Course 7 : Conv nets, PTMs, Transfer Learning

Categories Advanced AI

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Course Curriculum

CNN

Image classification using MLP

00:00
Drawbacks of MLPs for processing images

00:00
CNN architecture – The big picture

00:00
A closer look at feature extraction

00:00
Basic components of a CNN

00:00
Use case : Image classification using CNNs

00:00
Adding dropout layers to avoid overfitting

00:00
Use case : Object Detection

00:00
Use case : Image Segmentation:

00:00
Use case : Facial Recognition

00:00
Use case : Video Analysis:

00:00
Use case : Generative Models:

00:00

Pre-trained models using Convolutional Neural Networks (CNNs)

Transfer learning
Transfer learning is a machine learning technique that leverages pre-trained models to solve new tasks or improve performance on related tasks.

Introduction to Transfer Learning:

00:00
Pre-trained Models:

00:00
Transfer Learning Approaches

00:00
Feature Extraction:

00:00
Fine-tuning:

00:00
Domain Adaptation:

00:00
Data Augmentation

00:00
One-shot and Few-shot Learning

00:00
Use case : Transfer Learning in Computer Vision

00:00
Use case : Transfer Learning in Natural Language Processing

00:00

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Course 7 : Conv nets, PTMs, Transfer Learning

Course Curriculum

CNN

Image classification using MLP

Drawbacks of MLPs for processing images

CNN architecture – The big picture

A closer look at feature extraction

Basic components of a CNN

Use case : Image classification using CNNs

Adding dropout layers to avoid overfitting

Use case : Object Detection

Use case : Image Segmentation:

Use case : Facial Recognition

Use case : Video Analysis:

Use case : Generative Models:

Pre-trained models using Convolutional Neural Networks (CNNs)

VGGNet

ResNet

Inception and GoogLeNet

DenseNet

MobileNet

Transfer learning
Transfer learning is a machine learning technique that leverages pre-trained models to solve new tasks or improve performance on related tasks.

Introduction to Transfer Learning:

Pre-trained Models:

Transfer Learning Approaches

Feature Extraction:

Fine-tuning:

Domain Adaptation:

Data Augmentation

One-shot and Few-shot Learning

Use case : Transfer Learning in Computer Vision

Use case : Transfer Learning in Natural Language Processing

Course 7 : Conv nets, PTMs, Transfer Learning

Course Curriculum

CNN

Image classification using MLP

Drawbacks of MLPs for processing images

CNN architecture – The big picture

A closer look at feature extraction

Basic components of a CNN

Use case : Image classification using CNNs

Adding dropout layers to avoid overfitting

Use case : Object Detection

Use case : Image Segmentation:

Use case : Facial Recognition

Use case : Video Analysis:

Use case : Generative Models:

Pre-trained models using Convolutional Neural Networks (CNNs)

VGGNet

ResNet

Inception and GoogLeNet

DenseNet

MobileNet

Transfer learning Transfer learning is a machine learning technique that leverages pre-trained models to solve new tasks or improve performance on related tasks.

Introduction to Transfer Learning:

Pre-trained Models:

Transfer Learning Approaches

Feature Extraction:

Fine-tuning:

Domain Adaptation:

Data Augmentation

One-shot and Few-shot Learning

Use case : Transfer Learning in Computer Vision

Use case : Transfer Learning in Natural Language Processing

Student Ratings & Reviews

Transfer learning
Transfer learning is a machine learning technique that leverages pre-trained models to solve new tasks or improve performance on related tasks.