Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

"not a great hands-on book" according to Amazon Customer. Constantly assuming you have a solid background on almost all the concepts the author is trying to teach not a great hands-on book. Buy "Make Your Own Neural Network" for the first couple chapters, 1000% better explanation on both implementing neural nets and the math behind it. Disappointed.. "Good book, confusing support materials" according to consumer of goods. Very good intro to the ideas behind deep learning systems. I'm a beginner in this field, I'm still only part of the way through the text but I think I'll finish it and learn a lot.One issue right now, that could be easily solved, is that that using the accompanying source code at github can be frustrating. The book itself is not clear about exactly which version of Python and TensorFlow is required to run the examples. The downloaded code I tried so far uses Python 2.x, and a m. OK tutorial MsCurious I was looking forward to this for some time, hoping it would be a clean practical description of how to implement a basic deep network. This is more of an introductory tutorial on the basics that uses the TensorFlow library for illustrations.Though this isn't what I was looking for, I assume the objective was to product a good such tutorial. But it's written in a wordy manner, spends many pages reviewing basic machine learning, non-deep networks, and misc topics like reinforcem

Nikhil also has a passion for education, regularly writing technical posts on his blog, teaching machine learning tutorials at hackathons, and recently, received the Young Innovator Award from the Gordon and Betty Moore Foundation for re-invisioning the traditional chemistry set using augmented reality.. At age 19, he had a first author publication on using

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.Examine the foundations of machine learning and neural networksLearn how to train feed-forward neural networksUse TensorFlow to implement your first neural networkManage problems that arise as you begin to make networks deeperBuild neural networks that analyze complex imagesPerform effective dimensionality reduction using autoencode

At age 19, he had a first author publication on using protist models for high throughput drug screening using flow cytometry. About the AuthorNikhil Buduma is a computer science student at MIT with deep interests in machine learning and the biomedical sciences. Nikhil also has a passion for education, regularly writing technical posts on his blog, teaching machine learning tutorials at hackathons, and recently, received the Young Innovator Award from the Gordon and Betty Moore Foundation for re-invisioning the traditional chemistry set using augmented reality.. He is a two time gold medalis