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What you'll learn?
- Computer Vison and Image Recognition Specific Deep Learning and Convolutional Neural Networks using Python for Beginners
Course Overview
Hello and welcome to my new course "Computer Vision & Deep Learning in Python: From Novice to Expert"
Making a computer classify an image using Deep Learning and Neural Networks is comparatively easier than it was before. Using all these ready made packages and libraries will few lines of code will make the process feel like a piece of cake.
Its just like driving a big fancy car with an automatic transmission. You just only have to know how to use the basic controls to drive it. But, if you are a true engineer, you will also be fascinated about the internal working of the engine. In an expert level, you should be able to build your own version of that car from the scratch using the available basic components. Even-though the performance may not match the commercial production line version, the experience knowledge you gain from it cannot be explained in words.
And only because of this we have our course divided into exactly two halves. In the first half we will learn the working concepts of image recognition using computer vision and deep learning and will try to implement the simple versions of popular algorithms and techniques using plain python code. In the next half we will use the popular packages and libraries to implement more complex deep learning image classification models.
Here is a quick list of sessions that are included in this course.
The first three sessions will be theory sessions in which we will have overview about the concepts of deep learning and neural networks. We will also discuss the basics about a digital image and its composition
Then we will prepare your computer by installing and configuring Anaconda, the free and open-source Python data science platform and the other dependencies to proceed with our exercises.
If you are new to python programming, don't worry. The next four sessions will be covering the basics of python program with simple examples.
And here comes the aforementioned first half with our own custom code and libraries.
In the coming two theory sessions we will be covering the basics of image classification and the list of datasets that we are planning to cover in this course.
Then we will do a step by step custom implementation of The k-nearest neighbours (KNN) algorithm. It is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both non-linear classification and regression problems. We will use our own created classes and methods without using any external library. The theory sessions involve learning the KNN basics. Then we will go ahead with downloading the dataset, loading, preprocessing and splitting the data. We will try to train the program and will do an image classification among the three set of animals. Dogs, cats and pandas prediction using our custom KNN implementation.
Now we will proceed with Linear Classification. Starting with the Concept and Theory, we will proceed further with building our own scoring function and also implementing it using plain python code. Later we will discuss about the loss function concepts and also the performance optimization concepts and the terminology associated with it.
Then will start with the most important optimization algorithm for deep learning which is the Gradient Decent. We will have separate elaborate sessions where we will learn the concept and also implementation using the custom code for Gradient Decent. Later we will proceed with the more advanced Stochastic Gradient Decent with its concepts in the first sessions, later with implementing it using the custom class and methods we created.
We will then look at regularization techniques that can also be used for enhancing the performance and also will implement it with our custom code.
In the coming sessions, we will have Perceptron, which is a fundamental unit of the neural network which takes weighted inputs, process it and is capable of performing binary classifications. We will discuss the working of the Perceptron Model. Will implement it using Python and also we will try to do some basic prediction exercises using the preceptron we created.
In deep learning, back-propagation is a widely used algorithm in training feed-forward neural networks for supervised learning. We will then have a discussion about the mechanism of backward propagation of errors. Then to implement this concept, we will create our own classes and later implementation projects for a simple binary calculation dataset and also the MNIST optical character recognition dataset.
And with all the knowledge from the pain of making custom implementations. We can now proceed with the second half of deep learning implementation using the libraries and packages that are used for developing commercial Computer Vision Deep Learning programs
We will be using Keras which is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Theano and also other languages for creating deep learning applications
At first we will build a simple Neural Network implementation with Keras using the MNIST Optical Character Recognition Dataset. We will train and evaluate this neural network to obtain the accuracy and loss it got during the process.
In deep learning and Computer Vision, a convolutional neural network is a class of deep neural networks, most commonly applied to analysing visual imagery. At first we will have a discussion about the steps and layers in a convolutional neural network. Then we will proceed with creating classes and methods for a custom implementation of Convolutional neural network using the Keras Library which features different filters that we can use for images.
Then we will have a quick discussion about the CNN Design Best Practices and then will go ahead with ShallowNet. The basic and simple CNN architecture. We will create the common class for implementing ShallowNet and later will train and evaluate the ShallowNet model using the popular Animals as well as CIFAR 10 image datasets. Then we will see how we can serialize or save the trained model and then later load it and use it. Even-though a very shallow network, we will try to do prediction for an image we give using shallowNet for both the Animals and CIFAR 10 dataset
After that we will try famous CNN architecture called 'LeNet' for handwritten and machine-printed character recognition. For LeNet also, will create the common class and later will train, evaluate and save the LeNet model using the MNIST dataset. Later we will try to do prediction for a hand written digit image.
Then comes the mighty VGGNet architecture. We will create the common class and later will train, evaluate and save the VGGNet model using the CIFAR-10 dataset. After hours of training, later we will try to do prediction for photos of few common real-life objects falling in the CIFAR-10 categories.
While training deep networks, it is helpful to reduce the learning rate as the number of training epochs increases. We will learn a technique called as Learning Rate Scheduling in our next session and implement it in our python code.
Since we are spending hours to train a model, if we don't checkpoint our training models at the end of a job, there is a great chance that we'll have lost all of our hard earned results! We will see how we can efficiently do that in the coming sessions.
Enough with training using our little computer. Lets go ahead with popular Deep learning models already pre-trained for us which are included in Keras library. They are trained on Imagenet data which is a collection of image data containing 1000 categories of images.
The first pre-trained model that we are dealing with is the VGGNet-16, we will download the already trained model and then do the prediction. Later will go a bit deeper with VGGNet-19 pre-trained model and will do the image classification prediction.
The next pre-trained model that we are using is the ResNet, which can utilize a technique called skip connections, or shortcuts to jump over some layers. We will do the image classification prediction with this network too.
Finally, we will get the Inception and Xception models. Which are convolutional neural networks trained on more than a million images from the ImageNet database. They learn by using Depthwise Separable Convolutions. We will download the weights and do the image classification prediction with this network too.
Overall, this course will be the perfect recipe of custom and ready-made components that you can use for your career in Computer Vision using Deep Learning.
All the example code and sample images with dataset can be downloaded from the link included in the last session or resource section of this course.
We will also provide you with a course completion certificate once you are done with all the sessions and it will add great value to your career.
So best wishes and happy learning. See you soon in the class room.
Bibliography & Reference Credits:
* CS231M ・ Stanford University, CS231N ・ Stanford University
* pyimagesearch blog by Dr. Adrian Rosebrock, PhD.
* Deep Learning for Computer Vision : Dr. Adrian Rosebrock, PhD.
* Andrej Karpathy. CS231n: Convolutional Neural Networks for Visual Recognition.
* AndrejKarpathy.LinearClassification
* Machine Learning is Fun! Adam Geitgey
* Andrew Ng. Machine Learning
* Andrej Karpathy. Optimization
* Karen Simonyan and Andrew Zisserman. "Very Deep Convolutional Networks for Large-
Scale Image Recognition"
Intro Background Video Credits:
* Machine Learning: Living in the Age of AI
Pre-requisites
- A medium configuration computer and the willingness to indulge in the world of Deep Learning
Target Audience
- Beginners who are interested in becoming experts in Deep Learning and Computer Vision using Python
Curriculum 105 Lectures 13:35:46
-
Section 1 : Course Introduction and Table of Contents
-
Section 2 : Introduction to Deep Learning
- Lecture 1 :
- Introduction to Deep Learning
-
Section 3 : Introduction to Neural Networks
- Lecture 1 :
- Introduction to Neural Networks
-
Section 4 : Image Basics
- Lecture 1 :
- Image Basics - Part 1
- Lecture 2 :
- Image Basics - Part 2
-
Section 5 : Preparing your computer - Installing Anaconda
- Lecture 1 :
- Preparing your computer - Installing Anaconda
-
Section 6 : Preparing your computer - Installing Dependencies
- Lecture 1 :
- Preparing your computer - Installing Dependencies
-
Section 7 : Python Basics
- Lecture 1 :
- Python Basics - Assignment
- Lecture 2 :
- Python Basics - Flow Control
- Lecture 3 :
- Python Basics - Functions
- Lecture 4 :
- Python Basics - Data Structures
-
Section 8 : Load and Show Image
- Lecture 1 :
- Load and Show Image
-
Section 9 : Image Classification Basics
- Lecture 1 :
- Image Classification Basics - Part 1
- Lecture 2 :
- Image Classification Basics - Part 2
-
Section 10 : List of Popular Datasets Included
- Lecture 1 :
- List of Popular Datasets Included
-
Section 11 : KNN Image Classifier - Downloading Animals Dataset
- Lecture 1 :
- KNN Image Classifier - Downloading Animals Dataset
-
Section 12 : Creating Common Pre-processor
- Lecture 1 :
- Creating Common Pre-processor
-
Section 13 : Creating Common Loader
- Lecture 1 :
- Creating Common Loader - Part 1
- Lecture 2 :
- Creating Common Loader - Part 2
-
Section 14 : KNN Basics
- Lecture 1 :
- KNN Basics - Part 1
- Lecture 2 :
- KNN Basics - Part 2
-
Section 15 : KNN Implementation - Load and Process
- Lecture 1 :
- KNN Implementation - Load and Process - Part 1
- Lecture 2 :
- KNN Implementation - Load and Process - Part 2
- Lecture 3 :
- KNN Implementation - Load and Process - Part 3
-
Section 16 : KNN Implementation - Splitting the Dataset
- Lecture 1 :
- KNN Implementation - Splitting the Dataset
-
Section 17 : KNN Implementation - Training and Evaluation
- Lecture 1 :
- KNN Implementation - Training and Evaluation - Part 1
- Lecture 2 :
- KNN Implementation - Training and Evaluation - Part 2
-
Section 18 : KNN Prediction
- Lecture 1 :
- KNN Prediction - Part 1
- Lecture 2 :
- KNN Prediction - Part 2
-
Section 19 : Introduction to Linear Classification
- Lecture 1 :
- Introduction to Linear Classification - Part 1
- Lecture 2 :
- Introduction to Linear Classification - Part 2
-
Section 20 : Scoring Function Basics
- Lecture 1 :
- Scoring Function Basics - Part 1
- Lecture 2 :
- Scoring Function Basics - Part 2
-
Section 21 : Scoring Function - Implementation
- Lecture 1 :
- Scoring Function - Implementation - Part 1
- Lecture 2 :
- Scoring Function - Implementation - Part 2
-
Section 22 : Loss Function Basics
- Lecture 1 :
- Loss Function Basics
-
Section 23 : Optimization Concept Terminology and Challenges
- Lecture 1 :
- Optimization Concept Terminology and Challenges - Part 1
- Lecture 2 :
- Optimization Concept Terminology and Challenges - Part 2
-
Section 24 : Gradient Descent Implementation
- Lecture 1 :
- Gradient Descent Implementation - Part 1
- Lecture 2 :
- Gradient Descent Implementation - Part 2
- Lecture 3 :
- Gradient Descent Implementation - Part 3
- Lecture 4 :
- Gradient Descent Implementation - Part 4
- Lecture 5 :
- Gradient Descent Implementation - Part 5
-
Section 25 : Stochastic Gradient Descent Implementation
- Lecture 1 :
- Stochastic Gradient Descent Implementation - Part 1
- Lecture 2 :
- Stochastic Gradient Descent Implementation - Part 2
-
Section 26 : Introduction to Regularization
- Lecture 1 :
- Introduction to Regularization
-
Section 27 : Implementing Regularization
- Lecture 1 :
- Implementing Regularization
-
Section 28 : Introduction to Perceptrons
- Lecture 1 :
- Introduction to Perceptrons - Part 1
- Lecture 2 :
- Introduction to Perceptrons - Part 2
-
Section 29 : Perceptron Implementation: Creating Class
- Lecture 1 :
- Perceptron Implementation: Creating Class - Part 1
- Lecture 2 :
- Perceptron Implementation: Creating Class - Part 2
- Lecture 3 :
- Perceptron Implementation: Creating Class - Part 3
-
Section 30 : Perceptron Implementation: Creating BitWise Evaluation Program
- Lecture 1 :
- Perceptron Implementation: Creating BitWise Evaluation Program - Part 1
- Lecture 2 :
- Perceptron Implementation: Creating BitWise Evaluation Program - Part 2
-
Section 31 : Introduction to Back Propagation
- Lecture 1 :
- Introduction to Back Propagation - Part 1
- Lecture 2 :
- Introduction to Back Propagation - Part 2
-
Section 32 : Back Propagation Implementation - Creating Class
- Lecture 1 :
- Back Propagation Implementation - Creating Class - Part 1
- Lecture 2 :
- Back Propagation Implementation - Creating Class - Part 2
- Lecture 3 :
- Back Propagation Implementation - Creating Class - Part 3
- Lecture 4 :
- Back Propagation Implementation - Creating Class - Part 4
- Lecture 5 :
- Back Propagation Implementation - Creating Class - Part 5
- Lecture 6 :
- Back Propagation Implementation - Creating Class - Part 6
- Lecture 7 :
- Back Propagation Implementation - Creating Class - Part 7
-
Section 33 : Back Propagation - Create XOR Evaluation Program
- Lecture 1 :
- Back Propagation - Create XOR Evaluation Program - Part 1
- Lecture 2 :
- Back Propagation - Create XOR Evaluation Program - Part 2
-
Section 34 : Back Propagation - Create MNIST Evaluation Program
- Lecture 1 :
- Back Propagation - Create MNIST Evaluation Program - Part 1
- Lecture 2 :
- Back Propagation - Create MNIST Evaluation Program - Part 2
- Lecture 3 :
- Back Propagation - Create MNIST Evaluation Program - Part 3
-
Section 35 : Keras Based MNIST Evaluation Program
- Lecture 1 :
- Keras Based MNIST Evaluation Program - Part 1
- Lecture 2 :
- Keras Based MNIST Evaluation Program - Part 2
- Lecture 3 :
- Keras Based MNIST Evaluation Program - Part 3
- Lecture 4 :
- Keras Based MNIST Evaluation Program - Part 4
-
Section 36 : Introduction to Convolutional Neural Networks
- Lecture 1 :
- Introduction to Convolutional Neural Networks
-
Section 37 : Custom Convolution using Python
- Lecture 1 :
- Custom Convolution using Python - Part 1
- Lecture 2 :
- Custom Convolution using Python - Part 2
- Lecture 3 :
- Custom Convolution using Python - Part 3
- Lecture 4 :
- Custom Convolution using Python - Part 4
-
Section 38 : CNN Design Best Practices and ShallowNet Introduction
- Lecture 1 :
- CNN Design Best Practices and ShallowNet Introduction
-
Section 39 : Create ShallowNet Class
- Lecture 1 :
- Create ShallowNet Class - Part 1
- Lecture 2 :
- Create ShallowNet Class - Part 2
-
Section 40 : ShallowNet using Animals Dataset
- Lecture 1 :
- ShallowNet using Animals Dataset - Part 1
- Lecture 2 :
- ShallowNet using Animals Dataset - Part 2
-
Section 41 : ShallowNet using CIFAR10 Dataset
- Lecture 1 :
- ShallowNet using CIFAR10 Dataset
-
Section 42 : ShallowNet CIFAR10 Save and Load Model
- Lecture 1 :
- ShallowNet CIFAR10 Save and Load Model
-
Section 43 : ShallowNet CIFAR10 Predict
- Lecture 1 :
- ShallowNet CIFAR10 Predict
-
Section 44 : ShallowNet Animals Save, Load and Predict
- Lecture 1 :
- ShallowNet Animals Save, Load and Predict
-
Section 45 : LeNet Overview
- Lecture 1 :
- LeNet Overview
-
Section 46 : Create LeNet Class
- Lecture 1 :
- Create LeNet Class
-
Section 47 : Lenet MNIST Train and Save
- Lecture 1 :
- Lenet MNIST Train and Save
-
Section 48 : Lenet MNIST Prediction
- Lecture 1 :
- Lenet MNIST Prediction
-
Section 49 : Introduction to VGGNet Architecture
- Lecture 1 :
- Introduction to VGGNet Architecture
-
Section 50 : Creating VGGNet Class
- Lecture 1 :
- Creating VGGNet Class
-
Section 51 : VGGNet CIFAR 10 Model Save
- Lecture 1 :
- VGGNet CIFAR 10 Model Save
-
Section 52 : VGGNet CIFAR 10 Predict
- Lecture 1 :
- VGGNet CIFAR 10 Predict
-
Section 53 : Learning Rate Scheduler
- Lecture 1 :
- Learning Rate Scheduler - Part 1
- Lecture 2 :
- Learning Rate Scheduler - Part 2
-
Section 54 : Improvement Checkpoint
- Lecture 1 :
- Improvement Checkpoint - Part 1
- Lecture 2 :
- Improvement Checkpoint - Part 2
-
Section 55 : Pretrained VGGNet 16
- Lecture 1 :
- Pretrained VGGNet 16 - Part 1
- Lecture 2 :
- Pretrained VGGNet 16 - Part 2
-
Section 56 : Pretrained VGGNet 19
- Lecture 1 :
- Pretrained VGGNet 19
-
Section 57 : Pretrained ResNet
- Lecture 1 :
- Pretrained ResNet
-
Section 58 : Pretrained Inception
- Lecture 1 :
- Pretrained Inception
-
Section 59 : Pretrained Xception
- Lecture 1 :
- Pretrained Xception
-
Section 60 : SOURCE CODE AND FILES ATTACHED
- Lecture 1 :
- Source Code Download Link Attached
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Instructor
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I am a pioneering, talented and security-oriented Android/iOS Mobile and PHP/Python Web Developer Application Developer offering more than eight years’ overall IT experience which involves designing, implementing, integrating, testing and supporting impact-full web and mobile applications.
I am a Post Graduate Masters Degree holder in Computer Science and Engineering.
My experience with PHP/Python Programming is an added advantage for server-based Android and iOS Client Applications.
I am currently serving full-time as a Senior Solution Architect managing my client's projects from start to finish to ensure high-quality, innovative and functional design.
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