by Arish Ali | Released December 2018 | ISBN: 9781789613155

A quick and easy way for JavaScript developers to begin their journey with machine learningAbout This VideoCovers a broad range of topics in machine learning including the trending areas of Deep Neural Networks and Convolutional Neural Networks A hands-on application-focused course that provides an intuitive understanding of many machine learning algorithmsLearn to build and train different machine learning models on the browser or a node js serverIn DetailMachine learning is a growing and in-demand skill, but so far JavaScript developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. TensorFlow.js is a great way to begin learning machine learning in the browser with TensorFlow.js. It allows you to operate offline to train new models and retrain existing models.This course covers most of the major topics in machine learning and explains them with the help of Tensorflow.js implementations. The course is focused on the result-oriented nature of most JavaScript developers, and focuses on Tensorflow.js to the fullest in the least amount of time.At the end of the course, you'll evaluate and implement the right model to design smarter applications. The code files are placed at: https://github.com/PacktPublishing/Hands-on-Machine-Learning-with-TensorFlow.jsDownloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you. Show and hide more

1. Chapter 1 : Getting Ready for Machine Learning
   • The Course Overview 00:01:40
   • Introduction to Machine Learning 00:03:48
   • Getting Started with TensorFlow.js Using a Simple Example to Predict Weight 00:03:38
   • Setting Up Our Machine Learning Environment 00:07:56
2. Chapter 2 : Using Supervised Learning for Predictions
   • Types of Supervised Learning 00:03:20
   • Applying Regression 00:06:31
   • Predicting Salaries after College Using TensorFlow 00:07:06
   • Applying Classification 00:03:50
   • Predicting Mental Health Issues Using Logistic Regression 00:06:03
3. Chapter 3 : Deep Neural Networks
   • Understanding Simple Neural Networks 00:02:04
   • Concepts in Neural Network 00:03:51
   • Working with Deep Neural Networks 00:05:28
   • Image Classification Using Neural Networks 00:08:20
4. Chapter 4 : Making Our Models Better
   • Model Evaluation 00:04:14
   • Better Measures than Accuracy 00:05:13
   • Improving the Models 00:06:14
   • Optimizing the Models 00:04:13
5. Chapter 5 : Building Advance Models Easily with Layers
   • Using High-Level Layers API to Construct Neural Networks 00:07:35
   • Building Advanced Neural Networks with Layers Easily 00:04:40
   • Detecting Digits Using Layers 00:08:13
   • Building A Classifier Using Layers 00:04:22
6. Chapter 6 : Handling Models in TensorFlow
   • Importing a Keras Model into TensorFlow.js 00:03:49
   • Saving and Loading TensorFlow Models 00:06:17
   • Importing TensorFlow SavedModel into TensorFlow.js 00:04:23
   • Playing PAC-MAN Using a Webcam 00:07:17
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