English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 33m | 336 MB
Deep Learning lies at the heart of many leading machine learning and artificial intelligence applications. This course, Deep Learning with Keras, shows you how to use Keras to quickly create powerful deep neural networks.
There has been a revolution in artificial intelligence (AI) and machine learning, and deep learning-based solutions are leading the charge. Implementing these solutions can be tedious to create and require you to write many lines of complex code. Keras is a library that makes it much easier for you to create these deep learning solutions. In a few lines of code, you can create a model that could require hundreds of lines of conventional code. This course, Deep Learning with Keras, will get you up to speed with both the theory and practice of using Keras to implement deep neural networks. First, you will dive deep into learning how Keras implements various layers of neurons quickly and easily, with each layer defining the specific functionality needed to implement parts of your solution. Next, you will discover how to use Keras’ various methods for interconnecting these layers to form the structure of your deep neural networks. Finally, you will learn how you use Keras to implement several state-of-the-art neural networks, such as the widely used Convolutional and Recurrent Neural Networks, to make these concepts come to life. By the end of this course, you will gain the skills and experience required to effectively create deep neural networks through the course’s combination of lecture and hands-on coding.
Table of Contents
Course Overview
1 Course Overview
Introducing Keras
2 Introduction
3 What Is Keras
4 Neural Networks
5 Skills
6 Course Structure
Creating Your First Neural Network with Keras
7 Introduction to Installation
8 Installing TensorFlow
9 Installing Keras
10 Creating Your First Keras Neural Network
11 Changing the Backend
Constructing Models in Keras
12 Introduction to Models
13 Coding Complex Data
14 Estimating Layers and Neurons
15 Model Features
16 Converting to the Functional API
17 Summary
Employing Layers in Keras Models
18 Introduction to Models
19 Layer Groups
20 Common Layers
21 Shaping Layers
22 Merging Layers
23 Extension Layers
24 Summary
Building Convolutional NN with Keras
25 Introduction to CNNs
26 Why Do CNNs Exists
27 How Do CNNs Work
28 Convolution Layer
29 Convolution Layer Hyperparameters
30 Non-linear Activation
31 Pool Layer
32 Implementing CNNs in Keras
33 Coding Fashion MNIST
34 Transfer Learning Principles
35 Transfer Learning Implementation
36 Conclusion
Implementing Recurrent Neural Nets with Keras
37 Introduction to RNNs
38 RNN Implementation
39 Simple RNN Issues
40 LSTM and GRU
41 Keras RNN Layers
42 Coding Sentiment Analysis
43 Summary
Using Specialty Layers and Functions
44 Introduction
45 Keras Datasets
46 Keras Pre-trained Models
47 Functional API Second Look
Last Words
48 Last Words
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