udacity pytorch

Mxnet is the foundation, Gluon is the encapsulation, both like TensorFlow and Keras, but thanks to the dynamic graph mechanism, the interaction between the two is much more convenient than TensorFlow and Keras, its basic operation and pytorch very similar, but a lot of convenience, It's easy to get started with a pytorch foundation.Library import notation,From mxnet import Ndarray as Ndfrom mxnet import aut

Summarize the recent development of CNN Model (i) from:https://zhuanlan.zhihu.com/p/30746099 Yu June computer vision and deep learning1. PrefaceLong time no update column, recently because of the project to contact the Pytorch, feeling opened the deep learning new world of the door. In his spare time, Pytorch trained the recent CNN model of State-of-the-art in image classification, which is summarized in th

ones.Some people has called Keras so good that it's effectively cheatingin machine learning. So if you ' re starting off with deep learning, go through the examples and documentation to get a feel for what can do With it. And if you want to learn, the start out with this tutorial and the see where you can go from there.The similar alternatives is lasagne and Blocks, but the they only run on Theano. So if you tried Keras and is unhappy with it, the maybe try out one of the these alternatives to

1. CPU vs. GPU:Less CPU cores (few), better at serial tasks. The GPU has a lot of cores (thousands of), each of which is weak and has its own memory (several g), which is ideal for parallel tasks. The most typical application of GPUs is matrix operations.GPU Programming: 1) Cuda, only in Nvidia, 2) OpenCL similar to Cuda, the advantage is that it can be run on any platform, but relatively slowly. Deep learning can call off-the-shelf libraries without having to write Cuda code on their own.Using

I feel in the learning process, encountered do not understand, often need to review the probability theory of knowledge, so assume the machine learning nouns are mastered.The following articles are the first big time feel read once you can understand. The basis of probability theory Prior probability, posterior probability, Bayesian rule, maximal posterior probability hypothesis, maximum likelihood hypothesis Bayesian Bayesian learning – Maximum posterior probability hypothesis and maximum like

neural networks.Model structure examples such as the followingConvolution layer example belowFull Connection Layer example belowData set statistics are as followsThe results of each model are compared as followsCode addressHttps://github.com/zhangxiangxiao/Crepe (Torch)Https://github.com/mhjabreel/CharCNN (TensorFlow)Https://github.com/srviest/char-cnn-text-classification-pytorch (Pytorch)I'm a split line.

less than 1000 in the same game. WaveNets, CNNs, and attention mechanisms Google's Tacotron 2 text-to-speech system is impressive. This system is based on WaveNet and is also an automatic regression model deployed in Google Assistant and has been rapidly improved over the past year. Moving away from expensive and training long regression architectures is a larger trend. In the paper Attention is All you Need, the researchers completely get rid of loops and convolution, using a more complex Att

Turn the arrogant cow dev Nag. Dev Nag is a former Google senior engineer, AI start-up Wavefront founder and CTO, this article describes how he used less than 50 lines of code, on the Pytorch platform to complete the training of GAN. In 2014, Ian Goodfellow and he colleagues at the University of Montreal published a stunning paper introducing the world To Gans, or generative adversarial networks. Through an innovative combination of computational grap

://developers.google.com/edu/python/?hl=zh-CNcsw=1 This is a two-day short-term training course (two full days, of course), probably seven videos, each of which is followed by a programming assignment that can be completed within one hours of each job. This is my second class to learn Python (the first one is Codecademy python, very early to see, a lot of content are not remembered), then watch video + programming One hours a day, six days to finish, the effect is good, with Python write basic p

functions, began not accustomed to stata programming way, so the code is not easy to reuse, do file a long, slowly feel a bit chaotic. And then the matrix operation and computing function is not very good.Later, with his interest in data science and machine learning, there were some Python-based courses on the edx, Coursera, Udacity and other platforms. One of the most rewarding lessons for the utility is EdX's two Python courses (6.00.1x and 6.00.2X

("Entrepreneurial Innovation Executive Power" course)), until the certificate or proof of achievement.(4) The student who has not completed the article (3) on time will go to the Dean's Office,application for cancellation of "entrepreneurial Innovation Executive Power" course of elective results。Second: attached 1-MOOC learning platformNetEase Cloud Classroom (link, http://study.163.com/)China University Mooc (link, http://www.icourse163.org/)Tsinghua Academy Online (Link, www.xuetangx.com)Unit

The programmer's life is easy. There are plenty of jobs and good pay.Even if you don't want to be a programmer, it's still right to learn some programming. Especially for those who work in web design, digital marketing, corporate and it industries.But which language should you study?The Udacity website makes a great infographic (see below) to help you choose. But I'd like to talk a little bit further.On the basis of their infographic, I will give my a

Citation I was learning programming from last year (Python), and still in the introductory phase. The reason why my entry stage is so long, I think there are several reasons: first, no choice of teaching materialsAt first I read the concise Python tutorial, and most of the concepts in the book are simple enough to be considered as a basic understanding. Then do a network online tutorial, blindly emphasize the grammar rules, anti-sleep boring, like to do in the blanks. Then, after watching the tu

Original source: http://bbs.landingbj.com/t-0-267091-1.html "programming language" in the past is often the same as engineers, a house painting, but the progress of business and technology today, the importance of programming language is not alone, and even already deep in everyone's mind. For example, the game plug-in script, mouse keyboard record program, Excel formula, macro, in many places can see the shadow of the program language, even President Obama has written a program, but also the pr

Basic knowledge "Fluent Python" the 1th to 4th Chapter _ Data structure, code "python" basic data structure common use method "python coolbook" Data structure and algorithm _ multivariable Assignment "*" Two usages of "python Coolbook:collections "Data structure and algorithm _collections.deque queue yield application" Python COOLBOOK:HEAPQ "data structure and algorithm _HEAPQ heap queue algorithm container sequencing" Python coolbook:collections "Data structure and algorithm _ Container Type

) SOURCE Link: https://github.com/PAIR-code/deeplearnjs 7. Fast style migration base based on TensorFlow (GitHub 4843 stars, contributors are Logan Engstrom of MIT) SOURCE Link: Https://github.com/lengstrom/fast-style-transfer 8. PYSC2: StarCraft 2 Learning Environment (GitHub 3684 stars, contributors are DeepMind Timo Ewalds) SOURCE Link: https://github.com/deepmind/pysc2 9. Airsim:microsoft AI Research Open source Simulator based on Unreal Engine for automatic driving (GitHub 3861 star, contr

algorithm, the training process can be very unstable. But you can use some "tips" to get a more robust training process. In the following video, you can see the training evolution of the images generated by Gans. Code If you are interested in the basic implementation of Gans, you can see the link to the code: TensorFlow (HTTPS://GITHUB.COM/ERICJANG/GENADV_TUTORIAL/BLOB/MASTER/GENADV1.IPYNB) Torch and Python (pytorch) (https://github.com/devnag/

3087 stars, contributors are Google brain's Ryan Sepassi) SOURCE Link: https://github.com/tensorflow/tensor2tensor 13. Image conversion based on Pytorch implementation (GitHub 2847 stars, contributor Berkeley Jun-yan Zhu, Ph.D) Source Address: Https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix 14.Faiss: Efficient similarity for dense vectors search libraries and clustering libraries (GitHub 2629 sta

request. Of course, don't forget Starred!Updated every week. Stay tuned. -- By Django Learning Group, we look forward to your suggestions and comments for reference to the open class Udacity CS253: Classroom-Udacity Although the framework uses webapp2, it can be used as the basis. We recommend that you first clarify your purpose: If you want to learn, you should learn python and http-related knowledge. dja

own, you are not used to the Stata programming method, so the code is not easy to reuse. Do File is a long time and you will feel a little messy. Furthermore, matrix operations and operations are not very useful. Later, I became interested in data science and machine learning and offered Python-based courses on platforms such as edX, Coursera, and Udacity. The most useful learning tools are the two Python courses (6.00.1x and 6.00.2x) at MIT on edX