Learn to build neural networks from scratch in Python with this 3.5-hour course. By the end of the course, students will be able to understand the fundamentals of neural networks, code a neural network layer, implement activation functions, calculate loss using categorical cross-entropy, and build a complete neural network model. The course teaches essential skills such as coding neural network components, implementing activation functions, and calculating loss functions. The teaching method involves hands-on coding sessions and practical examples. This course is intended for individuals interested in understanding the inner workings of neural networks and gaining practical coding experience in Python.
Overview
Syllabus
Neural Networks from Scratch - P.1 Intro and Neuron Code.
Neural Networks from Scratch - P.2 Coding a Layer.
Neural Networks from Scratch - P.3 The Dot Product.
Neural Networks from Scratch - P.4 Batches, Layers, and Objects.
Neural Networks from Scratch - P.5 Hidden Layer Activation Functions.
Neural Networks from Scratch - P.6 Softmax Activation.
Neural Networks from Scratch - P.7 Calculating Loss with Categorical Cross-Entropy.
Neural Networks from Scratch - P.8 Implementing Loss.
Taught by
sentdex
