Overview

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The course "Mastering Neural Networks and Model Regularization" dives deep into the fundamentals and advanced techniques of neural networks, from understanding perceptron-based models to implementing cutting-edge convolutional neural networks (CNNs). This course offers hands-on experience with real-world datasets, such as MNIST, and focuses on practical applications using the PyTorch framework. Learners will explore key regularization techniques like L1, L2, and drop-out to reduce model overfitting, as well as decision tree pruning. What makes this course unique is its emphasis on building neural networks from scratch, allowing learners to grasp the intricate details of model design and training. Additionally, the course covers computational graphs, activation and loss functions, and how to efficiently utilize GPUs for faster computation. Learners will also delve into CNNs for image and audio processing, gaining insights into cutting-edge applications in these fields. By completing this course, learners will develop advanced skills in neural network design, model regularization, and the use of PyTorch for deep learning tasks—empowering them to tackle complex machine learning challenges with confidence.

Syllabus

Course Introduction

This course provides a comprehensive introduction to neural networks, focusing on the perceptron model, regularization techniques, and practical implementation using PyTorch. Students will build and evaluate neural networks, including convolutional architectures for image processing and audio signal modeling. Emphasis will be placed on comparing performance metrics and understanding advanced concepts like computational graphs and loss functions. By the end of the course, participants will be equipped with the skills to effectively design, implement, and optimize neural network models.

Multilayer Artificial Neural Networks

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.

Model Regularization

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.

PyTorch

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.

Convolutional Neural Networks

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.