deep learning coursera github

/* author:cyh_24 *//* date:2014.10.2 *//* Email: [Email protected] *//* more:http://blog.csdn.net/cyh_24 */Recently, the focus of the study in the image of this piece of content, the recent game more, in order not to drag the hind legs too much, decided to study deeplearning, mainly in Theano the official course deep Learning tutorial for reference.This series of blog should be continuously updated, I hope

Before I have been using Theano, the previous five deeplearning related articles are also learning Theano some notes, at that time already feel Theano use up a little trouble, sometimes want to achieve a new structure, it will take a lot of time to programming, so think about the code modularity, Easy to reuse, but because it's too busy to do it. Recently discovered a framework called Keras, which coincides with my ideas, is particularly simple to use

This article is a basic learning blog from the University of Paris, PhD Hadrien Jean, which aims to help beginners/Advanced Beginners Master the concept of linear algebra based on deep learning and machine learning. Mastering these skills can improve your ability to understand and apply a variety of data science algori

the difficulty of getting started with deep learning algorithms .. in other words, it is a silly call. In fact, almost all of them are used. I remember that this year's Deep Learning summit in London had a great opportunity to get a class comparison chart of the python library (searching for the source image...). I c

Original English: Teach Yourself Deep Learning with TensorFlow Author: Vincent Vanhoucke,google chief Scientist. Translation: Guokai Han. In recent years, deep learning has become one of the hottest topics in the field of machine learning. TensorFlow, as an open source proje

Learning Goals Understand multiple foundational papers of convolutional neural networks Analyze the dimensionality reduction of a volume in a very deep network Understand and Implement a residual network Build a deep neural network using Keras Implement a skip-connection in your network Clone a repository from

; Stores the running results of Session sessions std::vector When the C + + program is written, the compile time need to link the header file, open source has helped us tidy up, stored in the directory/usr/lib/python2.7/site-packages/tensorflow/include. When compiling and running, you need to link libtensorflow_cc.so, you can compile the library file as follows: Bazel build-c opt//tensorflow:libtensorflow_cc.so--copt=-m64- linkopt=-m64--spawn_strategy=standalone--genrule_strategy=standalo

Preface CIFAR-10 datasets are a common data set in the field of deep learning. The Cifar-10 consists of 60000 32*32 RGB color images, all of which include aircraft, cars, birds, fur, deer, dogs, frogs, horses, boats and trucks in 10 categories. 50000 training, 10000 tests. is often used as a classification task to evaluate the merits and demerits of deep

Original address: https://www.zhihu.com/question/27982282 Gein Chen's answer many thanks —————————————————————————————————————————— 1. The first step of learning the program, first let the program run, see the results, so that there will be an intuitive feeling.Caffe's official Online Caffe | The Deep learning Framework provides a lot of examples, and you can eas

mainstream framework, of course, not to say that Keras and CNTK are not mainstream, the article does not have any interest related things, but the keras itself has a variety of frameworks as the back end, So there is no point in contrast to its back-end frame, Keras is undoubtedly the slowest. and CNTK because the author of Windows is not feeling so also not within the range of evaluation (CNTK is also a good framework, of course, also cross-platform, interested parties can go to trample on the

including StackOverflow, GitHub above Or not, then refer to another deep learning environment tutorial, which is mentioned in the reference tutorial of the second, so entered the right now, and then installed successfully.(2) Then continue to follow Installation guide and go to the directory where you downloaded the package:tar -xzvf cudnn-9.0-linux-x64-cuda/inc

Logistic classification GitHub Project Address: https://github.com/ahangchen/GDLnotesWelcome to star, you can discuss it in issue area.Official Tutorial AddressVideo/subtitle Download About Simple but important classifier Train your first simple model entirely end to end Download, preprocess some pictures to classify Run an actual logistic classifier on images data Connect bit of math and code Det

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