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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