udemy deep learning

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

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

-bit integer. From -9223372036854775808 to 9223372036854775807. BinaryfieldStores binary data.BooleanfieldA field that stores true/false.CharfieldStores the string. Parameter max_length must exist.CommaseparatedintegerfieldIntegers separated by commasDatefieldStorage date, there are several additional parameters, Auto_now,auto_now_add.DatetimefiledStores the date and time.DecimalfieldStores decimals.EmailfieldA valid e-mail address value in the form of CharfieldFilefieldA field for uploading fil

insert for parent-child class:Deletion of file operations: Remove (): Delete this tab and the contents Empty (): The content is only the case, but the label is retained jquery Event Binding Supplemental delegate$ (' li '). Click (function () { //Method one}) $ (' Li '). On (' click ', Function () { //Method two})//jquery-based delegate binding $ (' TD '). ON (' Click ', '. Td-delete ', function () { $ ('. Remove,. Shade '). Removeclass (' Hide ')})Event Bindings:"More

ImportCopy3List1 = [1,2,3,[4,5]]4New_list1 =List15Shadow_copy_list1 =copy.copy (List1)6Deep_copy_list1 =copy.deepcopy (List1)7 #Modify the original object element: Change the ' 4 ' in the 4th element in the list to ' 7 '8List1[3][0] = 79 #Original listTen Print(List1)#[1, 2, 3, [7, 5]] One Print(ID (list1))#Address: 1975516434760 A #Assignment List - Print(NEW_LIST1)#[1, 2, 3, [7, 5]] - Print(ID (NEW_LIST1))#Address: 1975516434760 the #Shallow Copy list - Print(SHADOW_COPY_LIST1)#[1, 2, 3, [7,

Transferred from: http://baojie.org/blog/2013/01/27/deep-learning-tutorials/A few good deep learning tutorials, with basic videos and speeches. Two articles and a comic book are attached. There are some additions later.Jeff Dean @ StanfordHttp://i.stanford.edu/infoseminar/dean.pdfAn introductory introduction to what DL

is not fully connected, on the other hand, the weights of the connections between some neurons in the same layer are shared (that is, the same). Its incomplete connection and weight sharing network structure make it more similar to the biological neural network, which reduces the complexity of the network model (which is very important for the deep structure that is difficult to learn), and reduces the number of weights. Think back to the BP neural n

training, scale presents a problem for deep learning. The need to fully interconnect neurons, particularly in the upper layers, requires immense compute power. The first layer for an image-processing application could need to analyze a million pixels. The number of connections in the multiple layers of a deep network would be the orders of magnitude greater. "Th

The article was transferred from the deep learning public numberDeep learning is a new field in machine learning that is motivated by the establishment and simulation of a neural network for analytical learning of the human brain, which mimics the mechanisms of the human bra

1. Current situation:Deep learning is now very hot, and all kinds of meetings have to be stained with this point. Baidu Brain, Google's brain plan to engage in this. In some areas have achieved very good results, chip recognition, speech recognition, in the security field and even the identification of encryption protocols. The accuracy of the lab in the field of speech is over 90%.2. The essence of deep le