by Brandon McKinzie | Released May 2018 | ISBN: 9781788472463

Get hands-on and create your own machine learning solutionsAbout This VideoIntroduction to building models for computer vision and natural language processing.Overview of the many utilities that TensorFlow has to offer, such as TensorBoard, Estimators, and more.Learn how models are made in production settings, and how to best structure your TensorFlow programs.In DetailGoogle's TensorFlow framework is the current leading software for implementing and experimenting with the algorithms that power AI and machine learning. Google deploys TensorFlow for many of its products, such as Translate and Maps.We will embark on this journey by quickly wrapping up some important fundamental concepts, followed by a focus on TensorFlow to complete tasks in computer vision and natural language processing. You will be introduced to some important tips and tricks necessary for enhancing the efficiency of our models. We will highlight how TensorFlow is used in an advanced environment and brush through some of the unique concepts at the cutting edge of practical AI. If you want to develop a solid foundation on using TensorFlow and continue your journey into advancing the state of the art in AI to create your own smart machine learning solutions, this course is for you.All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/-Learn-Artificial-Intelligence-with-TensorFlow Show and hide more

1. Chapter 1 : TensorFlow Setup and Fundamental Concepts
   • The Course Overview 00:07:07
   • Machine Learning Basics 00:06:39
   • TensorFlow Basics Part 1: Tensors and Variables 00:08:37
   • TensorFlow Basics Part 2: Graphs and Sessions 00:07:59
   • TensorFlow Basics Part 3: Training, Saving, and Loading 00:08:06
2. Chapter 2 : Computer Vision
   • Convolutional Neural Networks 00:08:44
   • Preprocessing, Pooling, and Batch Normalization 00:04:47
   • Training a CNN on CIFAR-10 – Part 1 00:08:17
   • Training a CNN on CIFAR-10 – Part 2 00:11:54
3. Chapter 3 : Natural Language Processing and Recurrent Neural Networks
   • Embeddings 00:22:15
   • Recurrent Neural Networks 00:10:34
   • Bidirectionality and Stacking RNNs 00:04:45
   • Models for Text Classification – Part 1 00:07:58
   • Models for Text Classification – Part 2 00:07:57
4. Chapter 4 : Tips and Tricks
   • TensorBoard 00:05:54
   • Working with Estimators 00:13:10
   • Training Tips 00:12:04
   • Debugging Strategies 00:08:05
5. Chapter 5 : TensorFlow in Production
   • Requirements for ML at Scale 00:09:11
   • TensorFlow with C++ 00:29:12
   • TensorFlow Serving 00:14:30
   • TensorFlow Lite 00:02:59
6. Chapter 6 : Miscellaneous Topics and Course Summary
   • TPUs 00:12:19
   • AutoML 00:07:06
   • TensorFlow Eager 00:12:10
   • Course Summary and Next Steps 00:08:10
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