by Tom Joy | Released November 2018 | ISBN: 9781788475518

Apply Deep Learning to different data types and solve real-world problems with TensorFlowAbout This VideoTake the theory and apply it to create networks to classify sentence polarity, recognize handwritten digits, and then locate objects in an image.Learn the fundamentals of deep learning, to get a strong foundation.Combines an easily understandable explanation of deep learning coupled with a handful of implementations using the TensorFlow package.In DetailWe will not only get you up-and-running with deep learning, but also equip you with the skills to implement your own neural networks and apply them to the real world.We will use TensorFlow, an efficient Python library used to create and train our neural networks. You'll learn the skills to implement their architecture quickly and efficiently without having to deal with minutiae.You can rely on our expert guidance while learning the basic theory, backed up with relevant examples. We provide examples of neural networks, which you can use to highlight the key features. We then build up to more advanced networks. You'll learn to utilize a Convolutional Neural Network to classify images of handwritten text and then take your CNN further to perform object detection and localization in an image.This course will quickly get you past the fundamentals of TensorFlow; you'll go on to more exciting things such as implementing a variety of image recognition tasks. All the code and this course's supporting files are available on GitHub at - https://github.com/PacktPublishing/Getting-Started-with-TensorFlow-for-Deep-Learning- Show and hide more

1. Chapter 1 : An Introduction to Deep Learning and TensorFlow
   • The Course Overview 00:02:03
   • What Is Deep Learning? 00:07:05
   • Why Is Deep Learning Useful? 00:04:03
   • Activation Functions 00:04:11
   • Training Neural Networks 00:07:34
2. Chapter 2 : Getting Started with TensorFlow
   • Installing TensorFlow 00:02:04
   • Creating the Training Dataset 00:06:27
   • Creating the Models 00:11:07
   • Training the Model 00:11:14
   • Visualization and Evaluation 00:03:06
3. Chapter 3 : Implementing Your First Neural Network
   • Loading the Dataset 00:09:57
   • Defining the Model and Estimator 00:13:40
   • Training 00:02:39
   • Evaluation and Visualization 00:05:13
4. Chapter 4 : Handwritten Digit Classification
   • Introduction to CNNs 00:06:48
   • Loading the Dataset 00:07:13
   • Constructing the Classifier – Part One 00:09:29
   • Constructing the Classifier – Part Two 00:05:52
   • Training the Model 00:05:11
   • Testing and Results 00:05:25
5. Chapter 5 : Object Detection and Classification
   • Testing the Pre-Trained Model With Object Detection API 00:12:54
   • Creating a Cats and Dogs Dataset 00:06:07
   • Training Your New Model 00:10:33
   • Deploying Your New Model 00:05:17
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