23 OCT 2025 - We are back!
If you have been following us over the last few years, you will know that the last 2 months have been rough.
We website was practically not loading.
Sorry for the mess. We are back though and everything should run smoothly now. New servers. Updated domains. And new owners.
We invite you all to start uploading torrents again!

TORRENT DETAILS

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

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	5bbdee8f38374cd9b9b92e4dda91d66d5889aae6
Size:	592 MB in 46 files
Release Date(s):	Torrent Release: 13-12-2018 by user
Swarm:	0 Seeds & 2 Peers
Vote:	No votes yet.

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.

For More Udemy Free Courses >>> http://www.freetutorials.eu

For more Lynda and other Courses >>> https://www.freecoursesonline.me/

Our Forum for discussion >>> https://discuss.freetutorials.eu/

VISITOR COMMENTS (0

)

FILE LIST

Filename		Size
	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

RELATED DOWNLOADS

Torrent Name

Added

Size

Seeds

Peers

Uploader

Health