By Rodolfo Bonnin
- Bored of an excessive amount of conception on TensorFlow? This ebook is what you wish! 13 strong initiatives and 4 examples educate you the way to enforce TensorFlow in production.
- This example-rich advisor teaches you the way to accomplish hugely actual and effective numerical computing with TensorFlow
- It is a realistic and methodically defined advisor on the way to practice Tensorflow’s beneficial properties from the very beginning.
This publication of tasks highlights how TensorFlow can be utilized in numerous eventualities - this contains initiatives for education types, computing device studying, deep studying, and dealing with a variety of neural networks. every one undertaking presents interesting and insightful routines that would educate you ways to take advantage of TensorFlow and convey you the way layers of information might be explored through operating with Tensors. easily choose a venture that's according to your atmosphere and get stacks of data on find out how to enforce TensorFlow in production.
What you are going to learn
- Load, engage, dissect, technique, and shop complicated datasets
- Solve category and regression difficulties utilizing state-of-the-art ideas
- Predict the result of an easy time sequence utilizing Linear Regression modeling
- Use a Logistic Regression scheme to foretell the longer term results of a time series
- Classify pictures utilizing deep neural community schemes
- Tag a collection of pictures and discover good points utilizing a deep neural community, together with a Convolutional Neural community (CNN) layer
- Resolve personality reputation difficulties utilizing the Recurrent Neural community (RNN) model
About the Author
Rodolfo Bonnin is a platforms engineer and PhD pupil at Universidad Tecnológica Nacional, Argentina. He additionally pursued parallel programming and picture realizing postgraduate classes at Uni Stuttgart, Germany.
He has performed learn on excessive functionality computing seeing that 2005 and commenced learning and imposing convolutional neural networks in 2008,writing a CPU and GPU - aiding neural community feed ahead level. extra lately he is been operating within the box of fraud trend detection with Neural Networks, and is at present engaged on sign type utilizing ML techniques.
Table of Contents
- Exploring and reworking Data
- Linear Regression
- Logistic Regression
- Simple FeedForward Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks and LSTM
- Deep Neural Networks
- Running types at Scale – GPU and Serving
- Library install and extra Tips
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Additional resources for Building Machine Learning Projects with TensorFlow
Clustering In this chapter, we will start applying the data transforming operations that we learned in the previous chapter, and will begin finding interesting patterns in some given information, discovering groups of data, or clusters, using clustering techniques. In this process we will also gain two new tools: the ability to generate synthetic sample sets from a collection of representative data structures via the scikit-learn library, and the ability to graphically plot our data and model results, this time via the matplotlib library.
Constant ([1,2,3]) From numpy to tensors and vice versa TensorFlow is interoperable with numpy, and normally the eval() function calls will return a numpy object, ready to be worked with the standard numerical tools. run(tensor_to_eval). It accepts tensorobjects, numpy arrays, Python lists, and Python scalars. Then we invoke it again, and the Python interpreter shows the shape and type of the tensor. We can also use the IPython interpreter, which will allow us to employ a format more compatible with notebook-style tools, such as Jupyter: IPython prompt When talking about running TensorFlow Sessions in an interactive manner, it's better to employ the InteractiveSession object.
Sequence of numbered nodes that are not connected to each other. Sequence of numbered nodes that are connected to each other. An individual operation node. A constant. A summary node. Edge showing the data flow between operations. Edge showing the control dependency between operations. A reference edge showing that the outgoing operation node can mutate the incoming tensor. It will turn a darker color, and details about it and the nodes it connects to will appear in the info card in the upper-right corner of the visualization.