Building Machine Learning Projects with TensorFlow by Rodolfo Bonnin

By Rodolfo Bonnin

Key Features

  • 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.

Book Description

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

  1. Exploring and reworking Data
  2. Clustering
  3. Linear Regression
  4. Logistic Regression
  5. Simple FeedForward Neural Networks
  6. Convolutional Neural Networks
  7. Recurrent Neural Networks and LSTM
  8. Deep Neural Networks
  9. Running types at Scale – GPU and Serving
  10. Library set up and extra Tips

Show description

Read Online or Download Building Machine Learning Projects with TensorFlow PDF

Similar data mining books

Mining of Massive Datasets

The recognition of the internet and web trade offers many tremendous huge datasets from which details might be gleaned through information mining. This publication makes a speciality of sensible algorithms which have been used to resolve key difficulties in facts mining and which might be used on even the biggest datasets. It starts with a dialogue of the map-reduce framework, a huge software for parallelizing algorithms immediately.

Twitter Data Analytics (SpringerBriefs in Computer Science)

This short presents tools for harnessing Twitter facts to find strategies to advanced inquiries. The short introduces the method of accumulating information via Twitter’s APIs and gives suggestions for curating huge datasets. The textual content supplies examples of Twitter info with real-world examples, the current demanding situations and complexities of creating visible analytic instruments, and the easiest suggestions to handle those matters.

Advances in Natural Language Processing: 9th International Conference on NLP, PolTAL 2014, Warsaw, Poland, September 17-19, 2014. Proceedings

This publication constitutes the refereed court cases of the ninth foreign convention on Advances in typical Language Processing, PolTAL 2014, Warsaw, Poland, in September 2014. The 27 revised complete papers and 20 revised brief papers provided have been rigorously reviewed and chosen from eighty three submissions. The papers are geared up in topical sections on morphology, named entity acceptance, time period extraction; lexical semantics; sentence point syntax, semantics, and computer translation; discourse, coreference solution, computerized summarization, and query answering; textual content category, details extraction and data retrieval; and speech processing, language modelling, and spell- and grammar-checking.

Analysis of Large and Complex Data

This ebook deals a photograph of the state of the art in category on the interface among records, desktop technological know-how and alertness fields. The contributions span a wide spectrum, from theoretical advancements to useful functions; all of them proportion a powerful computational part. the themes addressed are from the next fields: information and knowledge research; laptop studying and information Discovery; facts research in advertising and marketing; facts research in Finance and Economics; info research in medication and the lifestyles Sciences; information research within the Social, Behavioural, and wellbeing and fitness Care Sciences; info research in Interdisciplinary domain names; category and topic Indexing in Library and knowledge technological know-how.

Additional resources for Building Machine Learning Projects with TensorFlow

Sample text

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.

Download PDF sample

Rated 4.04 of 5 – based on 45 votes