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Practical Deep Learning for Coders

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1. Practical Deep Learning

• Practical Deep Learning

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fast.ai

• About
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The First Certificate in Deep Learning

courses

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Your AI Product Needs Evals

LLMs

How to construct domain-specific LLM evaluation systems.

Author

Hamel Husain

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Structured Outputs

Introduction

JSON is one of the most widely used formats in the world for applications to exchange data.

Structured Outputs is a feature that ensures the model will always generate responses that adhere to your supplied JSON Schema, so you don't need to worry about the model omitting a required key, or hallucinating an invalid enum value.

Some benefits of Structed Outputs include:

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Cracking the C-suite 'How to Get and Master Key Executive Roles'

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Comparing Vanilla JS with HTMX and Hyperscript

🧑 human (Sep 21, 2024, 10:10 AM)

Compare these two approaches, one using vanilla js with HTMX and one using hyperscript. Explain the syntax of Hyperscript carefully to a programmer that has no knowledge of hyeprscript

<plain-js-example>
<button id="contacts-btn" hx-get="/contacts" hx-target="body"> ①
  Get Contacts
</button>
 ②

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fastcore Module Documentation

fastcore.basics

Basic functionality used in the fastai library

• def ifnone(a, b) b if a is None else a

• def maybe_attr(o, attr)

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Basic functionality

Basic functionality used in the fastai library

Basics

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source

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A tour of fastcore

Here’s a (somewhat) quick tour of a few higlights from fastcore.

Documentation

All fast.ai projects, including this one, are built with nbdev, which is a full literate programming environment built on Jupyter Notebooks. That means that every piece of documentation, including the page you’re reading now, can be accessed as interactive Jupyter notebooks. In fact, you can even grab a link directly to a notebook running interactively on Google Colab - if you want to follow along with this tour, click the link below:

colab_link('index')__