best deep learning book

natural to think that we can use convolution to solve this problem.(iv) The model of deep learning to buildQuestion: Since we want to use a deep learning model, then how do we let the model identify our initial data.We can do this:1, each sentence is convolution into a vector, using this vector to find the distanceLik

Deep Learning thesis note (7) Deep network high-level feature Visualization Zouxy09@qq.com Http://blog.csdn.net/zouxy09 I have read some papers at ordinary times, but I always feel that I will slowly forget it after reading it. I did not seem to have read it again one day. So I want to sum up some useful knowledge points in my thesis. On the one hand, my underst

He admired the bronze teacher for a long time, and when he learned that he had written a book on learning methods, "The art of deep learning", he bought the first ebook I paid for in my life on the Amazon China website.This reading note is not exactly in accordance with the original

One of the best tutorials to learn lstm is deep learning tutorial See http://deeplearning.net/tutorial/lstm.html The sentiment analysis here is actually a bit like Topic classification First learn to enter data format, run the whole process again, the data is also very simple, from the idbm download of the film review data, 50,000 annotated data, plus and minus half, 5,000 no annotated data, each film no mo

Entry route1, first of all on their own computer to install an open source framework, like TensorFlow, Caffe such, play this framework, the framework to use2, and then run some basic network, from the3, if there are conditions, the entire GPU computer, GPU run a lot faster, compared to the CPU To be more specific, I think you can follow these steps to learn it:First phase:1, realize and train only one layer of Softmax regression model for handwritten digital image classification;2, the implemen

This section describes how to use building deep networks for classification in http://deeplearning.stanford.edu/wiki/index.php/ufldl_tutorial.pdf. Divided into the following two parts: 　　1. From Self-taught to deep networks: From the previous introduction to self-taught Learning (Deep

"Abbreviation Mlapp, is also I study machine study of the first book, is a chatty of books. can help beginners to quickly build a complete framework of machine learning content, to avoid falling into such specific algorithms as logistic regression, support vector machine, trees trees. However, due to space constraints, many chapters of the discussion is relatively simple, such as probability map model, Gaus

Deep learning and shallow learningAs the deep learning now in full swing, in various fields gradually occupy the status of State-of-the-art, last semester in a course project in the deep learning the effect, Recently, when I was d

Deep Learning (depth learning) Learning notes finishing Series[Email protected]Http://blog.csdn.net/zouxy09ZouxyVersion 1.0 2013-04-08Statement:1) The Deep Learning Learning Series is a

This article is a summary of reading the Wide Deep Learning for Recommender Systems, which presents a combination of the Wide model and the DEEP model for the Promotion recommendation System (recommendation System) has a very important effect on performance. 1. Background This paper presents the wide Deep model, whic

Deep Learning (depth learning) Learning notes finishing Series[Email protected]Http://blog.csdn.net/zouxy09ZouxyVersion 1.0 2013-04-08Statement:1) The Deep Learning Learning Series is a

Here is a list of books which I had read and feel it was worth recommending to friends who was interested in computer Scie nCE.Machine Learningpattern recognition and machine learningChristopher M. BishopA new treatment of classic machine learning topics, such as classification, regression, and time series analysis from a Ba Yesian perspective. It is a must read for people who intends to perform in Bayesian learni