by Nimish Narang, Luis Capelo | Released July 2018 | ISBN: 9781789343557

Learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applicationsAbout This VideoProvides detailed explanation of neural networks and deep learningMake predictions with a trained model and get to grips with TensorBoardPerfectly balances theory, hands-on demos, and assessmentsIn DetailWith this course, you'll learn how to train, evaluate, and deploy Tensorflow and Keras models as real-world web applications. After a hands-on introduction to neural networks and deep learning, you'll use a sample model to explore details of deep learning and learn to select the right layers that can solve a given problem. By the end of the course, you'll build a Bitcoin application that predicts the future price, based on historic and freely available information. Show and hide more

1. Chapter 1 : Introduction to Neural Networks and Deep Learning
   • Course Overview 00:04:02
   • Setting up your Environment 00:05:06
   • Lesson Overview 00:01:36
   • What are Neural Networks and Deep Learning? 00:06:37
   • Limitations of Deep Learning 00:05:05
   • Common Components and Operations of Neural Networks 00:06:04
   • Configuring a Deep Learning Environment 00:01:23
   • Installing Python 3 00:05:29
   • Installing TensorFlow, Keras and TensorBoard 00:09:36
   • Installing Jupyter, Notebooks, Pandas and NumPy 00:03:15
   • Installation Completion 00:04:16
   • Training a Neural Network with TensorFlow convolutional layer 00:05:07
   • Training a Neural Network with TensorFlow fully connected layer 00:02:43
   • Train a Neural Network with TensorFlow 00:06:07
   • Testing network performance with unseen data 00:03:51
   • Summary 00:01:23
2. Chapter 2 : Model Architecture
   • Lesson Overview 00:01:04
   • Choosing the Right Model Architecture 00:08:39
   • Data Normalization 00:05:25
   • Using Keras as a TensorFlow Interface 00:02:38
   • Designing a Model 00:05:11
   • Training a Model 00:03:57
   • Making Predictions 00:02:24
   • The Keras Paradigm 00:02:10
   • From Data Preparation to Modeling 00:05:34
   • Reshaping the Time-Series Data 00:09:20
   • Reshaping the Time-Series Data 00:03:52
   • Training a Model 00:02:53
   • Training a Model 00:01:17
   • Making Predictions 00:01:53
   • Overfitting 00:01:01
   • Summary 00:00:44
3. Chapter 3 : Model Evaluation and Optimization
   • Lesson Overview 00:01:13
   • Model Evaluation 00:10:15
   • Using TensorBoard 00:08:58
   • Implementing Model Evaluation Metrics 00:07:24
   • Evaluating Bitcoin Model 00:14:19
   • Model Predictions 00:11:55
   • Interpreting Predictions 00:05:47
   • Hyperparameter Optimization 00:09:49
   • Epochs Implementation 00:10:50
   • Regularization Strategies Implementation 00:06:48
   • Summary 00:01:27
4. Chapter 4 : Productization
   • Lesson Overview 00:01:34
   • Handling and Dealing with New Data 00:12:15
   • Re-Training an Old Model 00:12:46
   • Training a New Model 00:03:33
   • Deploying a Model as a Web Application 00:04:20
   • Building and executing a Docker run command 00:03:49
   • Deployment and using Cryptonic 00:08:59
   • Summary 00:01:38
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