Instructors: Computer Science & AI School
19 sections • 21 lectures • 1h 31m
Video: MP4 1280×720 44 KHz | English + Sub
Updated 11/2022 | Size: 795 MB
Learn Deep Learning & Computer Vision for Image Classification using Pre-trained Models with Python using Google Colab
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
Requirements
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
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 FineTuning the model.
2. Transfer Learning by using the Model as Fixed Feature Extractor.
You will learn how to perform Data Augmentation.
You will learn how to load Dataset, Dataloaders.
You will Learn to FineTune the Deep Resnet Model.
You will learn how to use the Deep Resnet Model as Fixed Feature Extractor.
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.
Who this course is for
Deep Learning enthusiasts interested to learn with Python and Pytorch
Students and researchers interested in Deep Learning for Image Classification
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