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known as Artificial Intelligence or AI. According to the father of Artificial Intelligence, John McCarthy, AI is “The science and engineering of making intelligent machines, espe. ially intelligent computer programs”.The fundamental premise of AI is that it can create machines that can intelligently think in the.
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Explorations in Artificial Intelligence and Machine Learning, is a free book that provides an introduction to artificial intelligence, machine learning, and deep learning. It covers topics such as supervised learning, network analysis, reinforcement learning, and neural networks, providing an overview of these key concepts in the field of AI ...
Artificial Intelligence for Beginners - A Curriculum. Explore the world of Artificial Intelligence (AI) with our 12-week, 24-lesson curriculum! It includes practical lessons, quizzes, and labs. The curriculum is beginner-friendly and covers tools like TensorFlow and PyTorch, as well as ethics in AI.
- Artificial Intelligence for Beginners - A Curriculum
- Announcement - New Curriculum on Generative AI was just released!
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Explore the world of Artificial Intelligence (AI) with Microsoft's 12-week, 24-lesson curriculum! Dive into Symbolic AI, Neural Networks, Computer Vision, Natural Language Processing, and more. Hands-on lessons, quizzes, and labs enhance your learning. Perfect for beginners, this comprehensive guide, designed by experts, covers TensorFlow, PyTorch, and ethical AI principles. Start your AI journey today!"
In this curriculum, you will learn:
•Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI).
•Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch.
•Neural Architectures for working with images and text. We will cover recent models but may lack a little bit on the state-of-the-art.
•Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.
We just released a 12 lesson curriculum on generative AI. Come learn things like:
•prompting and prompt engineering
•text and image app generation
•search apps
As usual, there's a lesson, assignments to complete, knowledge checks and challenges.
Check it out:
Get started with the following resources:
•Student Hub page In this page, you will find beginner resources, Student packs and even ways to get a free cert voucher. This is one page you want to bookmark and check from time to time as we switch out content at least monthly.
Students, there are a couple of ways to use the curriculum. First of all, you can just read the text and look through the code directly on GitHub. If you want to run the code in any of the notebook - read our instructions, and find more advice on how to do it in this blog post.
However, if you would like to take the course as a self-study project, we suggest that you fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:
•Start with a pre-lecture quiz.
•Read the intro text for the lecture.
•If the lecture has additional notebooks, go through them, reading and executing the code. If both TensorFlow and PyTorch notebooks are provided, you can focus on one of them - choose your favorite framework.
•Notebooks often contain some of the challenges that require you to tweak the code a little bit to experiment.
✍️ Primary Author: Dmitry Soshnikov, PhD
🔥 Editor: Jen Looper, PhD
🎨 Sketchnote illustrator: Tomomi Imura
✅ Quiz Creator: Lateefah Bello, MLSA
We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on project-based and that it includes frequent quizzes.
By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12 week cycle.
You can run this documentation offline by using Docsify. Fork this repo, install Docsify on your local machine, and then in the etc/docsify folder of this repo, type docsify serve. The website will be served on port 3000 on your localhost: localhost:3000. A pdf of the curriculum is available at this link.
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Jun 6, 2023 · by Charlie Gerard. This textbook offers a comprehensive guide to applying machine learning techniques using JavaScript, including real-world examples and practical applications. Print length: 340 pages. Published: November 17, 2020. ISBN: 978-1484264171 (ISBN10: 1484264177) Download in PDF →.
First, we take the weighted sum of the neuron's inputs: + weight 2 × input 2 + weight 3 × input3Next we normal. ze this, so the result is between 0 and 1. For this, we use a mathematically conven. 1. 1+e−x. The Sigmoid function looks like this when plotted: istic "S. 1 and 0.
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associated with arti cial intelligence (AI). Such tasks involve recognition, diag-nosis, planning, robot control, prediction, etc. The \changes" might be either enhancements to already performing systems or ab initio synthesis of new sys-tems. To be slightly more speci c, we show the architecture of a typical AI 1
