Understand linear regression, gradient descent and many more by building neural networks without libraries or frameworks
What you'll learn
Understand the ideas behind neural networks.
Learn how to use plain Python to create neural networks.
Learn concepts like feed forward, backward propagation, gradient descent, regression step by step.
Understand how Softmax, ReLU and Sigmoid allow you to approximate complex non-linear prediction functions.
Realise that neural networks are not magic and can be implemented without using libraries, in any language you desire.
Requirements
You have an interest in neural networks.
You have some programming experience in Python or another language.
There will be no exercises in this course. Feel free to write along with the code examples.
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