Deep Learning Using Keras - A Complete and Compact Guide for Beginners

Description

In this course, we will start with extremely basic concepts such as learning the programming language fundamentals and other supporting libraries. Then we will proceed with the core topics with the help of real-world datasets to gain a complete understanding of deep learning using Python and Keras.

The artificial intelligence domain is divided broadly into deep learning and machine learning. In fact, deep learning is machine learning itself but deep learning with its deep neural networks and algorithms tries to learn high-level features from data without human intervention. That makes deep learning the base of all future self-intelligent systems. This course begins with going over the basics of Python and then quickly moves on to important libraries of Python that are critical to data analysis and visualizations, such as NumPy, Pandas, and Matplotlib. After the basics, we will then install the deep learning libraries-Theano and TensorFlow-and the API for dealing with these called Keras. Then, before we jump into deep learning, we will have an elaborate theory session about the basic structure of artificial neuron and neural networks, and about activation functions, loss functions, and optimizers. Furthermore, we will create deep learning multi-layer neural network models for a text-based dataset and then convolutional neural networks for an image-based dataset. You will also learn how the basic CNN layers such as the convolution layer, the pooling layer, and the fully connected layer work. Then, we will use different techniques to improve the quality of a model and perform optimization using image augmentation. By the end of this course, you will have a complete understanding of deep learning and will be able to implement these skills in your own projects. The complete code bundle for this course is available at https://github.com/PacktPublishing/Deep-Learning-using-Keras---A-Complete-and-Compact-Guide-for-Beginners

Learn the basics of Python programming
Use different Python libraries such as NumPy, Matplotlib, and Pandas
Understand the basic structure of artificial neurons and neural networks
Explore activation functions, loss functions, and optimizers
Create deep learning multi-layer neural network models for a text-based dataset
Create convolutional neural networks for an image-based dataset

This course is designed for beginners who want to learn basic to advanced deep learning and have basic computer knowledge.

This course is a combination of theory and practical videos where we will understand the basics of Python programming, Python libraries, and deep learning concepts, and then use different datasets to understand deep learning using Keras.

Perform exploratory data analysis of the loaded data and prepare the data for giving it into the deep learning model * Learn how basic CNN layers such as the convolution layer, the pooling layer, and the fully connected layer work * Learn to use Google Colab to enhance parallel processing with VGGNet and ResNet models

https://github.com/PacktPublishing/Deep-Learning-using-Keras---A-Complete-and-Compact-Guide-for-Beginners

Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than 8 years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.

1. Course Introduction and Table of Contents

2. Introduction to AI and Machine Learning

3. Introduction to Deep learning and Neural Networks

4. Setting up Computer - Installing Anaconda

5. Python Basics

6. Numpy Basics

7. Matplotlib Basics

8. Pandas Basics

9. Installing Deep Learning Libraries

10. Basic Structure of Artificial Neuron and Neural Network

11. Activation Functions Introduction

12. Popular Types of Activation Functions

13. Popular Types of Loss Functions

14. Popular Optimizers

15. Popular Neural Network Types

16. King County House Sales Regression Model - Step 1 Fetch and Load Dataset

17. Step 2 and 3 EDA and Data Preparation

18. Step 4 Defining the Keras Model

19. Step 5 and 6 Compile and Fit Model

20. Step 7 Visualize Training and Metrics

21. Step 8 Prediction Using the Model

22. Heart Disease Binary Classification Model - Introduction

23. Step 1 - Fetch and Load Data

24. Step 2 and 3 - EDA and Data Preparation

25. Step 4 - Defining the model

26. Step 5 - Compile Fit and Plot the Model

27. Step 5 - Predicting Heart Disease using Model

28. Redwine Quality MultiClass Classification Model - Introduction

29. Step1 - Fetch and Load Data

30. Step 2 - EDA and Data Visualization

31. Step 3 - Defining the Model

32. Step 4 - Compile Fit and Plot the Model

33. Step 5 - Predicting Wine Quality using Model

34. Serialize and Save Trained Model for Later Use

35. Digital Image Basics

36. Basic Image Processing using Keras Functions

37. Keras Single Image Augmentation

38. Keras Directory Image Augmentation

39. Keras Data Frame Augmentation

40. CNN Basics

41. Stride, Padding and Flattening Concepts of CNN

42. Flowers CNN Image Classification Model - Fetch Load and Prepare Data

43. Flowers Classification CNN - Create Test and Train Folders

44. Flowers Classification CNN - Defining the Model

45. Flowers Classification CNN - Training and Visualization

46. Flowers Classification CNN - Save Model for Later Use

47. Flowers Classification CNN - Load Saved Model and Predict

48. Flowers Classification CNN - Optimization Techniques - Introduction

49. Flowers Classification CNN - Dropout Regularization

50. Flowers Classification CNN - Padding and Filter Optimization

51. Flowers Classification CNN - Augmentation Optimization

52. Hyper Parameter Tuning

53. Transfer Learning using Pretrained Models - VGG Introduction

54. VGG16 and VGG19 prediction

55. ResNet50 Prediction

56. VGG16 Transfer Learning Training Flowers Dataset

57. VGG16 Transfer Learning Flower Prediction

58. VGG16 Transfer Learning using Google Colab GPU - Preparing and Uploading Dataset

59. VGG16 Transfer Learning using Google Colab GPU - Training and Prediction

60. VGG19 Transfer Learning using Google Colab GPU - Training and Prediction

61. ResNet-50 Transfer Learning using Google Colab GPU - Training and Prediction