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Deep Learning With Python For Image Classification
Section 1 - Introduction to the Course
Introduction to the Course (2:22)
Section 2 - Define Image Classification
Image Classification with single label and multi-label (2:59)
Section 3 - Pretrained Models Definition
PreTrained Models and their Applications (5:11)
Section 4 - Deep Learning Architectures for Image Classification
Deep Learning ResNet and AlexNet Architectures for Image Classification (4:58)
Section 5 - Google Colab for Writing Python Code
Set-up Google Colab for Writing Python Code (5:11)
Section 6 - Connect Google Colab with Google Drive
Connect Google Colab with Google Drive to Read and Write Data (2:43)
Section 7 - Access Data from Google Drive to Colab
Read Data from Google Drive to Colab Notebook (2:44)
Section 8 - Data Preprocessing for Image Classification
Lecture 1 - Perform Data Preprocessing for Image Classification (5:00)
Section 9 - Single-Label Image Classification using Deep Learning Models
Single-Label Image Classification using ResNet and AlexNet PreTrained Models (8:03)
Resources Single_Label Classification (Python Code)
Section 10 - Multi-Label Image Classification using Deep Learning Models
Lecture 2 - Resources Multi_Label Classification
Multi-Label Image Classification using ResNet and AlexNet PreTrained Models (6:21)
Section 11- Transfer Learning
Introduction to Transfer Learning (6:10)
Section 12- Link Google Drive with Google Colab
Link Google Drive with Google Colab (2:43)
Section 13 - Dataset, Data Augmentation, Dataloaders, and Training Function
Dataset, Data Augmentation, Dataloaders, and Training Function (7:20)
Section 14 - Deep ResNet Model FineTuning
Deep ResNet Model FineTuning (7:19)
Section 15 - Model Optimization
ResNet Model HyperParameteres Optimization (6:22)
Section 16 -Deep ResNet Training
Deep ResNet Model Training (3:34)
Section 17 - Deep ResNet Feature Extractor
Deep ResNet as Fixed Feature Extractor (4:42)
Section 18 - Model Optimization, Training and Results
Model Optimization, Training and Results Visualization (5:51)
Section 19 - Resources Code for Transfer Learning by FineTuning and Model Feature Extractor
Code of Classification using Transfer Learning (1:41)
Code for Transfer Learning by FineTuning and Model Feature Extractor
Classification Dataset
Lecture 1 - Perform Data Preprocessing for Image Classification
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