Discover How to Build Your Own Neural Network From Scratch…Even if You’ve Got Zero Math or Coding Skills!
What seemed like a lame and unbelievable sci-fi movie a few decades ago is now a reality. Machines can finally think. Maybe not quite as complex as the human brain, but more than enough to make everyone’s life a lot easier.
Artificial neural networks, based on the neurons found in the human brain give machines a ‘brain’. Patterned just like biological neurons, these software or hardware are a variety of the deep learning technology. With their help you can make your computer learn by feeding it data, which will then be generated as the output you desire. It is they to thank for the nanoseconds in which computers operate.
It may be science, but it is not actually rocket science. Everyone can learn how to take advantage of the progressed technology of today, get inside the ‘brain’ of the computers, and train them to perform the desired operations. They have been used in many different industries, and you can rest assured that you will find the perfect purpose for your own neural network.
The best part about this book is that it doesn’t require a college degree. Your high school math skills are quite enough for you to get a good grasp of the basics and learn how to build an artificial neural network. From non-mathematical explanations to teaching you the basic math behind the ANNs and training you how to actually program one, this book is the most helpful guide you will ever find. Carefully designed for you, the beginner, this guide will help you become a proud owner of a neural network in no time.
Here’s a Sneak Peak to What You’ll Discover Inside this Book:
- The 6 unique benefits of neural networks
- The difference between biological and artificial neural networks
- And inside look into ANN (Artificial Neural Networks)
- The industries ANN is used in
- How to teach neural networks to perform specific commands
- The different types of learning modalities (e.g. Hebbian Learning, unsupervised learning, supervised learning etc.)
- The architecture of ANN
- Basic math behind artificial neurons
- Simple networks for pattern classification
- The Hebb Rule
- How to build a simple neural network code
- The backpropogation algorithm and how to program it
- And much, much more!
There’s a lot more inside this book we’ll cover, so be prepared. I’ve made to lucidly explain everything I cover so that there’s zero confusion!
