English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 31m | 3.00 GB
Tips and tricks to improve your skills with Keras
Machine learning and deep learning allow us to interpret data structures and fit that data into models to identify patterns and make predictions. Keras makes this easier with its huge set of libraries that can be easily used for machine learning.
Designed for those with some existing Python and Keras skills and familiarity with machine learning principles, this course will enable you to enrich your skills by covering a number of more advanced applications. In this course, you will get hands-on experience in solving real problems by implementing cutting-edge techniques to significantly boost your Keras skills and, as a consequence, expand your ability to apply Keras to real-world problems. Throughout the course, you will work on real datasets to increase your expertise and keep adding new tools to your Keras toolbox.
By the end of this course, you will learn various tips, tricks, and techniques to upgrade your machine learning and deep learning algorithm knowledge, as well as how to build efficient models with Keras.
By the end of this course, you will have learned various tips, tricks, and techniques to upgrade your machine learning and deep learning algorithm knowledge; you will also be able to build efficient models with Keras.
Learn
- Run deep learning models with Keras and a TensorFlow backend
- Use image augmentation to improve training accuracy for your Keras models
- Learn how to generate articles with Recurrent Neural Networks in Keras
- Use Keras for Natural Language Processing
- Deploy Keras models into production
- Use the Keras Functional API for deep learning
Table of Contents
01 The Course Overview
02 Setting Up the Environment
03 Overview of Keras
04 Getting the Data and Training the Model
05 Packaging and Deploying Your Model
06 Connecting It to a Frontend Application
07 Creating and Training a Convolutional Neural Network Using Keras
08 Using Image Augmentation
09 Training a Model with Image Augmentation
10 Using Keras to Classify Videos
11 The Difference Between the Sequential and Functional APIs
12 Defining Simple Models Using the Functional API
13 Defining More Complex Models with Shared Layers
14 Defining Models with Multiple Inputs and Outputs
15 Introduction to Recurrent Neural Networks
16 Getting the Right Data Sets
17 Training a Text Generation Model
18 Testing out the Model
19 An Introduction to NLP and Keras
20 Building a Sentiment Analysis Engine with NLP
21 Training a Chatbot with NLP
22 Implementing Your Trained Chatbot
23 Overview of Some New Advancements
24 How to Use Keras Tuner
25 What Is AutoML
26 How to Use Keras AutoML
27 Options for Deploying Models
28 Creating a Model for Deployment
29 Creating a Flask App
30 Deploying to the Cloud
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