Foundations of Deep Learning and Neural Networks

In this module, we will introduce the basic concepts of deep learning and neural networks. We will explore the history, fundamental structures like perceptrons, and the process of training neural networks. Additionally, we'll cover important concepts such as activation functions and representations.

In this module, we will delve into the intricacies of artificial neural networks. We'll explore how the human brain inspires these networks, the detailed workings of perceptrons, and the layers that constitute neural networks. Additionally, we'll cover the sigmoid function and understanding MNIST data.

In this module, we will focus on feed-forward networks, their operation modes, and the dimensions involved. We'll break down the pseudocode required for batch processing and introduce vectorized methods to optimize neural network training.

In this module, we will dive deep into backpropagation, a crucial method for training neural networks. We'll introduce the loss function, break down the backpropagation process into multiple parts, and cover associated concepts such as the sigmoid function and stochastic gradient descent (SGD).

In this module, we will cover regularization techniques to enhance neural network performance. We'll explore dropout methods, batch normalization in multiple parts, and introduce tools like TensorFlow and Keras that facilitate these processes.

In this module, we will explore Convolutional Neural Networks (CNNs) and their applications. We'll discuss the ideas behind CNNs, analyze how they process image and video data, and implement essential operations like convolution, stride, padding, and pooling. We'll also cover combining networks for complex tasks.