Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying AI-Assisted Programming for Web and Machine Learning
  • Table Of Contents Toc
  • Feedback & Rating feedback
AI-Assisted Programming for Web and Machine Learning

AI-Assisted Programming for Web and Machine Learning

By : Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar
4.9 (11)
close
close
AI-Assisted Programming for Web and Machine Learning

AI-Assisted Programming for Web and Machine Learning

4.9 (11)
By: Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar

Overview of this book

AI-Assisted Programming for Web and Machine Learning shows you how to build applications and machine learning models and automate repetitive tasks. Part 1 focuses on coding, from building a user interface to the backend. You’ll use prompts to create the appearance of an app using HTML, styling with CSS, adding behavior with JavaScript, and working with multiple viewports. Next, you’ll build a web API with Python and Flask and refactor the code to improve code readability. Part 1 ends with using GitHub Copilot to improve the maintainability and performance of existing code. Part 2 provides a prompting toolkit for data science from data checking (inspecting data and creating distribution graphs and correlation matrices) to building and optimizing a neural network. You’ll use different prompt strategies for data preprocessing, feature engineering, model selection, training, hyperparameter optimization, and model evaluation for various machine learning models and use cases. The book closes with chapters on advanced techniques on GitHub Copilot and software agents. There are tips on code generation, debugging, and troubleshooting code. You’ll see how simpler and AI-powered agents work and discover tool calling.
Table of Contents (25 chapters)
close
close
3
Tools of the Trade: Introducing Our AI Assistants
23
Other Books You May Enjoy
24
Index

Prompting strategy

To leverage ChatGPT for machine learning, we need to have a clear understanding of how to implement the prompting strategies specifically for code generation for machine learning.

Let’s brainstorm what we would like to achieve in this task to get a better understanding of what needs to go into the initial prompt.

Strategy 1: Task-Actions-Guidelines (TAG) prompt strategy

1.1 – Task: The specific task or goal is to create a simple linear regression model to predict the “Yearly Amount Spent” by dataset based on various attributes in the dataset.

1.2 – Actions: In this case, the strategy is to let ChatGPT decide the steps, hence no specific steps are provided.

1.3 – Guidelines: We will provide the following guidelines to ChatGPT in our prompt:

  • The code should be compatible with Jupyter Notebook
  • Ensure that there are detailed comments for each line of code.
  • You have to explain each...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY