O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Beginning Application Development with TensorFlow and Keras

Video Description

Learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications

About This Video

  • Provides detailed explanation of neural networks and deep learning
  • Make predictions with a trained model and get to grips with TensorBoard
  • Perfectly balances theory, hands-on demos, and assessments

In Detail

With 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.

Table of Contents

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