To develop a Dog Classifier, we need to have images of dogs. How to Collect Imagery Data using Microsoft Azureįor most types of projects, you can find the data online from various data repositories and websites. Then, let’s import all the functions and classes from the fastbook package and fast.ai vision widgets API: from fastbook import * Let’s install the fastbook package to set up the notebook: !pip install -Uqq fastbook It's been developed to cover the introduction to Deep Learning using fast.ai and PyTorch. How to Import the Libraries and Set Up the Notebookīefore we get down to building our model, we need to import the required libraries and utility function from the set of notebooks called fastbook. Building an Application out of your Jupyter Notebook.Converting downloaded data into DataLoader objects.Collecting Imagery Data using Microsoft Azure. ![]() Importing the libraries and setting up the notebook.The only prerequisite to get started is that you know how to code in Python and that you are familiar with high school math. The goal is to learn how easy it is to get started with deep learning models and to be able to achieve near-perfect results with a limited amount of data using pre-trained models. This blog post will walk you through the process of developing a dog classifier using fast.ai. It is a research institute dedicated to helping everyone – from a beginner level coder to a proficient deep learning practitioner – achieve world-class results with state-of-the-art models and techniques from the latest research in the field. It is also becoming more accessible to domain experts and AI enthusiasts with the advent of libraries like TensorFlow, PyTorch, and now fast.ai.įast.ai's mission is to democratize deep learning. Deep learning is bringing revolutionary changes to many disciplines.
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