Building a Neural Network from Zero

Master Neural Networks by Building from Scratch: Forward/Backward Pass, SGD, and Fashion-MNIST Challenge

Description

Are you ready to take your understanding of neural networks to the next level? In “Building a Neural Network from Zero,” you’ll dive deep into the inner workings of neural networks by implementing everything from scratch. This course is perfect for those who want to go beyond using libraries and truly understand how each component functions under the hood.

In this hands-on course, we will manually construct a PyTorch-like framework to build, train, and evaluate neural networks. Starting from the fundamentals of numerical differentiation and gradient descent, you’ll gradually develop a complete training loop. You’ll gain in-depth knowledge of essential concepts, including:

• Numerical differentiation and three approaches to compute gradients

• Gradient descent in 2D and multi-dimensional spaces

• Stochastic Gradient Descent (SGD) with momentum

• Implementing cross-entropy loss and activation functions like Sigmoid

• Initializing neural network weights using He and Xavier methods

• Building a fully functional Feedforward Neural Network (FFNN) from scratch

By the end of the course, you’ll have a comprehensive understanding of how neural networks learn. To solidify your knowledge, we’ll tackle the Fashion-MNIST challenge, where you’ll apply your custom-built neural network to classify images accurately.

Whether you’re an aspiring machine learning engineer or a curious programmer, this course equips you with the foundational knowledge and hands-on experience to build and customize neural networks from the ground up.

Enroll today and start mastering neural networks by building them from scratch!

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