Neural Networks In Python From Scratch. Build step by step!

You will learn how to build Neural Networks with Python. Without the need for any library, you will see how a simple neural network from 4 lines of code, evolves into a artificial intelligence network that is able to recognize handwritten digits. During this process, you will learn concepts like: Feed forward, Cost functions, Back propagation, Hidden layers, Linear regression, Gradient descent and Matrix multiplication.

And all this with plain Python. Target audienceDevelopers who especially benefit from this course, are:Developer who want to learn the mechanics of neural networksDevelopers who want to avoid using neural network libraries and frameworksOr developers who use frameworks but want to learn the meaning of the individual network parametersChallengesMany tutorials claim to start from scratch, but import external libraries or rapidly type in code and before executing even once, you are looking at 50 lines of code. When finally the code is run, you are totally lost and still stuck trying to understand line 3.

This causes many students to give up learning Neural Networks. This course is different. It starts with the absolute beginning and each topic is a continuation of a previous example.

This way, you will learn neural networks from the ground up, step by step. What can you do after this course. You understand neural network concepts and ideas, like back propagation and gradient descent.