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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)
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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)
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3
Tools of the Trade: Introducing Our AI Assistants
23
Other Books You May Enjoy
24
Index

The new paradigm, programming with natural language

Probably the biggest game changer with using LLM-based AI assistants is that you’re able to interact with them using nothing but natural language. There’s no need to learn a programming language to get the response you need. This change constitutes a new paradigm in interacting with AI. We’re moving away from writing in specific languages for producing apps, data retrieval, or even how we produce images, presentations, and more to express at a high level what we want through a prompt.

Here is an example of things that are now possible to do using prompts, where it before needed considerably more effort:

  • Programming: With a prompt, you express what app you want to build or what changes you want to make with the code.
  • Image generation: Where you before needed a designer or artist, you can now generate via prompts.
  • Videos: There are tools out there that, once given a prompt, will generate videos where an avatar reads out your written text.
  • Text tasks: LLM-based AI assistants can generate emails, summarize large bodies of text, author interview ads, and much more; anything you can imagine with text really.

All these application areas mentioned above make it clear that LLM-based AI tools are useful not only to programmers and data scientists but numerous different professions.

Challenges and limitations

Is everything working perfectly at this point? AI assistants aren’t able to replace “you” just yet, and should be considered more of a “thinking partner.” Microsoft has even, through conscious naming, called their AI assistants “Copilots” where you’re clearly the pilot that sets out the direction. These tools can generate text and other modalities in seconds, but you need to verify the correctness. Often, the first response you get from a tool is something you need to iterate over. The good news is that it just takes seconds to redo the instruction.

An important thing to realize about AI assistants is that the more skilled you are at a certain topic, the more intelligent questions you can ask of it, and you’ll be able to better assess the correctness of the response.

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