Deep Learning with Python for Image Classification
Learn Deep Learning & Computer Vision for Image Classification using Pre-trained Models with Python using Google Colab
Note: 4.2/5 (6 notes) 1,303 students
Instructor(s): Computer Science & AI School
Last update: 2022-05-12
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
- Python and Pytorch required Deep Learning skills are taught in this course
- A Google Gmail account to get started with Google Colab to write Python Code
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.
In single-label Cassification, 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
- Introduction to the Course
- Define Image Classification
- Image Classification with single label and multi-label
- Pretrained Models Definition
- PreTrained Models and their Applications
- Deep Learning Architectures for Image Classification
- Deep Learning ResNet and AlexNet Architectures for Image Classification
- Google Colab for Writing Python Code
- Set-up Google Colab for Writing Python Code
- Connect Google Colab with Google Drive
- Connect Google Colab with Google Drive to Read and Write Data
- Access Data from Google Drive to Colab
- Read Data from Google Drive to Colab Notebook
- Data Preprocessing for Image Classification
- Perform Data Preprocessing for Image Classification
- Single-Label Image Classification using Deep Learning Models
- Single-Label Image Classification using ResNet and AlexNet PreTrained Models
- Python Code for Single-label Classification
- Multi-Label Image Classification using Deep Learning Models
- Multi-Label Image Classification using ResNet and AlexNet PreTrained Models
- Python Code for Multi-Label Classification
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