by Alvaro Fuentes | Released March 2018 | ISBN: 9781788398756

Perform Advanced Machine Learning with TensorFlow with 19 hands-on recipesAbout This VideoLearn quickly how to build Deep Learning models with TensorFlow by following clear recipesLearn how to improve the performance and speed of your machine learning models by applying advanced Deep Learning techniquesLearn how to use Deep Learning and TensorFlow to solve valuable problems using real-world datasetsIn DetailDeep Learning models often perform significantly better than traditional machine learning algorithms in many tasks. This course consists of hands-on recipes to use deep learning in the context of supervised and unsupervised learning tasks.After covering the basics of working with TensorFlow, it shows you how to perform the traditional machine learning tasks in supervised learning: regression and classification. This course also covers how to perform unsupervised learning using cutting-edge techniques from Deep Learning. To address many different use cases, this product presents recipes for both the low-level API (TensorFlow core) as well as the high-level APIs (tf.contrib.lean and Keras).All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/TensorFlow-1.X-Recipes-for-Supervised-and-Unsupervised-Learning Show and hide more

1. Chapter 1 : TensorFlow Basics
   • The Course Overview 00:04:07
   • Set Up and Installing TensorFlow 00:08:05
   • Defining and Running a Computational Graph 00:13:04
   • Visualizing a Computational Graph With TensorBoard 00:07:50
   • How to Read Data From Files 00:11:25
   • The Hello World of Deep Learning – Your First Deep Neural Network 00:15:20
2. Chapter 2 : Supervised Learning With Deep Neural Networks
   • Building DNN Models for Regression With TensorFlow Core 00:13:47
   • Building DNN Models for Classification With TensorFlow Core 00:11:04
   • Performing Regularization in DNN Models 00:13:10
   • How to Work With Optimizers 00:06:31
3. Chapter 3 : Working with High-Level APIs
   • How to Use Keras for Building DNN 00:10:11
   • Performing Regression with Estimators API 00:13:03
   • Performing Classification with Estimators 00:07:40
   • Working with Other Models from Estimators API 00:09:50
   • Customizing DNN Models – Layers, Activations, Optimizers and Metrics 00:08:27
4. Chapter 4 : Unsupervised Learning with Deep Neural Networks
   • Building Autoencoders 00:14:06
   • How to Perform PCA for Dimensionality Reduction 00:08:34
   • Building a Restricted Boltzmann Machine 00:11:56
   • How to Perform Clustering 00:11:14
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