5 Tips for Getting Started with AI
New to AI? You're in the right place! From tapping into university resources to engaging with AI communities, let's get you started.
Tip #1: Top University Lectures
We are lucky to live in an era where access to invaluable learning resources is readily available to all, as long as you have an internet connection. Leading universities such as MIT and Stanford generously share their wealth of knowledge, offering some of their lectures open to the public at no cost. Whether you're a beginner eager to explore the basics or a seasoned practitioner looking to deepen your understanding, these university lectures are invaluable resources to leverage on your AI journey.
📚 My Recommendations
Stanford NLP - CS224N: Natural Language Processing with Deep Learning / Spring 2024
MIT Introduction to Deep Learning
Tip #2: Code
Just like athletes need to workout daily to perform at their best, (aspiring) developers should make coding a part of their everyday routine. Platforms like LeetCode offer a variety of coding challenges, which help developers get hands-on experience with different algorithms and data structures crucial for AI projects, and there are plenty of Kaggle notebooks with fantastic code walkthroughs. Additionally, keeping up with new tools, frameworks, and libraries is essential in this fast-paced field. By coding regularly, developers can build a solid foundation and stay prepared to tackle complex AI tasks and adapt to changes in technology. Whether it's working on recommendation systems or LLM training, consistent coding practice is key to mastering AI.
📚 My recommendations
Tip #3: Math
Do you need to get a degree in math and know how to solve equations on a whiteboard to get a job in AI? No. But: While coding skills are essential, a solid understanding of mathematical principles forms the backbone of AI development. Concepts like linear algebra, calculus, probability, and statistics are not just theoretical constructs but practical tools for designing and optimizing AI algorithms. For instance, "training" in machine learning refers to the process of optimizing a model's parameters by minimizing a loss function. This involves iterative adjustments to the model's weights and biases using optimization algorithms, often based on techniques like gradient descent, which rely on concepts from Calculus and Linear Algebra to find the best fit between the model's predictions and the actual data. Probability and statistics provide the framework for making sense of data and drawing meaningful insights, guiding decisions in AI applications. Understanding these mathematical foundations of AI equips developers with the analytical tools needed to work on complex problems and innovate effectively.
📚 My recommendations
Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Tip #4: Contribute to Open Source
An impactful yet underrated way of improving your skills is to contribute to open-source projects to accelerate learning and foster community collaboration in AI development. Open source projects offer invaluable opportunities for developers to gain hands-on experience, collaborate with peers, and make meaningful contributions to the advancement of AI technologies. By participating in open source initiatives, you can not only refine your coding skills but also gain exposure to real-world projects and best practices, you can build a professional network, gain recognition within the community, and demonstrate your expertise to potential employers. Whether it's fixing bugs, adding new features, or improving documentation, every contribution makes a difference and contributes to the collective progress of AI innovation.
📚 My recommendations
Hugging Face - build demos, contribute open source models and datasets
Tip #5: Engaging with AI Communities
There’s enormous value in actively participating in AI communities, whether by delivering talks or hands-on workshops, assisting in event organization, or volunteering for conferences and hackathons. Engaging in these activities not only allows individuals to contribute their expertise and support to the community but also fosters personal growth and professional development. Delivering talks or workshops enables you to share insights, best practices, and innovative ideas with peers, while also improving communication and presentation skills. You don’t have to be an expert though – if you’re just getting started, sharing your personal AI journey could be very valuable and helpful for peers and beginners. Assisting in event organization and volunteering for conferences and hackathons helps create spaces for knowledge exchange, networking, and collaboration within the AI community. By actively participating in these initiatives, you can not only contribute to the advancement of AI but also build meaningful connections and enhance your skills and expertise in the process.
📚 My recommendations
