This paper summarizes some contents from the 1th chapter of Neural Networks and deep learning. Catalogue
Perceptual device
S-type neurons
The architecture of the neural network
Using neural networks to recognize handwritten numbers
Towards Deep learning
Perceptron (perceptrons)1. Fundament
Recently studied a few days of deep learning of the MATLAB Toolbox code, found that the author gives the source of the comments is very poor, in order to facilitate everyone to read, the code has been commented, share with you.Before reading the MATLAB Toolbox code, we recommend that you read a few CNN two classic materials, the convolutional neural Network MATLAB Toolbox Code understanding is very helpful,
the first week after-school assignment is a 10-course choice question
Note: The answer is from the first one and then the ABCD ... The answer has its own understanding, there are also from the online blog reference, only to learn.1. First questionI understand the answer: D.Reference answer: A. "AI is the new power", this is the topic of Wunda Teacher's speech on AI conference this year. Of course, the analogy is that AI, like electricity 100 years ago, is bringing great changes to our productiv
to be personal, but it's easy to look at SAS help. The PDV mechanism of SAS and the execution mechanism of macros must be understood. SAS has a great advantage, the standard of unification, as long as the learning to be able to swim throughout the system. R VS python: In contrast, R is statistically much stronger than Python because Statsmodel does not give force, and new statistical methods Python cannot keep pace. In the area of data mining, Pytho
since the beginning of the 2016, the use of neural networks and deep learning Alphago to win the Master of Human go, deep learning is also considered to be the closest machine learning approach to AI. from the current development trend of the global AI, the
/* 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
Deep learning is a prominent topic in the AI field. it has been around for a long time. It has received much attention because it has made breakthroughs beyond human capabilities in computer vision (ComputerVision) and AlphaGO. Since the last investigation, attention to deep learning has increased significantly.
When does the deep learning model in NLP need a tree structure?Some time ago read Jiwei Li et al and others [1] in EMNLP2015 published the paper "When is the Tree structures necessary for the deep learning of representations?", This paper mainly compares the recursive neural network based on tree structure (Recursive n
Write in front:has not tidied up the habit, causes many things to be forgotten, misses. Take this opportunity to develop a habit.Make a collation of the existing things, record, to explore and share new things.So the main content of the blog for I have done, the study of the collation of records and new algorithms, network framework of learning. It's basically about deep
closer to the real neuron activation model. Bridging the gap with pre-training 2 about pre-training in deep learning 2.1 Why pre-training
Deep networking has the following drawbacks: The deeper the network, the more training samples are needed. If the use of supervision will require a large number of samples, or small-scale samples can easily lead to overfitting
, momentum=0.9, decay=0.0, Nesterov=false)
model.fit (train_set_x, train_set_y, validation_split=0.1, nb_epoch=200, batch_size=256, Callbacks=[lrate])
The above code is to make the learning Rate index drop, as shown in the following figure:
Of course, can also directly modify the parameters in the SGD declaration function to directly modify the learning rate, learning
. You'll need to the know how-to-use this functions for future assignments. 1.1-sigmoid function, Np.exp ()
Before using Np.exp (), you'll use MATH.EXP () to implement the Sigmoid function. You'll then why Np.exp () is preferable to Math.exp ().
Exercise: Build a function that returns the sigmoid's a real number X. Use MATH.EXP (x) for the exponential funct Ion.
Reminder:Sigmoid (x) =11+e−x sigmoid (x) = \frac{1}{1+e^{-x} is sometimes also known as the The logistic function. It is a non-linear f
This section begins the Basic theory system learning phase of machine learning and deep learning, and the blog content is the notes that are collated during the learning process.1. Machine learningConcept: Multi-disciplinary interdisciplinary, involving probability theory, s
How Yahoo implements large-scale distributed deep learning on Hadoop Clusters
Over the past decade, Yahoo has invested a lot of energy in the construction and expansion of Apache Hadoop clusters. Currently, Yahoo has 19 Hadoop clusters, including more than 40 thousand servers and over Pb of storage. They developed large-scale machine learning algorithms on these
Deep learning target detection (object detection) series (i) r-cnnDeep learning target detection (object detection) series (ii) spp-netDeep learning target detection (object detection) series (iii) Fast R-CNNDeep learning target detection (object detection) series (iv) Faste
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about the author
The author Dai is a deep learning enthusiast who focuses on the NLP direction. This article introduces the current status of machine translation, and the basic principles and processes involved, to beginners who are interested in deep
In fact, starting from this blog post, we are really into the field of deep learning. In the field of deep learning, the proven mature algorithm, currently has deep convolutional network (DNN) and recursive Network (RNN), in the field of image recognition, video recognition,
Setting up a deep learning machine from Scratch (software)A detailed guide-to-setting up your machine for deep learning. Includes instructions to the install drivers, tools and various deep learning frameworks. This is tested on a
Source: Michael Nielsen's "Neural Network and Deep leraning", click the end of "read the original" To view the original English.This section translator: Hit Scir master Xu Wei (https://github.com/memeda)Statement: We will be in every Monday, Thursday, Sunday regularly serialized the Chinese translation of the book, if you need to reprint please contact [email protected], without authorization shall not be reproduced."This article is reproduced from" h
Today's two network structures are the latest in the industry for image processing problems proposed by the latest structure, the main solution is the Super deep network in training optimization problems encountered. To tell the truth, both models are not mathematically complex in themselves, but it does have a very good effect in combat (the deep residual network helps Microsoft's team to gain the 2015 Ima
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