Learn Neural Networks By Deriving And Coding One From Scratch In Python - Full Course (~2 hrs)
In this course, I explain the fundamentals of neural networks. I explain how they are derived and how they work. The course is motivated by a problem - building a machine to predict hand-written digits from the MNIST dataset. I start with a very simple linear binary classifier (the Perceptron) and with each lecture I improve the model until I have a Deep Neural Network.
All the code is written in Python and is hosted on GitHub (link below). I make use of NumPy and a tiny bit of Pandas and Matplotlib. No neural network libraries (e.g. TensorFlow and PyTorch) are used in this course, as it is about understanding fundamentals - not practical construction.
0:00 - Promo
2:01 - 1.1 Introduction
3:50 - 2.1 MNIST Dataset
7:10 - 2.2 Perceptron Model
21:26 - 2.3 Perceptron Learning Algorithm
35:24 - 2.4 Pocket Algorithm
40:20 - 2.5 Multiclass Support
46:56 - 2.6 Perceptron To Neural Network
58:44 - 3.1 Simple Images
1:00:42 - 3.2 Random Weights
1:13:32 - 3.3 Gradient Descent (Backpropagation)
1:41:13 - 3.4 Multiclass Support
1:53:50 - 3.5 Deep Learning
2:01:00 - 3.6 Stochastic Gradient Descent
2:08:13 - 3.7 Going Further
-- Code -----------------------
https://github.com/ben519/nnets-for-y...
-- Vids & Playlists ---------------------------------
Google Colab - • Introduction to Google Colab
NumPy - • NumPy Explained - Full Course (3 Hrs)
Pandas - • Python Pandas - Comprehensive Course ...
Neural Networks - • Learn Neural Networks By Deriving And...
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