read 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.
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 uniueness This section contains the following eight chaptersBuilding 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. This book is described as an in depth introduction to neural networks which defines neural networks describes how they work and explains how to build one While it certainly covers all these topics I found this book to be very difficult overall I do not have a background in Python or higher level math and while I could grasp the basic concepts I was honestly very lost for a good portion of the bookAt the same time I did appreciate the author s attempts to make neural networks understandable to a lay audience Neural networks are becoming and relevant and it is definitely worthwhile to have some grasp of what they are and how they work I think this book is best for people who have a background in a programming language like Python and a strong foundation in algebra statistics and calculus Since I do not have this background I would prefer to learn about the general concept of a neural network and less of the mathematical and programming aspects of them Overall it was an interesting and informative book but I personally found it too advanced to be a beginner s guide
summary Ì eBook or Kindle ePUB ↠ Michael Taylor, Mark Koning
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 Lets beginNeural Network Distinguish HandwritingWe are going to dig deep with Tensorflow and build a neural network that can distinguish between handwritten numbers Well use the same 5 steps we covered in the high level overview and we are going to take time exploring each line of codeNeural Network Classify Images10 minutes Thats 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 Googles 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 skys the limit. Excellent book to start with Clearly explained even the math with easy to follow diagrams
Michael Taylor, Mark Koning ↠ 3 read
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 TensorflowWhat you will gain from this book A deep understanding of how a Neural Network works How to build a Neural Network from scratch using PythonWho 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 networksWhats Inside Make Your Own Neural Network An Indepth Visual Introduction For BeginnersWhat Is a Neural NetworkNeural 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 learningwe gently explore these topics so that we can be prepared to dive deep further on To start well begin with a high level overview of machine learning and then drill down into the specifics of a neural networkThe Math of Neural NetworksOn a high level a network learns just like we do. This is an excellent book which covers a complex topic such as Neural Networks I started reading this book with no knowledge of Neural networks but after reading it I can say that I do understand the principles of Neural Networks a second or 3rd read and I can be a probationerdeveloperresearcher yes this book is comprehensive on the theory practicals tools and processes to get anyone started off on Neural NetworksFirst of all let us understand the Target Audience this book is a lot of math it will work even if you have a good background in Math that is enough to understand the concepts and understand but if you are a totally from a non math background this is not for you I graduated about 22 years back and totally out of touch with theory but I was able to understand it so some backgroundconcept is necessary but if you are really good in math this book really goes deep into itThe author does a great job in simplifying the concepts example a partial derivative is beautifully explained as enables you a measure how a single variable out of many impacts another single variable and chain rule is explained as Discovering the error of the specific weight is an important aspect of training the networksWhen I first read the first few chapters of the book it felt that this was going nowhere there were concepts around nodes weights error some of which I was able to understand and some I could not So but it really all came together when I was on the practical example a neural network which can read a image and determine if it is a chicken or a man It explains a simple 64 pixel image each pixel contains a number which represents the color and based on the color we should arrive at 0 for chicken and 1 for man And how we can keep on adjusting weights until we arrive at the right answer and minimize errorEssentially the image is reduced to a single number and that single number is derived by assigning weights assigned to each pixel to me this was poetic and achieved my NNN Neural Network Nirvana 33000 feet above ground while I read this book on my way back from on an international trip