Transfer Learning Ipynb Colab

There was an error while loading. Please reload this page. Go to the end to download the full example code. Created On: Mar 24, 2017 | Last Updated: Jan 27, 2025 | Last Verified: Nov 05, 2024 In this tutorial, you will learn how to train a convolutional neural network for image classification using transfer learning. You can read more about the transfer learning at cs231n notes

In practice, very few people train an entire Convolutional Network from scratch (with random initialization), because it is relatively rare to have a dataset of sufficient size. Instead, it is common to pretrain a ConvNet on a very large dataset (e.g. ImageNet, which contains 1.2 million images with 1000 categories), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest. These two major transfer learning scenarios look as follows: Welcome to this week's assignment, where you'll be using transfer learning on a pre-trained CNN to build an Alpaca/Not Alpaca classifier! A pre-trained model is a network that's already been trained on a large dataset and saved, which allows you to use it to customize your own model cheaply and efficiently.

The one you'll be using, MobileNetV2, was designed to provide fast and computationally efficient performance. It's been pre-trained on ImageNet, a dataset containing over 14 million images and 1000 classes. By the end of this assignment, you will be able to: Preprocess and augment data using the Sequential API Adapt a pretrained model to new data and train a classifier using the Functional API and MobileNet There was an error while loading.

Please reload this page. This repository is dedicated to learning PyTorch from scratch using Google Colab. It follows a step-by-step approach, starting from the basics and moving towards advanced concepts like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning, and Generative Models. The learning strategy focuses on practical coding, using PyTorch for deep learning tasks. Below is the breakdown of each stage along with the corresponding topics covered in the repository. Goal: Understand the basics of PyTorch tensors, tensor operations, and autograd.

Goal: Learn to build simple neural networks using torch.nn. Goal: Learn how to handle datasets in PyTorch.

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