deep learning tutorial tensorflow

Learning notes TF062: TensorFlow linear algebra compiling framework XLA, tf062tensorflow XLA (Accelerated Linear Algebra), a specialized Linear Algebra compiler (demain-specific compiler), optimizes TensorFlow computing. Real-time (just-in-time, JIT) compilation or advance (ahead-of-time, AOT) compilation to implement XLA, which facilitates hardware acceleration.

Deep learning has a profound effect on computer science. It makes it possible for cutting-edge technology to research and develop products that are used by tens of millions of of people everyday.The study announced the launch of the second-generation machine learning System (TENSORFLOW), which has been strengthened for

field, you, the reader, need to choose a suitable way to go. This should be a practical experience so that you can get an appropriate foundation on top of what you now understand. Note: Each path contains an introductory blog, a practice project, a program library of deep learning required for the project, and an ancillary course. Start by understanding the introduction and then install the required librar

Http://www.leiphone.com/news/201605/WOgdrkYSUwuwqQjD.html This summer, Lei Feng (public number: Lei Feng Network) will be held in Shenzhen, an unprecedented "global artificial Intelligence and Robotics Innovation Conference" (abbreviated as Gair). The scene of the conference, Lei Feng Network will release the "Artificial Intelligence Robotics TOP25 Innovation Enterprise List" list. We are currently visiting companies in the field of artificial intelligence and robotics to sift through the list

a larger new dataset that can be adjusted. Image datasets are larger than 200x10. A complex network structure requires more training sets. Be careful about fitting. References 1. cs231n convolutional neural Networks for Visual recognition 2. TensorFlow convolutional Neural Networks 3. How to Retrain Inception's Final Layer for New Categories 4. K-nn Classifier for image classification 5. Image augmentation for

Copyright NOTICE: This article for Bo Master hjimce original article, the original address is http://blog.csdn.net/hjimce/article/details/51899683. I. Course of study Personal feeling for any deep learning library, such as Mxnet, TensorFlow, Theano, Caffe, and so on, basically I use the same learning process, the gener

PyTorch dynamic computing diagram. In addition, Apple released the CoreML mobile machine learning library; A team of Uber released Pyro, a deep probability programming language; Amazon announced the provision of more advanced API Gluon on MXNet; Uber released the details of the internal machine learning infrastructure platform of Picasso; Because there are

an open source software library that uses a data flow graph (stream graphs) for numerical computations. A node (Nodes) represents a mathematical operation in a graph, and a line (edges) in a graph represents an array of multidimensional data, the tensor (tensor), that is interconnected between nodes. Its flexible architecture allows you to expand computing on a variety of platforms, such as one or more CPUs (or GPU), servers, mobile devices, and so on in a desktop computer.

results are connected at the full connection layer using 1x1 convolution kernel dimensionality reduction. Width, depth expansion, acceleration. Layers deeper, 22 layers, different depths add two loss functions to avoid the reverse propagation gradient disappear. Adding a variety of size convolution cores, reduced dimension inception model, 1x1 convolution kernel to reduce the feature map thickness.Combine the network to deepen and enhance the convolution module function.ResNet. 2015, ILSVRC doe

In deep learning, regardless of the learning framework, we encounter an important problem, that is, after training, how to store the depth of the network parameters. How these network parameters are invoked at the time of the test. In response to these two questions, this blog post explores how TensorFlow solves them.

BP neural networks are not effective in image classification. Even on CNN, the results of CNN's experiments are still better than the traditional algorithms. Migration learning is very effective in the image classification problem. The operation time is short and the result is accurate, can solve the problem of fitting and data set too small well. Through this project, we have gained a lot of valuable experience, as follows: Adjust the image to make

These days to get started learning machine learning content, the first to install TensorFlow.I've been tinkering with it for a few days. Maybe it's a stupid orz.Now try to write a tutorial, hoping to help the lost children!Roughly speaking, four steps:Install Python environment, configuration python path-> installation numpy-> installation TensorFlowStep1 Install

Amazon open machine learning system source code: Challenges Google TensorFlowAmazon took a bigger step in the open-source technology field and announced the opening of the company's machine learning software DSSTNE source code. This latest project will compete with Google's TensorFlow, which was open-source last year. Amazon said that DSSTNE has excellent perform

Learning Google's deep learning finally a little bit of the prospect, to share my tensorflow learning process. TensorFlow's official Chinese document is jerky, and the dataset has been used in the Mnist binary dataset. And not much about how to build their own picture datase

Last year in Beijing participated in a big data conference organized by O ' Reilly and Cloudera, Strata , and was fortunate to have the O ' Reilly published hands-on machine learning with Scikit-learn and TensorFlow English book, in general, this is a good technical book, a lot of people are also recommending this book. The author of the book passes specific examples, Few theories and two mature Python fra