[FreeCoursesOnline.Me] [Packtpub.Com] Building Machine Learning Systems with TensorFlow - [FCO]

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Description

By Rodolfo Bonnin

Released Friday, March 31, 2017

Torrent Contains: 48 Files, 9 Folders

Course Source: https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-tensorflow-video

Engaging projects that will teach you how complex data can be exploited to gain the most insight

Video Details

ISBN 9781787281806

Course Length 2 hours 44 minutes

Table of Contents

• EXPLORING AND TRANSFORMING DATA

• CLUSTERING

• LINEAR REGRESSION

• LOGISTIC REGRESSION

• SIMPLE FEEDFORWARD NEURAL NETWORKS

• CONVOLUTIONAL NEURAL NETWORKS

• RECURRENT NEURAL NETWORKS AND LSTM

• DEEP NEURAL NETWORKS

• LIBRARY INSTALLATION AND ADDITIONAL TIPS

Video Description

This video, with the help of practical projects, highlights how TensorFlow can be used in different scenarios—this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Simply pick a project in line with your environment and get stacks of information on how to implement TensorFlow in production.

Style and Approach

This video is a practical guide to implementing TensorFlow in production. It explores various scenarios in which you can use TensorFlow and shows you how to use it in the context of real-world projects. This will not only give you the upper hand in the field, but shows the potential for innovative uses of TensorFlow in your environment. This course opens the door to second- generation machine learning and numerical computation.

What You Will Learn

• Load, interact, dissect, process, and save complex datasets

• Solve classification and regression problems using state-of-the-art techniques

• Predict the outcome of a simple time series using Linear Regression modeling

• Use a Logistic Regression scheme to predict the future result of a time series

• Classify images using deep neural network schemes

• Tag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layer

• Resolve character-recognition problems using the Recurrent Neural Network (RNN) model

Authors

Rodolfo Bonnin

Rodolfo Bonnin is a systems engineer and Ph.D. student at Universidad Tecnológica Nacional, Argentina. He has also pursued parallel programming and image understanding postgraduate courses at Universität Stuttgart, Germany.

He has been doing research on high-performance computing since 2005 and began studying and implementing convolutional neural networks in 2008, writing a CPU- and GPU-supporting neural network feedforward stage. More recently he's been working in the field of fraud pattern detection with Neural Networks and is currently working on signal classification using machine learning techniques.

He is also the author of Building Machine Learning Projects with Tensorflow and Machine Learning for Developers by Packt Publishing.

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File list

[FreeCoursesOnline.Me] [Packtpub.Com] Building Machine Learning Systems with TensorFlow - [FCO]
Chapter 1 - Exploring and Transforming data/01. The Course Overview.mp4 18.6 MB
Chapter 1 - Exploring and Transforming data/02. TensorFlow's Main Data Structure Tensors.mp4 27.1 MB
Chapter 1 - Exploring and Transforming data/03. Handling the Computing Workflow TensorFlow's Data Flow Graph.mp4 16.2 MB
Chapter 1 - Exploring and Transforming data/04. Basic Tensor Methods.mp4 36.9 MB
Chapter 1 - Exploring and Transforming data/05. How TensorBoard Works.mp4 24.6 MB
Chapter 1 - Exploring and Transforming data/06. Reading Information from Disk.mp4 21.8 MB
Chapter 2 - Clustering/07. Learning from Data Unsupervised Learing.mp4 4.6 MB
Chapter 2 - Clustering/08. Mechanics of k-Means.mp4 5.8 MB
Chapter 2 - Clustering/09. k-Nearest Neighbor.mp4 18.9 MB
Chapter 2 - Clustering/10. Project 1 k-Means Clustering on Synthetic Datasetsets.mp4 19.5 MB
Chapter 2 - Clustering/11. Project 2 Nearest Neighbor on Synthetic Datasets.mp4 9.9 MB
Chapter 3 - Linear Regression/12. Univariate Linear Modelling Function.mp4 8.8 MB
Chapter 3 - Linear Regression/13. Optimizer Methods in TensorFlow The Train Module.mp4 5.5 MB
Chapter 3 - Linear Regression/14. Univariate Linear Regression.mp4 25.3 MB
Chapter 3 - Linear Regression/15. Multivariate Linear Regression.mp4 21.5 MB
Chapter 4 - Logistic Regression/16. Logistic Function Predecessor The Logit Functions.mp4 6.9 MB
Chapter 4 - Logistic Regression/17. The Logistic Function.mp4 9.6 MB
Chapter 4 - Logistic Regression/18. Univariate Logistic Regression.mp4 31.6 MB
Chapter 4 - Logistic Regression/19. Univariate Logistic Regression with keras.mp4 12.5 MB
Chapter 5 - Simple FeedForward Neural Networks/20. Preliminary Concepts.mp4 13.4 MB
Chapter 5 - Simple FeedForward Neural Networks/21. First Project Non-Linear Synthetic Function Regression.mp4 13.6 MB
Chapter 5 - Simple FeedForward Neural Networks/22. Second Project Modeling Cars Fuel Efficiency with Non-Linear.mp4 15.6 MB
Chapter 5 - Simple FeedForward Neural Networks/23. Third Project Learning to Classify Wines Multiclass Classification.mp4 12.6 MB
Chapter 6 - Convolutional Neural Networks/24. Origin of Convolutional Neural Networks.mp4 5.4 MB
Chapter 6 - Convolutional Neural Networks/25. Applying Convolution in TensorFlow.mp4 17.9 MB
Chapter 6 - Convolutional Neural Networks/26. Subsampling Operation Pooling.mp4 10.9 MB
Chapter 6 - Convolutional Neural Networks/27. Improving Efficiency Dropout Operation.mp4 6.2 MB
Chapter 6 - Convolutional Neural Networks/28. Convolutional Type Layer Building Methods.mp4 2.9 MB
Chapter 6 - Convolutional Neural Networks/29. MNIST Digit Classification.mp4 17.9 MB
Chapter 6 - Convolutional Neural Networks/30. Image Classification with the CIFAR10 Dataset.mp4 12.9 MB
Chapter 7 - Recurrent Neural Networks and LSTM/31. Recurrent Neural Networks.mp4 6.5 MB
Chapter 7 - Recurrent Neural Networks and LSTM/32. A Fundamental Component Gate Operation and Its Steps.mp4 7.1 MB
Chapter 7 - Recurrent Neural Networks and LSTM/33. TensorFlow LSTM Useful Classes and Methods.mp4 3 MB
Chapter 7 - Recurrent Neural Networks and LSTM/34. Univariate Time Series Prediction with Energy Consumption Data.mp4 13.8 MB
Chapter 7 - Recurrent Neural Networks and LSTM/35. Writing Music a la Bach.mp4 44.9 MB
Chapter 8 - Deep Neural Networks/36. Deep Neural Network Definition and Architectures Through Time.mp4 4.9 MB
Chapter 8 - Deep Neural Networks/37. Alexnet.mp4 9.8 MB
Chapter 8 - Deep Neural Networks/38. Inception V3.mp4 1.8 MB
Chapter 8 - Deep Neural Networks/39. Residual Networks (ResNet).mp4 3.3 MB
Chapter 8 - Deep Neural Networks/40. Painting with Style VGG Style Transfer.mp4 15.4 MB
Chapter 9 - Library Installation And Additional Tips/41. Windows Installation.mp4 12.7 MB
Chapter 9 - Library Installation And Additional Tips/42. mac OS Installation.mp4 13.7 MB
Discuss.FreeTutorials.Us.html 165.7 KB
FreeCoursesOnline.Me.html 108.3 KB
FreeTutorials.Eu.html 102.2 KB
How you can help Team-FTU.txt 259 B

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