Deep Learning - Artificial Neural Networks with TensorFlow

In this module, we will introduce the author and provide an overview of the course's learning objectives and structure. We will discuss the approach taken in this course, the prerequisites needed, and provide a summary of the topics that will be covered throughout the course.

In this module, we will delve into the foundational concepts of machine learning and neural networks. We will begin by understanding what machine learning is and exploring linear classification and regression theories with TensorFlow 2.0. Through practical examples, you will learn how to apply these theories using real-world datasets. We will also cover the structure and function of neurons, the learning process of models, and how to make predictions.

In this module, we will delve into the world of feedforward artificial neural networks (ANNs). Starting with an introduction to ANNs, we will explore forward propagation and the geometrical significance of neural networks. We will cover various activation functions, multiclass classification, and the representation of image data.

In this module, we will dive deep into the crucial aspect of loss functions used in neural networks. We will start by understanding Mean Squared Error (MSE) from a probabilistic viewpoint, which is commonly used in regression tasks. Next, we will explore binary cross entropy and categorical cross entropy.

In this module, we will delve into the critical optimization technique of gradient descent and its variations. We will begin with an introduction to the fundamental concept of gradient descent, followed by an exploration of stochastic gradient descent and its advantages. You will learn about the role of momentum in accelerating convergence and the importance of variable and adaptive learning rates in optimization.