fast.CNTK Simple and fast.TensorFlow uses only CUDNN v2, but even so its performance is still 1.5 times times slower than V2 with CUDNN torch, and there is a memory overflow problem in training googlenet when the batch size is 128.Theano performance on large networks is comparable to TORCH7. But its main problem is that the boot time is particularly long because it needs to compile the C/cuda code into binary, and TensorFlow does not have this problem. In addition, the import of Theano consumes
protected] .Caffe Simple and fast.CNTK Simple and fast.TensorFlow uses only CUDNN v2, but even so its performance is still 1.5 times times slower than V2 with CUDNN torch, and there is a memory overflow problem in training googlenet when the batch size is 128.Theano performance on large networks is comparable to TORCH7. But its main problem is that the boot time is particularly long because it needs to compile the C/cuda code into binary, and TensorFlow does not have this problem. In addition,
1979, in the book "Del, Escher, Bach-set the Big one". Hofstadter's PhD, Harry Foundalis, established an automated system to solve his doctoral research project, a system called "Phaeco". This program can not only solve bongard problem, but also is a kind of architecture of cognitive visual pattern recognition.
deep Learning and Bongard issues
The Phaeco created in 2006 is very influential because it not
Learning notes TF042: TF. Learn, distributed Estimator, deep learning Estimator, tf042estimator
TF. Learn, an important module of TensorFlow, various types of deep learning and popular machine learning algorithms. TensorFlow offic
Machine learning Types
Machine Learning Model Evaluation steps
Deep Learning data Preparation
Feature Engineering
Over fitting
General process for solving machine learning problems
Machine Learning Four Br
, feature processing, deep learning algorithm implementation, and so on.3.2 Screening of samplesData and features are the two most important aspects of machine learning, because they determine the upper limit of the entire model. Reviews recommended because of its own multi-service (including takeaway, merchant, group
get started. David Silver has also recently published a short article on deep-enhanced learning.
Deep Learning Framework : A lot of deep learning frameworks, the most famous three should be TensorFlow (Google), Torch (Facebo
Free and open source mobile deep The learning framework, deploying by Baidu.
This is the simply deploying CNN on mobile devices with the low complexity and the high speed. It supports calculation on the IOS GPU, and is already adopted by the Baidu APP.
size:340k+ (on ARM v7)Speed:40ms (for IOS Metal GPU mobilenet) or M
first, deep reinforcement learning of the bubbleIn 2015, DeepMind's Volodymyr Mnih and other researchers published papers in the journal Nature Human-level control through deep reinforcement learning[1], This paper presents a model deep q-network (DQN), which combines depth
experiment with the CPU and # CPU_ONLY := 1 remove the previous # number.
If you use the GPU and have cudnn acceleration , # USE_CUDNN := 1 Remove the previous # number.
If you use Openblas, it will be BLAS := atlas changed and BLAS := open added BLAS_INCLUDE := /usr/include/openblas (the default matrix operations library in Caffe is Atlas, but Openblas has some performance optimizations, so it is recommended to change Openblas)
Not to b
is worth mentioning that the middle layer added a lot of softmax classifier, to prevent overfitting, that is: When the inception network, the branches of the same output, in order to make full use of the neural network structure, in the middle layer is the output, the final comparison of the output results, In order to find the best output of the corresponding structure.
8.Using Open-source Implementation
We can look for existing open source files from GitHub in the process of training the r
: deep learning has made great progress in vision and speech, attributed to the ability to automatically extract high level features. The current reinforcement learning successfully combines the results of deep learning, that is, DQN, to get breakthrough on Atari games.Howev
0. OriginalDeep learning algorithms with applications to Video Analytics for A Smart city:a Survey1. Target DetectionThe goal of target detection is to pinpoint the location of the target in the image. Many work with deep learning algorithms has been proposed. We review the following representative work:SZEGEDY[28] modified the
HTMS by Jeff Hawkins: "continuous online sequence learning with an unsupervised neural network model"? [arxiv]
Word2vec: "Efficient estimation of Word representations in Vector Space" [arxiv, Google code]
"Feedforward sequential Memory networks:a New Structure to learn long-term Dependency" [arxiv]
Framework Benchmarks
"Comparative Study of Caffe, Neon, Theano and Torch for deep
1. Preface
In the process of learning deep learning, the main reference is four documents: the University of Taiwan's machine learning skills open course; Andrew ng's deep learning tutorial; Li Feifei's CNN tutorial; Caffe's offi
involves an algorithm that is useful for estimating subspaces. The ICA model can be introduced .)
[11] Xiaomei Qu. Feature Extraction by combining independent subspaces analysis and von techniques. International Conference on System ScienceAnd engineering, 2012.
[12] Pietro berkes, Frank Wood and Jonathan pillow. characterizing neural dependencies with the copo models. In nips, 2008.
[13] Y-lan boureau, Jean Ponce, Yann lecun. A Theoretical Analysis of feature pooling in visual recognition. In
multiple languages, such as Python, R, and Julia. Mxnet also comes with a series of neural network guides and blueprints. It is also noteworthy that a related project uses JavaScript to implement mxnet in a browser environment where interested friends can test a graphics classification model.
6. Qix
This is a library of GitHub versions of various computing and programming topics related to resources, including Node.js, Golang, and depth learning. The
Deep understanding of the pthread thread in multi-thread programmingPthreadPOSIX threads is short for POSIXThread Standard.The first few blogs have provided you with a preliminary multi-thread concept. Before learning more about thread synchronization and other multi-thread
This paper describes how to apply the deep learning-based target detection algorithm to the specific project development, which embodies the value of deep learning technology in actual production, and is considered as a landing realization of AI algorithm. The algorithms section of this article can be found in the prev
/* 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
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