Unlocking the Power of A.I.: A Beginner's Guide
Table of Contents
- Introduction
- Notable Researchers in AI
- Getting Started in AI
- Resources for Learning AI
- Online Courses
- YouTube Channels
- Free Books
- Coding, Training, and Deploying
- Jupiter Notebooks
- Google Colab
- Google Cloud
- How to Read Research Papers
- Abstracts and Introductions
- Related Work
- Model Description
- Results and Experiments
- Conclusion and Citations
- Where To Find Research Papers
- Understanding the Math
Introduction
Welcome everyone to this workshop on getting started in AI. In this workshop, we will discuss notable researchers in AI, resources for learning AI, coding, training, and deploying AI models, how to read research papers, and understanding the math behind AI algorithms.
Notable Researchers in AI
Let's start by highlighting some notable researchers in the field of AI. These researchers have made significant contributions to the field and have valuable resources available for learning AI:
- Fei-Fei Li: Stanford professor, co-founder of ImageNet, and creator of online courses on AI.
- Demis Hassabis: Co-founder of Google DeepMind, responsible for creating AlphaGo, an AI that defeated the world's best Go player.
- Andrew Ng: Co-founder of Coursera and Google Brain, known for his popular machine learning courses on Coursera.
- Geoffrey Hinton: Renowned researcher in deep learning, his course "Neural Networks for Machine Learning" is highly recommended.
- Sebastian Thrun: Founder of Udacity, a platform with many AI-related courses.
- Siraj Raval: YouTuber known for his AI-related content, including tutorials and machine learning projects.
- Three Blue One Brown: YouTube Channel offering visually intuitive explanations of complex math concepts, including those related to AI.
- Andrew Ng: Author of "Understanding Machine Learning: From Theory to Algorithms" and "Machine Learning Yearning."
Getting Started in AI
To get started in AI, it's essential to have a solid foundation in programming and mathematics. Skills in Python and linear algebra are particularly important. If You are new to AI, consider taking online courses or tutorials to learn the basics. Websites like Coursera, Udacity, and YouTube offer a variety of AI-related courses and tutorials.
Resources for Learning AI
There are numerous resources available for learning AI, including online courses, YouTube channels, and free books. Here are some recommendations:
Online Courses
- Coursera: Offers a wide range of AI courses, including machine learning, deep learning, and natural language processing.
- Udacity: Provides both free and paid courses on AI topics, with a focus on practical application.
YouTube Channels
- Siraj Raval: Known for his engaging AI-related content, including tutorials and machine learning projects.
- Three Blue One Brown: Offers visually intuitive explanations of complex math concepts, including those related to AI.
- Two Minute Papers: Provides short videos summarizing recent AI research papers.
Free Books
- "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz and Shai Ben-David. This book covers the fundamentals of machine learning, including theory and algorithms.
- "Machine Learning Yearning" by Andrew Ng. This book offers practical advice and guidelines for applying machine learning in real-world projects.
Coding, Training, and Deploying
When it comes to coding, training, and deploying AI models, there are several tools and platforms available. Here are three popular options:
Jupiter Notebooks
Jupyter notebooks are a great way to code, train, and deploy small AI models locally. They provide an interactive and flexible environment for running Python code, making it easy to experiment with different models and algorithms.
Google Colab
Google Colab is a cloud-Based Jupyter notebook platform that offers free access to GPUs. It provides a convenient way to run AI models with more significant computational requirements. Colab is great for larger projects that require more memory and processing power.
Google Cloud
For even more extensive AI projects that require significant resources and scalability, Google Cloud provides a range of services, including AI Platform and ML Engine. These platforms offer robust infrastructure and powerful tools for training and deploying AI models.
How to Read Research Papers
Reading research papers is an essential skill for staying up-to-date in the field of AI. Here is a step-by-step guide on how to read research papers effectively:
- Start with the abstract and introduction sections to understand the problem being addressed and the proposed solution.
- Explore the related work section to see how the Current research fits into the existing body of knowledge.
- Dive into the model description to understand the specific architecture and algorithms used in the research.
- Examine the results and experiments section to evaluate the model's performance and compare it to other approaches.
- Read the conclusion section to get a summary of the research findings and insights for future work.
- Don't forget to review the citations to find additional resources and related papers.
Where to Find Research Papers
Research papers can be found through various sources, including:
- Google Scholar: A search engine specifically designed for academic papers.
- University databases: Many universities maintain online databases of research papers accessible to the public.
- ArXiv: A preprint server that hosts scientific papers in various fields, including AI and machine learning.
- Conference proceedings: AI conferences like NeurIPS, ICML, and CVPR often publish their papers online.
Understanding the Math
While understanding the mathematical principles behind AI algorithms is not critical for all roles, it can be valuable for researchers, mathematicians, and those involved in performance tuning. Linear algebra, calculus, and statistics are the primary branches of math used in AI. Understanding symbols such as summation (Σ) and activation functions is important for grasping the concepts presented in research papers.
In conclusion, getting started in AI requires a strong foundation in programming and mathematics. There are numerous resources available, such as online courses, YouTube channels, and research papers, to enhance your knowledge. Don't be afraid to dive into the math and experiment with coding, training, and deploying AI models. AI is an exciting field with vast opportunities for learning and growth.