theano deep learning

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

This paper describes how to apply the deep learning-based target detection algorithm to the specific project development, which embodies the value of deep learning technology in actual production, and is considered as a landing realization of AI algorithm. The algorithms section of this article can be found in the prev

This article is a basic learning blog from the University of Paris, PhD Hadrien Jean, which aims to help beginners/Advanced Beginners Master the concept of linear algebra based on deep learning and machine learning. Mastering these skills can improve your ability to understand and apply a variety of data science algori

Deep learning over the past few years, the feature extraction capability of convolutional neural Networks has made this algorithm fire again, in fact, many years ago, but because of the computational complexity of deep learning problems, has not been widely used.As a general rule, the convolution layer is calculated in

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

theoretical knowledge : Deep learning: 41 (Dropout simple understanding), in-depth learning (22) dropout shallow understanding and implementation, "improving neural networks by preventing Co-adaptation of feature detectors "Feel there is nothing to say, should be said in the citation of the two blog has been made very clear, direct test itNote :1. During the test

I. List of studies1. Comprehensive class(1) collected a variety of the latest and most classic literature, neural network resources list: Https://github.com/robertsdionne/neural-network-papers contains the deep learning domain classic, as well as the latest and best algorithm, If you learn this list over and over again, you have already reached the great God level.(2) Machine

Target detection is a simple task for a person, but for a computer it sees an array of values of 0~255, making it difficult to directly get a high-level semantic concept for someone or a cat in the image, or the target to eat the area in the image. The target in the image may appear in any position, the shape of the target may have a variety of changes, the background of the image is very different ..., these factors lead to target detection is not an easy task to solve. Thanks to

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

1. Structure diagramIntroductionFeature extraction, deformation handling, occlusion handling, and classification is four important components in Pedestri An detection. Existing methods Learn or design these components either individually or sequentially. The interaction among these are not yet well explored. This paper proposes, they should be jointly learned in order to maximize their strengths through cooperation. We formulate these four to a joint deep