cs224d

Welcome reprint, Reprint annotated Source:http://blog.csdn.net/neighborhoodguo/article/details/473787371. The following is the detailed derivation process for problem set 2:2.I am enclosing my own code for the resolution process, please correct me if there is any mistake.Https://github.com/NeighborhoodWang/CS224D-problem-set2.git3. Key points to note:Python Generatorshttp://www.python-course.eu/generators.phpThe difference between range and xrangeHttp

Refer to: Machine Learning Public Course notes (5): Neural Network (neural networks) CS224D Notes 3--Neural network Deep learning and natural Language processing (4) _ Stanford cs224d Big Homework Quiz 1 with solution cs224d Problem Set 1 jobs Softmax def softmax (x): assert Len (x.shape) > 1 X-= Np.max (x, Axis=1, keepdims=true) x = np.exp (x)/Np.sum

the best w then Max () in the left of the score selected is not y_i, plus l_i so the final must be RI very large, is not the smallest, if w is optimal? It is certain that Max () has chosen Yi,delta to be zero, and then the total must be minimal. Such a w must make score (y_i) larger than all the other score (y) and a margin of l_i (y).3.BPTSBpts in the paper is relatively small, slide is quite detailed and Pset2 part of the code is good drops. There are three differences between bpts and the pr

Welcome reprint, Reproduced please indicate the source:http://blog.csdn.net/neighborhoodguo/article/details/476706051. Detailed derivation process:2. Code examplesThe evil gfw actually put GitHub upload port to seal, so I can only use the domestic version of this cottageHttps://gitcafe.com/NeighborhoodGuo/cs224d-problem-set3.git Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced.

Welcome reprint, reprint indicate the origin: http://blog.csdn.net/neighborhoodguo/article/details/47071597The detailed derivation process of computational problems in 1.assignment1 The negative sampling in the programming question is similar to the derivation process of the third question (a), so there is no deduction 4. (a) because over-fitting'll make model have a poor generalized error and overfit the training set. In order to improve our model's accuracy, we should introduce regulariza

Recently began to watch Stanford's cs224d course, which is the newest Stanford's Practical course on deep learning. Interested friends can point here This article is just one of my learning notes, inevitably have errors and omissions, if a netizen found my mistake or omission, please do not hesitate, in this thank. The course information is still quite complete, the video has been uploaded to YouTube, various matrials are also available on the officia

Ethernet communication between computers is too slow to develop faster communication between computers.Asynchronous SGDThe calculation method that was previously said to be synchronous requires waiting for each work core to be completed in order to summarize and calculate the results, so that part of the time is spent waiting.In view of this, an asynchronous SGD is proposedAssign tasks or assign them as usual, but who will upload the results to master and then update the data for each work core

with CNN, which is superior to the information extracted from the distorted sound.The second is to replace the normal DNN with the recurrent NN.Hmm-free RNN RecognitionThis translates the traditional sub-phone extraction into the collapsing function.The word output no longer takes the whole word as a unit, and the part of the word fragment as a unitFor a period of time when the gap between the voices is not pronounced, the "_" occupies the position.Using RNN is a lot lower than normal nn Error

description for the last few sentences. Conversely, you can do image retrieval.However, the resulting sentences are limited sentences, the computer can not "describe", so there is an improved version.First, use the CNN model to project the image into a vector, and then use LSTM to generate the sentence. This is a bit like a machine translation, just replaced the source language with an imageFinally, the evaluation method for this model (evaluation) is named mean rankWhen you create a picture fo

Welcome reprint, reprint indicate the origin:http://blog.csdn.net/neighborhoodguo/article/details/46868143 This blog is used to share a variety of learning materials cs224d Oh, I download the finishing study these can be used for a long time, I hope to help to learn this knowledge of friends, video is from YouTube to get down, only for learning and exchange use Oh, we do not disorderly forwarding. 1. Lecture 1 Material 2.Lecture 2 Material

Welcome reprint, Reprint annotated Source:Http://www.cnblogs.com/NeighborhoodGuo/p/4702932.htmlGo Go GoThe tenth lecture also successfully concluded, is worthy of the advanced Recursive nn said content is indeed some advanced, but if serious

Welcome reprint, Reprint annotated Source:Http://www.cnblogs.com/NeighborhoodGuo/p/4711678.htmlCS224D's 11th lesson is their class exams, so the video on the back jumps straight to lecture 12. Lecture 12 is a foreign guest who is said to be a

hahaha, finally came to each lesson of the time to write notes. The content of this course is relatively small, perhaps in order to give problem set 1 free time.Don't say much nonsense. Write it.This video tells the main thing is the recommended

})}$Only need to count the $w_{i}$, $W _{i-1}$ with the frequency of appearing together can be.It looks really simple, very mathematical beauty. Of course, as a popular science books, it will not tell you how harmful this method is.Implementation, you can use the following two algorithms:①KMP: Put $w_{i}$, $W _{i-1}$ two words together, run once the text string.②ac automaton: Same stitching, but pre-spell all the pattern string, input AC automaton, just run once text string.But if you are an ACM

• "Dimensionality reduction" (reference from Stanford cs224d Deep Learning for NLP course)• I like deep learning.ILIKENLP.ienjoyflying.Let's say that there are 3 sentences that become our corpus, and we have noticed the problem of the correlation between words.Using a new encoding to form a discrete statistical matrix, the number of context-related words = 1. Statistics I Like Enjoy Deep Learning Nlp F

1. Reading The Recurrent neural Network (NN) is the most commonly used neural network structure in NLP (Natural language Processing), and the convolution neural network is similar in the field of image recognition. Before we introduced CNN in detail in 4 blog posts, it is now necessary to introduce RNN before using CNN for a variety of purposes. Then it will be better to integrate practice. Some valuable links: http://www.cnblogs.com/neopenx/p/4623328.html https://yjango.gitbooks.io/super

Awesome series Awesome Machine Learning Awesome Deep Learning Awesome TensorFlow Awesome TensorFlow implementations Awesome Torch Awesome Computer Vision Awesome Deep Vision Awesome RNN Awesome NLP Awesome AI Awesome Deep Learning Papers Awesome 2vec Deep learning [Book] Neural Networks and deep learning Chinese Translation (incomplete): Neural network and in-depth learning fifth Chinese translation: [Translate] Chapter Fifth why

layer and activation function how to mix together. Because before in the UFLDL fc+sigmoid is treated as a layer.Notebook inside to help you write "unit test", very good, so every step has checkpoint know that they did not.NumPy default is to pass the reference, remember to use the Xx.copy () method to return a deep copy.Assignment 3Transfer a Tanh derivative rnn_layers.py h (t) at the time of BP in addition to their own node output, t+1 node also has gradient passed over. Say more are

homework.If you don't like watching video, but prefer reading , neural networks and deep learning (neural Networks and Learning) is an online free book written for beginners in deep learning. The book of Deep Learning is also a great free book, but a little higher-learning.Once you have the basics, you can also develop in these areas: Almost all of these deep learning materials are more or less related to computer vision (computer vision). Recurrent neural Networks (Recurrent nerua

Bengio, LeCun, Jordan, Hinton, Schmidhuber, Ng, de Freitas and OpenAI had done Reddit AMA's. These is nice places-to-start to get a zeitgeist of the field.Hinton and Ng lectures at Coursera, UFLDL, cs224d and cs231n at Stanford, the deep learning course at udacity, and the sum Mer School at IPAM has excellent tutorials, video lectures and programming exercises that should help you get STARTED.NB Sp The online book by Nielsen, notes for cs231n, and blo