deep learning mlp

Preface At present, deep learning to grab enough eyeballs and attention, from the layout of major companies, to the springing out of a wave of start-up companies, and then to all kinds of popularization, in-depth analysis of the relevant public number, every day there are a large number of technology, paper interpretation related articles, blogs, etc., a variety of information such as flooding into our vis

Deep learning has been fire for a long time, some people have been here for many years, and some people have just begun, such as myself. How to get into this field quickly in a short period of time to master deep learning the latest technology is a question worth thinking about. In the present situation, it is the best

Recently, Google published in the Journal of the American Medical Council titled "Development and Validation of a deep learning algorithm for Detection of diabetic retinopathy in Reti NAL Fundus Photographs "is a deep learning algorithm that Google researchers have put forward to explain the signs of diabetic retinopat

First, it's up to the father of Ai, Turing. Turing once had a dream uninstall "computer and Intelligence" (1950) article, if one day, the computer can do, across the wall, you do not know the opposite and you communicate is a person or computer, then this computer has artificial intelligence. For the next half century, Ai has not developed much. Although the computer has the powerful memory and the data processing ability, but does not have the human cognition ability. For example, Wang, Meo

most important thing to know about OpenAI is to understand the frontiers of AI research.What is the research direction of Ai's frontier?OpenAI raised three points:-Training Generative Models-Algorithms for inferring algorithms from data-New approaches to reinforcement learningSo what do these three categories represent, respectively?Deep generative ModelsThe first type is oriented to the generation model, the main task is to generate new information,

feature algorithms, our goal is usually to isolate the variables that explain the observed data.Deep learning allows a computer to construct complex concepts through simpler concepts. (The examples in the comparison book can be understood clearly)The idea of learning the correct representation of data is a point of view for explaining deep

In the previous sections, we have covered what is target detection and how to detect targets, as well as the concepts of sliding windows, bounding box, and IOU, non-maxima suppression.Here will summarize the current target detection research results, and several classical target detection algorithms to summarize, this article is based on deep learning target detection, in the following sections, will be spe

Source: Michael Nielsen's "Neural Network and Deep leraning"This section translator: Hit Scir master Xu Zixiang (Https://github.com/endyul)Disclaimer: We will not periodically serialize 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" hit SCIR "public number, reprint has obtained consent. " Using neural networks

matching is no longer effective, and then the OCR algorithm is difficult to parse the results.In recent years, The Deep Neural Network (DNN) has been proved to be a powerful recognition capability in the field of image recognition. The identification of single text is a typical classification problem. The usual practice is to train a deep neural network, the last layer of the network is divided into n cate

Https://github.com/exacity/deeplearningbook-chinese In the help of many netizens and proofreading, the draft slowly became a first draft. Although there are still many problems, at least 90% of the content is readable and accurate. We kept the meaning of the original book Deep learning as much as possible and kept the original book's statement. However, we have limited levels and we cannot eliminate the va

Python1. Theano is a Python class library that uses array vectors to define and calculate mathematical expressions. It makes it easy to write deep learning algorithms in a python environment. On top of it, many classes of libraries have been built.1.Keras is a compact, highly modular neural network library that is designed to reference torch, written in Python, to support the invocation of GPU and CPU-optim

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

1. A series of articles about getting started with DQN:DQN from getting started to giving up2. Introductory Paper2.1 Playing Atariwith a deep reinforcement learning DeepMind published in Nips 2013, the first time in this paper Reinforcement learning this name, and proposed DQN (deep q-network) algorithm, realized from

Neural network and support vector machine for deep learningIntroduction: Neural Networks (neural network) and support vector machines (SVM MACHINES,SVM) are the representative methods of statistical learning. It can be thought that neural networks and support vector machines both originate from the Perceptual machine (Perceptron). Perceptron is a linear classification model invented by Rosenblatt in the 195