Complete And Compact Dummies Guide To Deep Learning Using Keras

Learn the basics of deep learning, the base of all future self intelligent systems, how to prepare a trained model, and how to predict using available data sets. Read more.

Abhilash

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Course Skill Level

Beginner

Time Estimate

5h 8m

I am a Post Graduate Masters Degree holder in Computer Science and Engineering with experience in Android/iOS Mobile and PHP/Python Web Developer Apps

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About This Course
Curriculum

About This Course

Who this course is for:

Beginner who wants to learn Basic to Advanced Deep Learning

What you’ll learn:

Deep Learning
Computer Vision
Keras
Machine Learning
Python

Requirements:

Basic computer knowledge and an interest to learn Deep Learning using Keras

Welcome to my new course ‘Complete And Compact Dummies Guide To Deep Learning Using Keras’.

As you already know, 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 try to learn high-level features from data without human intervention. That makes deep learning the base of all future self intelligent systems.

And in this course, I am starting from the very basic things like learning the programming language basics and other supporting libraries first, then proceeding with the core topic.

Let’s see what are the interesting topics included in this course. First, we will have an introductory theory session about Artificial Intelligence, Machine learning, Artificial Neurons based Deep Learning and Neural Networks.

After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the Anaconda package and will check and see if everything is installed fine. We will be using the browser based IDE called Jupyter notebook for our further coding exercises.

I know some of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions List and Tuples, Dictionaries, Functions etc.

Then, we will start with learning the basics of the Python Numpy library which is used to add support for large, multi-dimensional arrays and matrices, along with a large collection of classes and functions. Then we will learn the basics of the Matplotlib library which is a plotting library for Python for corresponding numerical expressions in NumPy. And finally, the pandas library which is a software library written for the Python programming language for data manipulation and analysis.

After the basics, we will then install the deep learning libraries theano, tensorflow and the API for dealing with these called as Keras. We will be writing all our future codes in Keras.

After completing this course, you will be provided with a course completion certificate which will add value to your portfolio.

Our Promise to You

By the end of this course, you will have learned Deep Learning using Keras.

10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about Deep Learning.

Course Curriculum

Section 1 - Introduction
	Course Introduction And Table Of Contents	00:00:00
	Course Resources	00:00:00
	Introduction To AI And Machine Learning	00:00:00
	Introduction To Deep Learning And Neural Networks	00:00:00
	Setting Up Computer – Installing Anaconda	00:00:00
Section 2 - Python Basics
	Python Basics – Assignment	00:00:00
	Python Basics – Flow Control – Part 1	00:00:00
	Python Basics – Flow Control – Part 2	00:00:00
	Python Basics – List And Tuples	00:00:00
	Python Basics – Dictionary And Functions – Part 1	00:00:00
	Python Basics – Dictionary And Functions – Part 2	00:00:00
Section 3 - Numpy Basics
	Numpy Basics – Part 1	00:00:00
	Numpy Basics – Part 2	00:00:00
Section 4 - Matplotlib Basics
	Matplotlib Basics – Part 1	00:00:00
	Matplotlib Basics – Part 2	00:00:00
Section 5 - Pandas Basics
	Pandas Basics – Part 1	00:00:00
	Pandas Basics – Part 2	00:00:00
Section 6 - Installing Deep Learning Libraries
	Installing Deep Learning Libraries	00:00:00
Section 7 - Basic Structure Of Artificial Neuron And Neural Network
	Basic Structure Of Artificial Neuron And Neural Network	00:00:00
Section 8 - Activation Functions
	Activation Functions Introduction	00:00:00
Section 9 - Popular Types Of Activation Functions
	Popular Types Of Activation Functions	00:00:00
Section 10 - Popular Types Of Loss Functions
	Popular Types Of Loss Functions	00:00:00
Section 11 - Popular Optimizers
	Popular Optimizers	00:00:00
Section 12 - Popular Neural Network Types
	Popular Neural Network Types	00:00:00
Section 13 - King County House Sales Regression Model
	King County House Sales Regression Model – Step 1 Fetch And Load Dataset	00:00:00
	Step 2 And 3 – EDA And Data Preparation – Part 1	00:00:00
	Step 2 And 3 – EDA And Data Preparation – Part 2	00:00:00
	Step 4 Defining The Keras Model – Part 1	00:00:00
	Step 4 Defining The Keras Model – Part 2	00:00:00
	Step 5 And 6 Compile And Fit Model	00:00:00
	Step 7 Visualize Training And Metrics	00:00:00
	Step 8 Prediction Using The Model	00:00:00
Section 14 - Heart Disease Binary Classification Model
	Heart Disease Binary Classification Model – Introduction	00:00:00
	Step 1 – Fetch And Load Data	00:00:00
	Step 2 And 3 – EDA And Data Preparation – Part 1	00:00:00
	Step 2 And 3 – EDA And Data Preparation – Part 2	00:00:00
	Step 4 – Defining The Model	00:00:00
	Step 5 – Compile Fit And Plot The Model	00:00:00
	Step 5 – Predicting Heart Disease Using Model	00:00:00
Section 15 - Red Wine Quality Multiclass Classification Model
	Red Wine Quality Multiclass Classification Model – Introduction	00:00:00
	Step1 – Fetch And Load Data	00:00:00
	Step 2 – EDA And Data Visualization	00:00:00
	Step 3 – Defining The Model	00:00:00
	Step 4 – Compile Fit And Plot The Model	00:00:00
	Step 5 – Predicting Wine Quality Using Model	00:00:00
Section 16 - Serialize And Save Trained Model For Later Use
	Serialize And Save Trained Model For Later Use	00:00:00
Section 17 - Digital Image Basics
	Digital Image Basics	00:00:00
	Basic Image Processing Using Keras Functions – Part 1	00:00:00
	Basic Image Processing Using Keras Functions – Part 2	00:00:00
	Basic Image Processing Using Keras Functions – Part 3	00:00:00
Section 18 - Keras Image Augmentation
	Keras Single Image Augmentation – Part 1	00:00:00
	Keras Single Image Augmentation – Part 2	00:00:00
	Keras Directory Image Augmentation	00:00:00
	Keras Data Frame Augmentation	00:00:00
Section 19 - CNN Basics
	CNN Basics	00:00:00
	Stride Padding And Flattening Concepts Of CNN	00:00:00
Section 20 - Flowers CNN Image Classification Model
	Fetch Load And Prepare Data	00:00:00
	Create Test And Train Folders	00:00:00
	Defining The Model – Part 1	00:00:00
	Defining The Model – Part 2	00:00:00
	Defining The Model – Part 3	00:00:00
	Training And Visualization	00:00:00
	Save Model For Later Use	00:00:00
	Load Saved Model And Predict	00:00:00
	Optimization Techniques – Introduction	00:00:00
	Dropout Regularization	00:00:00
	Padding And Filter Optimization	00:00:00
	Augmentation Optimization	00:00:00
Section 21 - Hyper Parameter Tuning
	Hyper Parameter Tuning – Part 1	00:00:00
	Hyper Parameter Tuning – Part 2	00:00:00
Section 22 - Transfer Learning Using Pretrained Models - VGG
	Transfer Learning Using Pretrained Models – VGG Introduction	00:00:00
	VGG16 And VGG19 Prediction – Part 1	00:00:00
	VGG16 And VGG19 Prediction – Part 2	00:00:00
Section 23 - ResNet50 Prediction
	ResNet50 Prediction	00:00:00
Section 24 - VGG16 Transfer Learning Training Flowers Dataset
	VGG16 Transfer Learning Training Flowers Dataset – Part 1	00:00:00
	VGG16 Transfer Learning Training Flowers Dataset – Part 2	00:00:00
Section 25 - VGG16 Transfer Learning Flower Prediction
	VGG16 Transfer Learning Flower Prediction	00:00:00
Section 26 - VGG16 Transfer Learning using Google Colab GPU - Preparing And Uploading Dataset
	Preparing And Uploading Dataset	00:00:00
	VGG16 Transfer Learning using Google Colab GPU – Training And Prediction	00:00:00
	VGG19 Transfer Learning using Google Colab GPU – Training And Prediction	00:00:00
Section 27 - ResNet-50 Transfer Learning using Google Colab GPU - Training And Prediction
	ResNet50 Transfer Learning Using Google Colab GPU – Training And Prediction	00:00:00