About this course
Prepare the environment and review intermediate concepts for advanced learning.
Prerequisites & Readiness Check
2 Parts
- 1:00 Hr
Prerequisites for Mastering TensorFlow: Advanced AI-Driven Solutions
45 Min
TensorFlow Advanced Environment Setup Guide
15 Min
Chapter 1: Advanced Data Pipelines
3 Parts
- 2:15 Hr
Complex Data Loading
45 Min
Advanced Preprocessing
45 Min
Exercise: Create a tf.data Pipeline with Augmentation
45 Min
Chapter 2: Advanced CNN Architectures
3 Parts
- 2:15 Hr
Modern CNN Architectures
45 Min
Optimization Techniques
45 Min
Exercise: Train a CNN with Mixed Precision
45 Min
Chapter 3: Advanced RNNs and Sequence Modeling
3 Parts
- 2:30 Hr
Bidirectional and Stacked RNNs
45 Min
Sequence-to-Sequence Models
45 Min
Exercise: Build a Bidirectional LSTM Classifier
60 Min
Chapter 4: Transfer Learning and Fine-Tuning
3 Parts
- 2:30 Hr
Advanced Fine-Tuning
45 Min
Knowledge Distillation
45 Min
Exercise: Distill a Pretrained CNN Model
60 Min
Chapter 5: Transformers and Attention Mechanisms
3 Parts
- 2:30 Hr
Attention Mechanisms
45 Min
Transformers
45 Min
Exercise: Transformer Model
60 Min
Chapter 6: Generative Models and GANs
3 Parts
- 2:30 Hr
GAN Fundamentals
45 Min
Advanced GANs
45 Min
Exercise: Simple GAN Implementation
60 Min
Chapter 7: Model Optimization and Hyperparameter Tuning
3 Parts
- 2:15 Hr
Hyperparameter Tuning
45 Min
Model Optimization
45 Min
Exercise: Tune Hyperparameters with Bayesian Optimization
45 Min
Chapter 8: Model Deployment
3 Parts
- 2:30 Hr
TensorFlow Serving
45 Min
Mobile and Web Deployment
45 Min
Exercise: Deploy a Model with TensorFlow.js
60 Min
Chapter 9: Distributed Training and MLOps
3 Parts
- 2:30 Hr
Distributed Training
45 Min
MLOps Basics
45 Min
Exercise: Simulate Federated Learning with TFF
60 Min
Capstone Projects
2 Parts
- 55:00 Hr
Knowledge-Distilled Object Detection System
300 Min
Federated Learning for Sentiment Analysis
3000 Min
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