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- Limitedfree coursesaccess
- Play & PauseCourse Videos
- VideoRecorded Lectures
- Learn onMobile/PC/Tablet
- Quizzes andReal Projects
- Lifetime CourseCertificate
- Email & ChatSupport
What you'll learn?
- Computer Vison and Image Recognition Specific Deep Learning and Convolutional Neural Networks using Python for Beginners
Course Overview
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
Our learners work at
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