By Rodolfo Bonnin
- Bored of an excessive amount of thought on TensorFlow? This ebook is what you wish! 13 good initiatives and 4 examples train you ways 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 pragmatic and methodically defined advisor as a way to observe Tensorflow’s positive aspects from the very beginning.
This publication of tasks highlights how TensorFlow can be utilized in numerous eventualities - this contains tasks for education types, desktop studying, deep studying, and dealing with numerous neural networks. each one undertaking offers fascinating and insightful workouts that might educate you the way to take advantage of TensorFlow and express you ways layers of information could be explored via operating with Tensors. easily decide a venture that's based on your surroundings and get stacks of knowledge on find out how to enforce TensorFlow in production.
What you are going to learn
- Load, have interaction, dissect, strategy, and shop advanced datasets
- Solve class and regression difficulties utilizing state-of-the-art thoughts
- 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 photographs utilizing deep neural community schemes
- Tag a collection of pictures and realize gains utilizing a deep neural community, together with a Convolutional Neural community (CNN) layer
- Resolve personality popularity difficulties utilizing the Recurrent Neural community (RNN) model
About the Author
Rodolfo Bonnin is a platforms engineer and PhD scholar at Universidad Tecnológica Nacional, Argentina. He additionally pursued parallel programming and photograph realizing postgraduate classes at Uni Stuttgart, Germany.
He has performed examine on excessive functionality computing on account 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 development 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 set up and extra Tips
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Additional resources for Building Machine Learning Projects with TensorFlow
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.
Command line use To invoke TensorBoard, the command line is: How TensorBoard works Every computation graph we build, TensorFlow has a real-time logging mechanism for, in order to save almost all the information that a model possesses. However, the model builder has to take into account which of the possible hundred information dimensions it should save, to later serve as an analysis tool. To save all the required information, TensorFlow API uses data output objects, called Summaries. These Summaries write results into TensorFlow event files, which gather all the required data generated during a Session's run.
If it receives one, then TensorBoard will visualize your graph as well. Instead, consider running the merged summary op every n steps. Double-click to expand a high-level node. 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.