Course Overview
This project-driven course provides a comprehensive introduction to TensorFlow, Google’s premier machine learning framework. Participants will gain practical experience building, training, and deploying neural networks while mastering fundamental AI/ML concepts through hands-on coding exercises and real-world applications.
Course Objectives
By completing this course, participants will be able to:
✅ Understand TensorFlow’s architecture and ecosystem
✅ Build and train various neural network architectures
✅ Implement computer vision and NLP solutions
✅ Optimize models for performance and accuracy
✅ Deploy TensorFlow models in production environments
Who Should Attend?
🟢 Software Developers entering AI/ML fields
🟢 Data Scientists expanding their toolkit
🟢 Engineering Students pursuing AI specializations
🟢 Technical Professionals transitioning to ML roles
(Prerequisites: Basic Python knowledge, familiarity with algebra/statistics)
Course Modules
Module 1: TensorFlow Fundamentals
- Introduction to TensorFlow 2.x ecosystem
• Tensors, variables, and automatic differentiation
• Building first computational graphs
• Hands-on: Linear regression implementation
Module 2: Core Machine Learning with TF
- Implementing classification models
• Training loops and callbacks
• Hyperparameter tuning with Keras Tuner
• Lab: Digit classification on MNIST dataset
Module 3: Deep Learning Architectures
- Dense networks and activation functions
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs/LSTMs)
• Workshop: Image recognition project
Module 4: Advanced Model Development
- Transfer learning with pre-trained models
• Custom layer creation
• Multi-input/output architectures
• Case study: Real-world TF pipeline
Module 5: Model Optimization & Deployment
- Quantization and pruning techniques
• TF Serving for model deployment
• Converting models for mobile (TFLite)
• Capstone: End-to-end model deployment