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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. Additionally, we will demonstrate how to save and load models, discuss the use of Keras, and gather feedback for continuous improvement.

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. You will gain hands-on experience by preparing code for ANN using the MNIST dataset, and applying ANN techniques for both image classification and regression tasks. Finally, we will discuss strategies for choosing the optimal hyperparameters for your neural networks.

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, the appropriate loss function for binary classification problems. Finally, we will examine categorical cross entropy, essential for multiclass classification scenarios. Additionally, we will differentiate between various types of loss functions and their specific applications, analyze how these loss functions impact model training and performance, and learn how to apply the correct loss functions based on the nature of the classification or regression problem. This detailed study will enhance your understanding of how different loss functions impact model performance and guide you in selecting the right one for your specific tasks.

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. We will then cover the basics of Adam optimization, one of the most popular optimization algorithms, and conclude with a deeper exploration of its advanced aspects. This comprehensive study will equip you with a thorough understanding of gradient descent and its variations, essential for training effective neural networks.