Deep Learning Python Project: CNN based Image Classification
Learn Image Classification with Deep Learning & Transfer Learning in Python on Google Colab.
Development ,Data Science,Python
Lectures -22
Resources -4
Duration -1.5 hours
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Course Description
In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models. Image Classification is a computer vision task to recognize an input image and predict a single-label or multi-label for the image as output using Machine Learning techniques.
You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models.
You will learn how to connect Google Colab with Google Drive and how to access data.
You will perform data preprocessing using different transformations such as image resize and center crop etc.
You will perform two types of Image Classification, single-label Classification, and multi-label Classification using deep learning models with Python.
You will be able to learn Transfer Learning techniques:
1. Transfer Learning by Fine-tuning the model.
2. Transfer Learning by using the Model as a Fixed Feature Extractor.
You will learn how to perform Data Augmentation.
You will learn how to load Datasets and Data loaders.
You will learn to fine-tune the Deep Resnet Model.
You will learn how to use the Deep Resnet Model as a Fixed Feature Extractor.
You will Learn HyperParameters Optimization and results in visualization.
In single-label Classification, when you feed the input image to the network it predicts a single label. In multi-label Classification, when you feed the input image to the network it predicts multiple labels. You will Learn Deep Learning architectures such as ResNet and AlexNet. The ResNet is a deep convolution neural network proposed for image classification and recognition. ResNet network architecture is designed for classification tasks trained on the image dataset of natural scenes that consists of 1000 classes. Deep residual nets won 1st place on the ILSVRC 2015 Classification challenges. Alexie is a deep convolution neural network trained on the ImageNet dataset to classify the images into 1000 classes. It has five convolution layers followed by max-pooling layers and 3 fully connected layers. AlexNet won the ILSVRC 2012 Classification challenge. You will perform image classification using ResNet and AlexNet deep learning models. The Deep Learning community has greatly benefitted from these open-source models where pre-trained models are a major reason for rapid advancements in Computer Vision and deep learning research.
Goals
- Learn Image Classification using Deep Learning PreTrained Models
- Learn Single-Label Image Classification and Multi-Label Image Classification
- Learn Deep Learning Architectures Such as ResNet and AlexNet
- Write Python Code in Google Colab
- Connect Colab with Google Drive and Access Data
- Perform Data Preprocessing using Transformations
- Perform Single-Label Image Classification with ResNet and AlexNet
- Perform Multi-Label Image Classification with ResNet and AlexNet
- Learn Transfer Learning
- Dataset, Data Augmentation, Dataloaders, and Training Function
- Deep ResNet Model FineTuning
- ResNet Model HyperParameteres Optimization
- Deep ResNet as Fixed Feature Extractor
- Models Optimization, Training and Results Visualization
Prerequisites
- Deep Learning with Python and Pytorch is taught in this course.
- A Google Gmail account is required to get started with Google Colab to write Python Code.

Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
-
Introduction to the Course 02:22 02:22
Define Image Classification
1 Lectures

Pretrained Models Definition
1 Lectures

Deep Learning Architectures for Image Classification
1 Lectures

Google Colab for Writing Python Code
1 Lectures

Connect Google Colab with Google Drive
1 Lectures

Access Data from Google Drive to Colab
1 Lectures

Data Preprocessing for Image Classification
1 Lectures

Single-Label Image Classification using Deep Learning Models
2 Lectures

Multi-Label Image Classification using Deep Learning Models
2 Lectures

Transfer Learning
1 Lectures

Link Google Drive with Google Colab
1 Lectures

Dataset, Data Augmentation, Dataloaders, and Training Function
1 Lectures

Deep ResNet Model FineTuning
1 Lectures

Model Optimization
1 Lectures

Deep ResNet Training
1 Lectures

Deep ResNet Feature Extractor
1 Lectures

Model Optimization, Training and Results
1 Lectures

Resources: Code for Transfer Learning by FineTuning and Model Feature Extractor
2 Lectures

Instructor Details

Mazhar Hussain
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