Overview
Explore TensorFlow through hands-on practice, completing an end-to-end tutorial to gain practical skills in building and deploying machine learning models.
Syllabus
-
- Introduction to TensorFlow
-- Overview of TensorFlow and its applications
-- Installation and setup
- Basic TensorFlow Concepts
-- Tensors and operations
-- Graphs and sessions (eager execution mode)
-- Data types, shapes, and broadcasting
- Building Machine Learning Models with TensorFlow
-- Loading and preprocessing data
-- Building a simple linear model
-- Implementing a feed-forward neural network
- Training and Optimization
-- Loss functions and optimization algorithms
-- Backpropagation and gradient descent
-- Monitoring training with TensorBoard
- Model Evaluation
-- Splitting data into training, validation, and test sets
-- Evaluating model performance
-- Understanding overfitting and regularization techniques
- Advanced TensorFlow Techniques
-- Working with datasets and data pipelines
-- Using tf.data for efficient data loading
-- Implementing callbacks for training control
- Deploying TensorFlow Models
-- Saving and loading models
-- Exporting models for deployment
-- Basics of TensorFlow Serving and TensorFlow Lite
- Hands-on Project: End-to-End Model Development
-- Project outline and dataset introduction
-- Building, training, and evaluating a custom model
-- Deploying the model and making predictions
- Conclusion and Next Steps
-- Recap of key concepts
-- Resources for further learning
-- Q&A session and final project showcase
Taught by
Tags
On-Demand
