What you would learn in Python neural networks from scratch. Build them step by step course?
I'm Loek van der Ouweland, an experienced software engineer with over 25 decades of expertise. I'm the author of Wunderlist for Windows, Microsoft To-do, and Mahjong for Windows, and I enjoy imparting knowledge about software engineering!
You'll learn to create Neural Networks with plain Python in this class. With no need for any library, You will be able to see how a primary neural network, built created from just four lines of code, evolves into a neural network capable of recognizing handwritten numbers. In this course, you will be taught concepts such as Feedforward, Backpropagation, Cost Hidden layers, Linear regression gradient descent, and Multiplication of the matrix.
The subjects of the course include:
Each topic is an extension of a prior instance. In this way, you'll be able to learn about neural networks from the beginning.
What makes this course different from other courses?
Many tutorials promise to begin from scratch; however, you can import libraries from other sources or enter code, and before starting even one time, you're looking at fifty lines. After the code has been executed, you're lost and stuck trying to comprehend the 3rd line.
My goal is to start from the beginning. This is a clean Python file and has no libraries to import. There are many things to do before we can reach our final goal. But this doesn't need to be a problem so long as we do these steps one at a time!
Course Content:
- Learn about the concepts of neural networks.
- Learn to make use of plain Python to build neural networks.
- Learn about concepts such as feed-forward, the backward propagation process, gradient descent, and regression step-by-step.
- Learn the ways Softmax, ReLU and Sigmoid enable you to approximate complicated non-linear prediction algorithms.
- Be aware that neural networks aren't magical and can be built without libraries and in any language you want.
Download Python neural networks from scratch. Build them step by step from below links NOW!
