Introduction to Neural Networks

Overview

The course "Introduction to Neural Networks" provides a comprehensive introduction to the foundational concepts of neural networks, equipping learners with essential skills in deep learning and machine learning. Dive into the mathematics that drive neural network algorithms and explore the optimization techniques that enhance their performance. Gain hands-on experience training machine learning models using gradient descent and evaluate their effectiveness in practical scenarios. You’ll also delve into the architecture of feedforward neural networks and the innovative techniques used to prevent overfitting, such as dropout and regularization. The course uniquely emphasizes Convolutional Neural Networks (CNNs), highlighting their applications in fields like computer vision and image processing. Real-world examples and research insights will help you stay current with advancements in neural networks while preparing you to propose innovative solutions for emerging challenges. This course offers the tools and knowledge to advance your expertise in algorithms and machine learning methodologies.

Syllabus

Overview and Foundations

This module will provide a comprehensive overview of the course and lay the foundations needed to be successful in the field of Deep Learning. It will also introduce motivation for the field and discuss the history of the field.

Learning in Neural Networks

This module will discuss the fundamentals of Machine Learning. You will explore different aspects of Machine Learning Algorithms and what is needed to create an algorithm.

Feedforward Neural Networks

This module will discuss the building blocks of Deep Feedforward Neural Networks. Students will explore different parts of Deep Feedforward NN and what is needed to create and train the algorithms.

Regularization in Neural Networks

This module will discuss the regularization in Deep Feedforward Neural Networks. Learners will explore the reasons for regularization along with different techniques.