A Complete Guide on TensorFlow 2.0 using Keras API Course Syllabus

Overview: This comprehensive course guides you through the complete TensorFlow 2 ecosystem with hands-on projects and real-world applications. You'll progress from foundational concepts to advanced deployment techniques, mastering deep learning using the Keras API. The curriculum spans approximately 8 hours of on-demand video, structured into focused modules that build practical skills in neural networks, computer vision, NLP, and model deployment. Each section emphasizes code implementation, best practices, and production-ready workflows, ensuring you gain the confidence to build and deploy AI solutions effectively.

Module 1: Introduction to TensorFlow 2

Estimated time: 0.5 hours

• Overview of TensorFlow and its role in deep learning
• Setting up the development environment
• Installing TensorFlow 2

Module 2: TensorFlow Basics and Keras API

Estimated time: 0.75 hours

• Understanding tensors, operations, and automatic differentiation
• Building models using the Sequential API
• Building models using the Functional API
• Introduction to Keras as a high-level API

Module 3: Training Neural Networks

Estimated time: 1 hour

• Implementing loss functions and optimizers
• Using evaluation metrics for model assessment
• Setting up training, validation, and testing workflows

Module 4: Convolutional Neural Networks (CNNs)

Estimated time: 1 hour

• Designing CNNs for image classification
• Applying data augmentation techniques
• Using dropout and batch normalization for regularization

Module 5: Recurrent Neural Networks (RNNs) and LSTMs

Estimated time: 1 hour

• Building RNNs for sequential data
• Implementing LSTMs and GRUs for complex patterns
• Working with time series and sequence prediction

Module 6: Natural Language Processing Projects

Estimated time: 1 hour

• Text preprocessing and tokenization
• Using word embeddings in Keras
• Building models for text classification and generation

Module 7: Transfer Learning and Pretrained Models

Estimated time: 0.75 hours

• Applying pretrained models like MobileNet and Inception
• Differentiating between fine-tuning and feature extraction
• Improving model performance using transfer learning

Module 8: TensorFlow Tools and Deployment

Estimated time: 0.75 hours

• Using TensorBoard for monitoring training progress
• Saving and loading trained models
• Deploying models with TensorFlow Lite and TF Serving

Module 9: Real-World Projects and Best Practices

Estimated time: 1.25 hours

• End-to-end implementation of ML/DL projects
• Debugging models and improving performance
• Applying production best practices and performance tuning

Prerequisites

• Basic knowledge of Python programming
• Familiarity with machine learning concepts
• Understanding of neural networks fundamentals (recommended)

What You'll Be Able to Do After

• Build and train neural networks using TensorFlow 2 and Keras
• Develop image classification models using CNNs and transfer learning
• Create models for time series forecasting and NLP tasks
• Deploy machine learning models using TensorFlow Lite and TF Serving
• Use TensorBoard and best practices for model monitoring and optimization