Deep Learning with TensorFlow Online Course

Deep Learning with TensorFlow Online Course

This course teaches deep learning with TensorFlow 2, the industry-standard library by Google. You’ll start with machine learning fundamentals—classification and regression—then explore artificial neural networks and their inspiration from biological neural networks. The course covers key deep learning concepts such as loss functions (MSE, binary and categorical cross-entropy) and optimization techniques, including stochastic gradient descent, momentum, adaptive learning rates, and Adam. By the end, you’ll be able to build and train artificial neural networks using TensorFlow for real-world deep learning applications.

Who should take this Course?

The Deep Learning with TensorFlow Online Course is ideal for data scientists, AI enthusiasts, software developers, and researchers who want to build and deploy advanced neural network models. It is also suitable for students, professionals, and technology enthusiasts seeking hands-on experience in deep learning, computer vision, natural language processing, and other AI applications using TensorFlow.

What you will learn

• Understand what machine learning is
• Build linear models with TensorFlow 2
• Learn how to build deep neural networks with TensorFlow 2
• Learn how to perform image classification and regression with ANN
• Learn loss functions such as mean-squared error and cross-entropy loss
• Learn about stochastic gradient descent, momentum, and Adam optimization

Course Outline

Welcome

• Introduction
• Outline

Machine Learning and Neurons

• What is Machine Learning?
• Code Preparation (Classification Theory)
• Classification Notebook
• Code Preparation (Regression Theory)
• Regression Notebook
• The Neuron
• How Does a Model Learn?
• Making Predictions
• Saving and Loading a Model
• Why Keras?
• Suggestion Box

Feedforward Artificial Neural Networks

• Artificial Neural Networks Section Introduction
• Forward Propagation
• The Geometrical Picture
• Activation Functions
• Multiclass Classification
• How to Represent Images
• Code Preparation (Artificial Neural Networks)
• ANN for Image Classification
• ANN for Regression
• How to Choose Hyperparameters

In-Depth: Loss Functions

• Mean Squared Error
• Binary Cross Entropy
• Categorical Cross Entropy

In-Depth: Gradient Descent

• Gradient Descent
• Stochastic Gradient Descent
• Momentum
• Variable and Adaptive Learning Rates
• Adam Optimization (Part 1)
• Adam Optimization (Part 2)