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Build Neural Networks In Python From Scratch. Step By Step!

Build Neural Networks In Python From Scratch. Step By Step!
Build Neural Networks In Python From Scratch. Step By Step!

Build Neural Networks In Python From Scratch. Step By Step!

Machine learning concepts like linear regression, gradient descent, and deep learning may be learned without the need for any pre-existing software frameworks.

What you’ll learn

Build Neural Networks In Python From Scratch. Step By Step!

  • Understand the concepts that underpin neural networks.
  • Learn how to create neural networks using basic Python.
  • Step by step, you will learn principles like feedforward, backward propagation, gradient descent, and regression.
  • Learn how to estimate complicated non-linear prediction functions using Softmax, ReLU, and Sigmoid.
  • Recognize that neural networks are not magical and may be built in any language without the need for libraries.

Requirements

  • You’re fascinated with neural networks.
  • You’ve worked with Python or another programming language before.
  • This course will not include any exercises. Feel free to copy and paste the code samples into your own documents.

Description

I’m the author of Wunderlist for Windows, Microsoft To-Do, and Windows Mahjong, and I like teaching software programming!

This course will teach you how to create neural networks using just Python. You will witness how a basic neural network with just four lines of code turns into a network that can recognize handwritten numbers without the need for any libraries. Feedforward, cost, backpropagation, hidden layers, linear regression, gradient descent, and matrix multiplication are some of the topics you’ll study in this process.

Every topic follows from the one before it. This method will teach you how to build neural networks from the ground up.

What distinguishes this course from others?

Many courses promise to start from scratch, yet after importing external libraries or quickly typing in code, you’re staring at 50 lines of code before even running it. When the code is finally run, you’re still trying to figure out what line 3 means.

My objective is to start from the very beginning. Before we can achieve our objective, we must take a number of measures. That, however, does not have to be an issue if we take things one step at a time!

My students tell me that after taking this course, they have a better understanding of how neural networks function. They built neural networks from the ground up after learning the code.

Who this course is for:

  • Developers who wish to learn how to design neural networks from the ground up without relying on libraries.

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