deep learning goodfellow

Course Description: This is an introductory course on deep learning, and deep learning is mainly used for machine translation, image recognition, games, image generation and more. The course also has two very interesting practical projects: (1) Generate music based on RNN (2) Basic X-ray detection, GitHub address: Http

The 2016 is a very important historical node, signifying that the AI system of unity of knowledge and line will go to the historical stage. It changes not only the next go, it will change a lot of things. --KaiyuOn the "Adas and autonomous Driving Trends forum" of the "2016 Smart cars and Shanghai Forum", Dr. Kaiyu, founder and CEO of Horizon Robotics, delivered a keynote speech entitled "The road to autonomous driving based on deep

The theme report of "Transfer model of deep learning" shorthand and commentary (iv) Bai Chu of the Red bean Family concern 2017.11.04 22:33* 3275 reading 141 comments 0 like 0 The author presses: machine learning is moving towards a new era of interpretive models based on "semantics". Migration learning is likely to ta

The author Matthew May is a computer postgraduate in parallel machine learning algorithms, and Matthew is also a data mining learner, a data enthusiast, and a dedicated machine-learning scientist. Open source tools play an increasingly important role in data science workflows. GitHub Ten deep learning projects, which i

implementation in Toolbox is very simple:In the NNTRAIN.M:batch_x = batch_x.* (rand (Size (batch_x)) >nn.inputzeromaskedfraction)That is, the size of the (nn.inputzeromaskedfraction) part of the X-0,denoising Autoencoder appears to be stronger than sparse autoencoderContractive auto-encoders:This variant is "Contractive auto-encoders:explicit invariance during feature extraction" proposedThis paper also summarizes a bit of autoencoder, it feels goodThe contractive autoencoders model is:whichThe

Structured Query language to manipulate database.for example:1. INSERT into Events VALUES ("rubyconf", 100); Insert a piece of data into the events table2. SELECT * from events; Take out all the dataTri ACID (4 properties)Transaction: A process of doing business. Package a set of actions to execute together.Use begin;...commit; it can guarantee the correctness of data access, either succeed together or fail together.Atomicity: A transaction is an atom.Consistency: Consistency ensures that the i

usually used only when there are a large number of annotated training data. In such cases, fine tuning can significantly improve the performance of the classifier. However, if there are a large number of unlabeled datasets (for unsupervised feature learning/pre-training), there are only relatively few annotated training sets, and the effect of fine tuning is very limited.The previously mentioned network is generally three layers, the following is a g

The 1th chapter introduces the course of deep learning, mainly introduces the application category of deep learning, the demand of talents and the main algorithms. This paper introduces the course chapters, the course arrangement, the applicable crowd, the prerequisites and the degree to be achieved after the completio

capabilities and work in areas where human experience is missing. In recent years, the use of intensive learning and training of the deep neural network has made rapid progress. These systems have surpassed the level of human players in video games, such as atari[6,7] and 3D virtual Games [8,9,10]. However, the most challenging areas of play in terms of human intelligence, such as Weiqi, are widely conside

variables, and the parent process has also seen this modification. The vfork function may occur because the fork of the early system did not implement the write-time replication technology, resulting in a lot of useless work in each fork call (in most cases, it is called exec to execute a new program after fork) the efficiency is not high, so the vfork function is created. The current implementation basically uses the write-time replication technology, and when the vfork function is used improp

1. Why add pooling (pooling) to the convolutional networkIf you only use convolutional operations to reduce the size of the feature map, you will lose a lot of information. So think of a way to reduce the volume of stride, leaving most of the information, through pooling to reduce the size of feature map.Advantages of pooling:1. Pooled operation does not increase parameters2. Experimental results show that the model with pooling is more accurateDisadvantages of pooling:1. Because the stride of t

CSS deep understanding of learning notes-margin and css learning notes-margin 1. margin and container size Element size: ① visible size clientWidth (standard); ② occupying size 　　 Margin and visual size: ① applicable to normal block elements without width/height; ② applicable only to horizontal dimension Margin and occupy size: ① block/inline-block horizontal ele

Deep understanding of CSS learning notes border and css learning notes 1. border-width Border-width does not support percentages: semantics and scenarios are determined. In reality, the concepts of borders do not support percentages. Border-width supports keywords: thin, medium (default), and thick. The values are 1px, 3px, and 5px (except IE7 ). Why is the defau

above. Move right to erase the non-0-bit to the right of the decimal points of the result. These non-0 bits are actually positive, but because they are erased, the result subtracts the values of the non-0 bits represented by the original negative result, and the final result is rounded down rather than rounded to 0. Floating point number: Standard for representing floating-point numbers and their operations: IEEE Standard 754. Floating-point numbers are normalized, non-nor

Deep learning veteran Yann LeCun detailed convolutional neural network The author of this article: Li Zun 2016-08-23 18:39 This article co-compiles: Blake, Ms Fenny Gao Lei Feng Net (public number: Lei Feng net) Note: convolutional Neural Networks (convolutional neural network) is a feedforward neural network, its artificial neurons can respond to a part of the coverage of the sur

Model optimization is important for both traditional machine learning and deep learning, especially in deep learning, and it is likely that more difficult challenges will need to be addressed during training. At present, the popular and widely used optimization algorithm has

Turn from deep learning public numberThis article is from: InfoQHttp://www.infoq.com/cn/news/2016/01/evaluation-comparison-deep-learnArtificial intelligence is undoubtedly the forefront of the computer world, and deep learning is undoubtedly the focus of artificial intellige