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Deep Learning, Artificial Intelligence Data Science and Computer Vision using Python
Syllabus :
- Section 1 : Course Introduction and Table of Contents
- Section 2 : Introduction to Deep Learning
- Section 3 : Introduction to Neural Networks
- Section 4 : Image Basics
- Section 5 : Preparing your computer - Installing Anaconda
- Section 6 : Preparing your computer - Installing Dependencies
- Section 7 : Python Basics
- Section 8 : Load and Show Image
- Section 9 : Image Classification Basics
- Section 10 : List of Popular Datasets Included
- Section 11 : KNN Image Classifier - Downloading Animals Dataset
- Section 12 : Creating Common Pre-processor
- Section 13 : Creating Common Loader
- Section 14 : KNN Basics
- Section 15 : KNN Implementation - Load and Process
- Section 16 : KNN Implementation - Splitting the Dataset
- Section 17 : KNN Implementation - Training and Evaluation
- Section 18 : KNN Prediction
- Section 19 : Introduction to Linear Classification
- Section 20 : Scoring Function Basics
- Section 21 : Scoring Function - Implementation
- Section 22 : Loss Function Basics
- Section 23 : Optimization Concept Terminology and Challenges
- Section 24 : Gradient Descent Implementation
- Section 25 : Stochastic Gradient Descent Implementation
- Section 26 : Introduction to Regularization
- Section 27 : Implementing Regularization
- Section 28 : Introduction to Perceptrons
- Section 29 : Perceptron Implementation: Creating Class
- Section 30 : Perceptron Implementation: Creating BitWise Evaluation Program
- Section 31 : Introduction to Back Propagation
- Section 32 : Back Propagation Implementation - Creating Class
- Section 33 : Back Propagation - Create XOR Evaluation Program
- Section 34 : Back Propagation - Create MNIST Evaluation Program
- Section 35 : Keras Based MNIST Evaluation Program
- Section 36 : Introduction to Convolutional Neural Networks
- Section 37 : Custom Convolution using Python
- Section 38 : CNN Design Best Practices and ShallowNet Introduction
- Section 39 : Create ShallowNet Class
- Section 40 : ShallowNet using Animals Dataset
- Section 41 : ShallowNet using CIFAR10 Dataset
- Section 42 : ShallowNet CIFAR10 Save and Load Model
- Section 43 : ShallowNet CIFAR10 Predict
- Section 44 : ShallowNet Animals Save, Load and Predict
- Section 45 : LeNet Overview
- Section 46 : Create LeNet Class
- Section 47 : Lenet MNIST Train and Save
- Section 48 : Lenet MNIST Prediction
- Section 49 : Introduction to VGGNet Architecture
- Section 50 : Creating VGGNet Class
- Section 51 : VGGNet CIFAR 10 Model Save
- Section 52 : VGGNet CIFAR 10 Predict
- Section 53 : Learning Rate Scheduler
- Section 54 : Improvement Checkpoint
- Section 55 : Pretrained VGGNet 16
- Section 56 : Pretrained VGGNet 19
- Section 57 : Pretrained ResNet
- Section 58 : Pretrained Inception
- Section 59 : Pretrained Xception
- Section 60 : SOURCE CODE AND FILES ATTACHED - Link in Description
