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
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- 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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On-Demand
