By Rodolfo Bonnin
- Bored of an excessive amount of thought on TensorFlow? This publication is what you wish! 13 sturdy initiatives and 4 examples educate you the way to enforce TensorFlow in production.
- This example-rich consultant teaches you ways to accomplish hugely actual and effective numerical computing with TensorFlow
- It is a pragmatic and methodically defined advisor in order to practice Tensorflow’s beneficial properties from the very beginning.
This ebook of tasks highlights how TensorFlow can be utilized in several situations - this contains tasks for education types, computer studying, deep studying, and dealing with a number of neural networks. each one venture offers interesting and insightful routines that might train you ways to take advantage of TensorFlow and convey you ways layers of information should be explored by means of operating with Tensors. easily decide a undertaking that's based on your surroundings and get stacks of data on the best way to enforce TensorFlow in production.
What you are going to learn
- Load, engage, dissect, method, and shop advanced datasets
- Solve type and regression difficulties utilizing cutting-edge strategies
- Predict the result of an easy time sequence utilizing Linear Regression modeling
- Use a Logistic Regression scheme to foretell the long run 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 acceptance 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 picture realizing postgraduate classes at Uni Stuttgart, Germany.
He has performed learn on excessive functionality computing given that 2005 and started learning and imposing convolutional neural networks in 2008,writing a CPU and GPU - aiding neural community feed ahead degree. extra lately he is been operating within the box of fraud trend detection with Neural Networks, and is presently engaged on sign category 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 versions at Scale – GPU and Serving
- Library install and extra Tips
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Extra info for Building Machine Learning Projects with TensorFlow
Float32 32 bits floating point. float64 64 bits floating point. DT_INT8 8 bits signed integer. int16 16 bits signed integer. int32 32 bits signed integer. int64 64 bits signed integer. uint8 8 bits unsigned integer. Each element of a tensor is a byte array. DT_BOOL Boolean. bool Creating new tensors We can either create our own tensors, or derivate them from the well-known numpy 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.
When you run TensorBoard, it will read the graph definition from the file and display it graphically so you can interact with it. First, create the TensorFlow graph that you'd like to collect summary data from and decide which nodes you would like to annotate with summary operations. merge_all_summaries to combine them into a single op that generates all the summary data. SummaryWriter. If it receives one, then TensorBoard will visualize your graph as well. Instead, consider running the merged summary op every n steps.
Merge_all_summaries to combine them into a single op that generates all the summary data. SummaryWriter. 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.