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Official Product Description

Unlock the Power of Data: Master SQL, Databases & Deep Learning in Just 8 Hours!

DescriptionLearn Deep Learning, Machine Learning & Computer Vision for Image Classification in PyTorch Using CNN Transfer Learning
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 accurately identify objects? If so, then this 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.
What you’ll learn

Access 25 lectures & 2 hours of content 24/7
Use Google Colab notebooks for writing the Python code for image classification using Deep Learning models
Learn how to connect Google Colab with Google Drive & how to access data
Perform data preprocessing using different transformations such as image resize & center crop etc.
Perform two types of Image Classification, single-label & multi-label classifications using deep learning models with Python
Learn Transfer Learning techniques

Transfer Learning by Fine-Tuning the model
Transfer Learning by using the Model as Fixed Feature Extractor

Learn how to perform Data Augmentation
Load Dataset, Dataloaders
Learn to fine-tune the Deep ResNet Model
Use the Deep Resnet Model as Fixed Feature Extractor
Learn HyperParameters Optimization & results visualization
SpecsImportant Details

Length of time users can access this course: lifetime
Access options: desktop & mobile
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: beginner
Certificate of Completion ONLY
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Requirements

Any device with basic specifications
A Google Gmail account to get started with Google Colab to write Python Code