by Dan Van Boxel | Released June 2016 | ISBN: 9781786464491

Channel the power of deep learning with Google's TensorFlow! About This VideoExplore various possibilities with deep learning and gain amazing insights from data using Google's brainchild—TensorFlowWant to learn what more can be done with deep learning? Explore various neural networks with the help of this comprehensive guide!Rich in concepts, this is an advanced guide on deep learning that will give you the background to innovate in your environmentIn DetailWith deep learning going mainstream for making sense of data, getting accurate results using deep networks is possible. This video is your guide to explore possibilities with deep learning. It will enable you to understand data like never before. With efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights which would change how you look at data.With this video, you will dig your teeth deeper into the hidden layers of abstraction using raw data. This video will offer you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. During the video course, you will come across topics like logistic regression, convolutional neural networks, training deep networks, and so on. With the help of practical examples, the video will cover advanced multilayer networks, image recognition, and beyond.This course uses TensorFlow 0.8 and Python 3.5, while not the latest version available, it provides relevant and informative content for legacy users of TensorFlow, and Python. Show and hide more

1. Chapter 1 : Getting Started
   • The Course Overview 00:03:00
   • Installing TensorFlow 00:05:34
   • Simple Computations 00:05:32
   • Logistic Regression Model Building 00:06:59
   • Logistic Regression Training 00:04:53
2. Chapter 2 : Deep Neural Networks
   • Basic Neural Nets 00:05:17
   • Single Hidden Layer Model 00:05:06
   • Single Hidden Layer Explained 00:04:33
   • Multiple Hidden Layer Model 00:05:22
   • Multiple Hidden Layer Results 00:04:43
3. Chapter 3 : Convolutional Neural Networks
   • Convolutional Layer Motivation 00:05:04
   • Convolutional Layer Application 00:06:56
   • Pooling Layer Motivation 00:03:59
   • Pooling Layer Application 00:04:18
   • Deep CNN 00:06:29
   • Deeper CNN 00:04:08
   • Wrapping Up Deep CNN 00:04:56
4. Chapter 4 : Recurrent Neural Networks
   • Introducing Recurrent Neural Networks 00:09:03
   • skflow 00:09:19
   • RNNs in skflow 00:04:04
5. Chapter 5 : Wrapping Up
   • Research Evaluation 00:06:56
   • The Future of TensorFlow 00:04:19
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