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This plan includes
- Instant access to 11,000+ online courses
- Play & Pause Course Videos
HD Video Recorded Lectures- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Instructor Chat Support
- Cancel Plan Anytime
This plan includes
- Instant access to 11,000+ online courses
- Play & Pause Course Videos
- HD Video Recorded Lectures
- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Instructor Chat Support
- Cancel Plan Anytime
What you'll learn?
- 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
Course Overview
Are you interested in unlocking the full potential of Artificial Intelligence? Do you want to learn how to create powerful image recognition systems that can identify objects with incredible accuracy? If so, then our course on Deep Learning with Python for Image Classification is just what you need! In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models and Transfer Learning. 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.
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You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models.
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You will learn how to connect Google Colab with Google Drive and how to access data.
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You will perform data preprocessing using different transformations such as image resize and center crop etc.
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You will perform two types of Image Classification, single-label Classification, and multi-label Classification using deep learning models with Python.
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You will be able to learn Transfer Learning techniques:
1. Transfer Learning by FineTuning the model.
2. Transfer Learning by using the Model as Fixed Feature Extractor.
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You will learn how to perform Data Augmentation.
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You will learn how to load Dataset, Dataloaders.
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You will Learn to FineTune the Deep Resnet Model.
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You will learn how to use the Deep Resnet Model as Fixed Feature Extractor.
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You will Learn HyperParameters Optimization and results visualization.
In single-label Classification, when you feed input image to the network it predicts single label. In multi-label Classification, when you feed 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 designed for classification task, trained on the imageNet dataset of natural scenes that consists of 1000 classes. Deep residual nets won the 1st place on the ILSVRC 2015 Classification challenge. Alexnet is a deep convolution neural network trained on 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 the Computer Vision and deep learning research.
Pre-requisites
- Deep Learning with Python and Pytorch is taught in this course
- A Google Gmail account to get started with Google Colab to write Python Code
Target Audience
- Deep Learning enthusiasts interested to learn with Python and Pytorch
- Students and researchers interested in Deep Learning for Image Classification
Curriculum 22 Lectures 01:23:54
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Section 1 : Introduction to the Course
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Section 2 : Define Image Classification
- Lecture 1 :
- Image Classification with single label and multi-label
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Section 3 : Pretrained Models Definition
- Lecture 1 :
- PreTrained Models and their Applications
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Section 4 : Deep Learning Architectures for Image Classification
- Lecture 1 :
- Deep Learning ResNet and AlexNet Architectures for Image Classification
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Section 5 : Google Colab for Writing Python Code
- Lecture 1 :
- Set-up Google Colab for Writing Python Code
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Section 6 : Connect Google Colab with Google Drive
- Lecture 1 :
- Connect Google Colab with Google Drive to Read and Write Data
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Section 7 : Access Data from Google Drive to Colab
- Lecture 1 :
- Read Data from Google Drive to Colab Notebook
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Section 8 : Data Preprocessing for Image Classification
- Lecture 1 :
- Perform Data Preprocessing for Image Classification
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Section 9 : Single-Label Image Classification using Deep Learning Models
- Lecture 1 :
- Single-Label Image Classification using ResNet and AlexNet PreTrained Models
- Lecture 2 :
- Resources: Single-Label Classification
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Section 10 : Multi-Label Image Classification using Deep Learning Models
- Lecture 1 :
- Multi-Label Image Classification using ResNet and AlexNet PreTrained Models
- Lecture 2 :
- Resources: Multi-Label Classification
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Section 11 : Transfer Learning
- Lecture 1 :
- Introduction to Transfer Learning
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Section 12 : Link Google Drive with Google Colab
- Lecture 1 :
- Link Google Drive with Google Colab
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Section 13 : Dataset, Data Augmentation, Dataloaders, and Training Function
- Lecture 1 :
- Dataset, Data Augmentation, Dataloaders, and Training Function
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Section 14 : Deep ResNet Model FineTuning
- Lecture 1 :
- ResNet Model HyperParameteres Optimization
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Section 15 : Deep ResNet Training
- Lecture 1 :
- Deep ResNet Model Training
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Section 16 : Deep ResNet Feature Extractor
- Lecture 1 :
- Deep ResNet as Fixed Feature Extractor
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Section 17 : Model Optimization, Training and Results
- Lecture 1 :
- Model Optimization, Training and Results Visualization
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Section 18 : Resources: Code for Transfer Learning by FineTuning and Model Feature Extractor
- Lecture 1 :
- Code of Classification using Transfer Learning
- Lecture 2 :
- Code for Transfer Learning by FineTuning and Model Feature Extractor
- Lecture 3 :
- Classification Dataset
Our learners work at
Frequently Asked Questions
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