Machine Learning with Neural Networks: An In-depth Visual Introduction with Python: Make Your Own Neural Network in Python: A Simple Guide on Machine Learning with Neural Networks. (English Edition) Versión Kindle de Michael Taylor


Machine Learning with Neural Networks: An In-depth Visual Introduction with Python: Make Your Own Neural Network in Python: A Simple Guide on Machine Learning with Neural Networks. (English Edition) Versión Kindle

de Michael Taylor
Genero : Algoritmos Informáticos

Book's Cover of Machine Learning with Neural Networks: An In-depth Visual Introduction with Python: Make Your Own Neural Network in Python: A Simple Guide on Machine Learning with Neural Networks. (English Edition) Versión Kindle

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Make Your Own Neural Network in PythonA step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow. What you will gain from this book: * A deep understanding of how a Neural Network works. * How to build a Neural Network from scratch using Python.Who this book is for: * Beginners who want to fully understand how networks work, and learn to build two step-by-step examples in Python. * Programmers who need an easy to read, but solid refresher, on the math of neural networks.What’s Inside - ‘Make Your Own Neural Network: An Indepth Visual Introduction For Beginners’What Is a Neural Network?Neural networks have made a gigantic comeback in the last few decades and you likely make use of them everyday without realizing it, but what exactly is a neural network? What is it used for and how does it fit within the broader arena of machine learning?we gently explore these topics so that we can be prepared to dive deep further on. To start, we’ll begin with a high-level overview of machine learning and then drill down into the specifics of a neural network.The Math of Neural NetworksOn a high level, a network learns just like we do, through trial and error. This is true regardless if the network is supervised, unsupervised, or semi-supervised. Once we dig a bit deeper though, we discover that a handful of mathematical functions play a major role in the trial and error process. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns. * Forward Propagation * Calculating The Total Error * Calculating The Gradients * Updating The WeightsMake Your Own Artificial Neural Network: Hands on ExampleYou will learn to build a simple neural network using all the concepts and functions we learned in the previous few chapters. Our example will be basic but hopefully very intuitive. Many examples available online are either hopelessly abstract or make use of the same data sets, which can be repetitive. Our goal is to be crystal clear and engaging, but with a touch of fun and uniqueness. This section contains the following eight chapters.Building Neural Networks in PythonThere are many ways to build a neural network and lots of tools to get the job done. This is fantastic, but it can also be overwhelming when you start, because there are so many tools to choose from. We are going to take a look at what tools are needed and help you nail down the essentials. To build a neural networkTensorflow and Neural NetworksThere is no single way to build a feedforward neural network with Python, and that is especially true if you throw Tensorflow into the mix. However, there is a general framework that exists that can be divided into five steps and grouped into two parts. We are going to briefly explore these five steps so that we are prepared to use them to build a network later on. Ready? Let’s begin.Neural Network: Distinguish HandwritingWe are going to dig deep with Tensorflow and build a neural network that can distinguish between handwritten numbers. We’ll use the same 5 steps we covered in the high-level overview, and we are going to take time exploring each line of code.Neural Network: Classify Images10 minutes. That’s all it takes to build an image classifier thanks to Google! We will provide a high-level overview of how to classify images using a convolutional neural network (CNN) and Google’s Inception V3 model. Once finished, you will be able to tweak this code to classify any type of image sets! Cats, bats, super heroes - the sky’s the limit.

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Good python and neural networking guide This makes a great intro to learning about Python, machine learning, and neural networks. It is written in a very easy to read and understandable way that even I could understand. I’m a complete beginner to programming and computer mechanics but am starting to learn more and more and am really interested in the field. I found this book super helpful, it cleared up all my questions and explained everything in such a clear, simple fashion. Some of the maths and stuff on gradients I found pretty confusing at first but after reading it through a couple of times I’ve now got the hang of them. If you’ve just started out using Python, and want to learn more about the maths behind neural networking then I’d strongly recommend this guide. It definitely helped me.
Great reference Fantastic reference and information. Covers the basics without overwhelming your thinking process.
Good place to start Excellent book to start with. Clearly explained, even the math, with easy to follow diagrams.