by Abhilash Nelson | Released May 2019 | ISBN: 9781838986476

Deep learning and data science using a Python and Keras library - A complete guide to take you from a beginner to professionalAbout This VideoLearn data science using a Python and Keras libraryLearn convolutional neural networks using PythonIn DetailThe world has been obsessed with the terms machine learning and deep learning recently. We use these technologies every day with or without our knowledge through Google suggestions, translations, ads, movie recommendations, friend suggestions, and sales and customer experiences. There are tons of other applications too! No wonder that deep learning and machine learning specialists, along with data science practitioners, are the most sought-after talent in the technology world. However, it's a common misconception that you need to study lots of mathematics, statistics, and complex algorithms for learning these technologies. It's like believing that you must learn the working of a combustion engine before you learn how to drive a car. A basic know-how of the internal working of the engine is of course an added advantage, but it's not mandatory.Similarly, this course is a perfect balance between learning the basic deep learning concepts and implementing the built-in deep learning classes and functions from the Keras library using the Python programming language. These classes, functions and APIs are just like the control pedals of a car engine, which you can use to build an efficient deep-learning model. This is a basic-to-advanced crash course in deep learning, neural networks, and convolutional neural networks using Keras and Python. It'll help your skill up to meet the demand of the tech world and skyrocket your career prospects. Show and hide more Publisher resources Download Example Code

1. Chapter 1 : Course Intro and Table of Contents
   • Course Intro and Table of Contents 00:08:59
2. Chapter 2 : Deep Learning Overview
   • Deep Learning Overview 00:12:27
3. Chapter 3 : Chosing ML or DL for your project
   • Chosing ML or DL for your project 00:08:54
4. Chapter 4 : Preparing Your Computer
   • Preparing Your Computer 00:12:52
5. Chapter 5 : Python Basics
   • Python Basics 00:34:38
6. Chapter 6 : Installing Theano Library and Sample Program to Test
   • Installing Theano Library and Sample Program to Test 00:10:51
7. Chapter 7 : TensorFlow library Installation and Sample Program to Test
   • TensorFlow library Installation and Sample Program to Test 00:09:06
8. Chapter 8 : Keras Installation and Switching Theano and TensorFlow Backends
   • Keras Installation and Switching Theano and TensorFlow Backends 00:09:13
9. Chapter 9 : Multi-Layer Perceptron Concepts
   • Multi-Layer Perceptron Concepts 00:03:01
10. Chapter 10 : Training Neural Network - Steps and Terminology
    • Training Neural Network - Steps and Terminology 00:10:06
11. Chapter 11 : First Neural Network with Keras - Understanding Pima Indian Dataset
    • First Neural Network with Keras - Understanding Pima Indian Dataset 00:06:40
12. Chapter 12 : Training and Evaluation Concepts Explained
    • Training and Evaluation Concepts Explained 00:11:15
13. Chapter 13 : Pima Indian Model - Steps Explained
    • Pima Indian Model - Steps Explained 00:36:05
14. Chapter 14 : Pima Indian Model - Performance Evaluation
    • Pima Indian Model - Performance Evaluation 00:35:21
15. Chapter 15 : Understanding Iris Flower Dataset
    • Understanding Iris Flower Dataset 00:07:43
16. Chapter 16 : Developing the Iris Flower Model
    • Developing the Iris Flower Model 00:24:02
17. Chapter 17 : Understanding the Sonar Returns Dataset
    • Understanding the Sonar Returns Dataset 00:07:18
18. Chapter 18 : Developing the Sonar Returns Model
    • Developing the Sonar Returns Model 00:09:37
19. Chapter 19 : Sonar Model Perfomance Improvement
    • Sonar Model Perfomance Improvement 00:27:32
20. Chapter 20 : Understanding the Boston Housing Dataset
    • Understanding the Boston Housing Dataset 00:06:41
21. Chapter 21 : Developing the Boston Housing Baseline Model
    • Developing the Boston Housing Baseline Model 00:07:54
22. Chapter 22 : Boston Performance Improvement
    • Boston Performance Improvement 00:16:02
23. Chapter 23 : Save the Trained Model as JSON File (Pima Indian Dataset)
    • Save the Trained Model as JSON File (Pima Indian Dataset) 00:16:59
24. Chapter 24 : Save and Load Model as YAML File - Pima Indian Dataset
    • Save and Load Model as YAML File - Pima Indian Dataset 00:04:56
25. Chapter 25 : Load and Predict using the Pima Indian Model
    • Load and Predict using the Pima Indian Model 00:08:45
26. Chapter 26 : Save Load and Predict using Iris Flower Dataset
    • Save Load and Predict using Iris Flower Dataset 00:08:24
27. Chapter 27 : Save Load and Predict using Sonar Dataset
    • Save Load and Predict using Sonar Dataset 00:09:37
28. Chapter 28 : Save Load and Predict using Boston Dataset
    • Save Load and Predict using Boston Dataset 00:07:47
29. Chapter 29 : Checkpointing Models
    • Checkpointing Models 00:23:59
30. Chapter 30 : Plotting Model Behaviour History
    • Plotting Model Behaviour History 00:08:52
31. Chapter 31 : Dropout Regularisation
    • Dropout Regularisation 00:23:14
32. Chapter 32 : Learning Rate Schedule using Ionosphere Dataset
    • Learning Rate Schedule using Ionosphere Dataset 00:40:07
33. Chapter 33 : Convolutional Neural Networks – Introduction
    • Convolutional Neural Networks – Introduction 00:16:16
34. Chapter 34 : Downloading the MNIST Handwritten Digit Dataset
    • Downloading the MNIST Handwritten Digit Dataset 00:16:15
35. Chapter 35 : Multi-Layer Perceptron Model using MNIST
    • Multi-Layer Perceptron Model using MNIST 00:16:26
36. Chapter 36 : Convolutional Neural Network Model using MNIST
    • Convolutional Neural Network Model using MNIST 00:13:12
37. Chapter 37 : Convolutional Neural Network Model using MNIST - Part 2
    • Convolutional Neural Network Model using MNIST - Part 2 00:11:53
38. Chapter 38 : Large CNN using MNIST
    • Large CNN using MNIST 00:08:47
39. Chapter 39 : Load Save and Predict using MNIST
    • Load Save and Predict using MNIST 00:13:50
40. Chapter 40 : Introduction to Image Augmentation using Keras
    • Introduction to Image Augmentation using Keras 00:10:45
41. Chapter 41 : Augmentation using Sample Wise Standardization
    • Augmentation using Sample Wise Standardization 00:09:57
42. Chapter 42 : Augmentation using Feature Wise Standardization and ZCA Whitening
    • Augmentation using Feature Wise Standardization and ZCA Whitening 00:04:18
43. Chapter 43 : Augmentation using Rotation and Flipping
    • Augmentation using Rotation and Flipping 00:04:23
44. Chapter 44 : Saving Augmentation for MNIST
    • Saving Augmentation for MNIST 00:05:24
45. Chapter 45 : CIFAR-10 Object Recognition Dataset - Understanding and Loading
    • CIFAR-10 Object Recognition Dataset - Understanding and Loading 00:12:12
46. Chapter 46 : Simple CNN using CIFAR-10 Dataset
    • Simple CNN using CIFAR-10 Dataset 00:09:25
47. Chapter 47 : Simple CNN using CIFAR-10 Dataset - Part 2
    • Simple CNN using CIFAR-10 Dataset - Part 2 00:06:10
48. Chapter 48 : Simple CNN using CIFAR-10 Dataset – Coding
    • Simple CNN using CIFAR-10 Dataset – Coding 00:07:45
49. Chapter 49 : Train and Save CIFAR-10 Model
    • Train and Save CIFAR-10 Model 00:08:27
50. Chapter 50 : Load and Predict using CIFAR-10 CNN Model
    • Load and Predict using CIFAR-10 CNN Model 00:15:37
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