Mastering Neural Networks and Model Regularization

In this module, you will learn about the fundamental concepts in neural networks, covering the perceptron model, model parameters, and the back-propagation algorithm. You'll also learn to implement a neural network from scratch and apply it to classify MNIST images, evaluating performance against sklearn's library function.

In this module, you'll delve into techniques to enhance machine learning model performance and generalization. You'll grasp the necessity of regularization to mitigate overfitting, compare L1 and L2 regularization methods, understand decision tree pruning, explore dropout regularization in neural networks, and observe how regularization shapes model decision boundaries.

In this module, you'll cover essential concepts and practical skills in deep learning using PyTorch. You'll also learn computational graphs in supervised learning, create and manipulate tensors in PyTorch, compare activation and loss functions, learn implementation steps and library functions for neural network training, and optimize models by running them on GPU for enhanced performance.

In this module, you'll focus on advanced applications of convolutional neural networks (CNNs) using PyTorch. You'll also learn to implement CNN filters, compare different CNN architectures, develop models for image processing tasks in PyTorch, and explore techniques for modeling audio time signals using Spectrogram features for enhanced analysis and classification.