupervised Learning
• Model Evaluation and Tuning
Model Evaluation Metrics
Hyperparameter Tuning Strategies
Cross-validation and Overfitting
• Deployment of TensorFlow Models
Saving and Loading Models
TensorFlow Serving
Integrating Models into Applications
• Hands-On Projects
Project 1: Image Classification with CNNs
Project 2: Sentiment Analysis with RNNs
Project 3: Building a GAN for Image Generation
• Conclusion and Future Directions
Review of Key Concepts
Emerging Trends in Deep Learning
Resources for Continued Learning

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Subjects

Conference Talks