Deep Learning Framework Google TensorFlow Learning notes one __ deep learning

TensorFlowTensorFlow is Google's second generation of AI learning systems based on Distbelief, whose name comes from its own operating principles. Tensor (tensor) means n-dimensional arrays, flow (stream) means the computation based on data flow diagram, TensorFlow flows from one end of the flow graph to the other. TensorFlow is a system that transmits complex data structures to artificial neural networks for analysis and processing. TensorFlow can be used in a wide range of machine-depth learning areas such as speech recognition or image recognition, which has been improved in the 2011 's in-depth learning infrastructure distbelief, which runs on a variety of devices as small as a smartphone and up to thousands of data center servers. TensorFlow will be completely open source and can be used by anyone. TensorFlow express a high level of machine learning computing, greatly simplifying the first generation of systems, and with greater flexibility and scalability. One of the highlights of TensorFlow is the support for distributed computing of heterogeneous devices, which can automate models on a variety of platforms, from mobile phones to individual cpu/gpu to hundreds of GPU cards distributed systems.
From the current documentation, TensorFlow supports the CNN, RNN, and lstm algorithms, which are the most popular deep neural network models currently in Image,speech and NLP.
This time Google open source depth learning system TensorFlow can be applied in many places, such as speech recognition, natural language understanding, computer vision, advertising and so on. However, based on the above argument, we should not exaggerate the role of TensorFlow in an industry machine learning system. In a complete industry speech recognition system, in addition to the depth learning algorithm, there are many areas of expertise related to the algorithm, as well as massive data collection and engineering system architecture. But overall, this time Google's open source is meaningful, especially for many Chinese startups, most of them do not have the ability to understand and develop an international synchronization of the deep learning system, so tensorflow will greatly reduce the depth of learning in various industries in the application of difficulty. Let's take a look at Google's TensorFlow playground.The address of the playground is: http://playground.tensorflow.org. The greatest magic of neural networks is that we do not need to come up with a variety of characteristics to input to the machine learning system. We only need to enter the most basic features X1, x2, as long as we give enough multilayer neural networks and neurons, the neural network will assemble the most useful features. And when we have a neural network, our system can learn which features are effective and which are ineffective, which greatly improves our ability to solve complex machine learning problems, which is simply too cool.