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Beginning Application Development with TensorFlow and Keras

Beginning Application Development with TensorFlow and Keras

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by Nimish Narang
Publisher: Packt Publishing
Release Date: July 2018
ISBN: 9781789343557
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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