Mastering Neural Networks: From Zero to Fashion-MNIST Hero

What you will learn:

Construct and implement neural networks from scratch.
Master gradient descent, SGD with momentum, and other optimization methods.
Develop custom layers, activation functions, and loss functions without relying on external libraries.
Successfully apply your custom-built neural network to the challenging Fashion-MNIST dataset.

Description

Ready to conquer the world of neural networks? Join our comprehensive course and unlock the secrets of deep learning by building your own neural network from the ground up! Forget relying on pre-built libraries; this course empowers you to master the fundamental concepts and algorithms that drive these powerful systems.

This immersive journey starts with a solid foundation in numerical differentiation and gradient descent. You'll learn three distinct methods for calculating gradients, navigate the intricacies of gradient descent in both 2D and multi-dimensional spaces, and harness the power of Stochastic Gradient Descent (SGD) with momentum to optimize your models. We'll delve into crucial elements like implementing cross-entropy loss, activating your neurons with Sigmoid functions, and expertly initializing weights using He and Xavier methods.

You will build a complete, fully functional Feedforward Neural Network (FFNN) step-by-step, understanding every line of code and its impact. We'll then put your newly acquired skills to the test by tackling the challenging Fashion-MNIST image classification problem—a thrilling culmination of your learning journey. Prepare to not only grasp the theoretical underpinnings but also gain the practical experience necessary to build, train, and evaluate neural networks confidently.

Whether you're an aspiring AI engineer or a curious programmer, this course provides the perfect blend of theoretical knowledge and hands-on application. Enroll now and transform your understanding of neural networks – one line of code at a time!

Curriculum

Fundamental Deep Learning Principles

This foundational section begins with an introduction, then delves into the core concepts of numerical differentiation, exploring three different approaches for calculation. We then move to understanding gradient descent in both 2D and multi-dimensional spaces, followed by an in-depth exploration of Stochastic Gradient Descent (SGD) with momentum. Finally, this section concludes by covering essential aspects like cross-entropy loss, Sigmoid activation function implementation, and various weight initialization techniques (He/Xavier).

Building Your Neural Network

Building upon the foundational knowledge, this section guides you through the construction of a fully functional feedforward neural network. You'll learn to build linear layers step-by-step, implementing each layer with a thorough understanding. This is followed by the creation of the complete feed-forward network structure. We cover the implementation of SGD optimization for training, culminating in the thrilling challenge of applying your custom network to classify images in the Fashion-MNIST dataset.

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