How can you learn more about neural networks for AI?

To effectively learn neural networks, start with foundational courses like Coursera's "Neural Networks and Deep Learning." Supplement this with books and online tutorials for deeper insights. Get hands-on experience by working on Kaggle competitions or Google Colab projects. Understanding core concepts like neurons, layers, and backpropagation is essential before diving into complex projects. Don't go it alone; engage with communities on Reddit or LinkedIn to share ideas and get feedback. In summary, a blend of theory, practical projects, and community engagement will make your learning journey in neural networks rewarding and comprehensive.

Before diving into details of neural networks, first think about the problem to be solved and the best modeling approach. Neural networks are one general class of functional forms, but might not be the best to capture the patterns in your data and generalize well. Even within neural networks, there are vastly different architectures, like fully connected networks vs transformers, which capture quite different patterns. You may hear that neural networks are “universal” approximators, but this is not useful because it is true even for trivial networks that provide no ability to generalize. More useful are results from renormalization theory showing that sufficiently deep and not too wide networks are forced to provide some generalization.

Have a go at Google's "Foundational" course on the topic, found inside the "Machine Learning Crash Course". It's a great introduction.

If I'd be starting from scratch, I'd watch all of Andrej Karpathy's lectures on neural networks. These lectures cover everything from intimately understanding the basics of neural networks, training them, and work towards building your own GPT model. After that, I'd jump straight into a personal project. Build something, anything, that you find interesting and fun. It can be anything from translating old English texts into modern readable English, or a classifier to detect the species of plants or birds in nature. I would never recommend Kaggle competitions to anyone, as these do not represent real world development or application of machine learning. If you enjoy competitions, go ahead - but there are better ways to learn.

Learning about neural networks goes beyond just grasping the mathematics and theory behind their operations. It also involves the practical aspect of developing and implementing neural network concepts. Therefore, the knowledge and experience of frameworks and various tools can greatly facilitate the implementation process of projects. Popular frameworks like TensorFlow and PyTorch have garnered significant popularity and are recognized for their user-friendliness. Additionally, a multitude of open-source libraries are available to enhance the efficiency of tasks, such as OpenCV, Keras, Numpy, and Scikit-learn.

Do check out Tensorflow Playground. It's a no code tool that can help you understand how any changes to the parameters and neural net configuration affects your final prediction. I find this tool particularly helpful to help students /aspiring practicioners to grasp the concept visually.

Numerous platforms offer AI courses, both with and without certifications. Coursera, a renowned MOOC platform, provides a variety of intriguing AI courses from prestigious universities and tech industry leaders. For beginners, IBM offers a course titled “Introduction to Artificial Intelligence (AI)”, and the University of Pennsylvania offers a specialization called “AI For Business”. There are also other excellent options available, such as the Professional Certificate Program in Machine Learning & Artificial Intelligence offered by MIT Professional Education.

Andrew Ng's "Machine Learning" course on Coursera is a good introduction to neural networks. If seeking more advanced content, his "Deep Learning Specialization" is also a good followup. Geoffrey Hinton also shares his expertise through a course on Coursera. Udacity's "Deep Learning Nanodegree" is structured around hands-on projects. Additionally, edX features MIT's "Introduction to Deep Learning". Collectively, these provide a good foundation for anyone aspiring to master neural networks.

If you could only read one book on AI, it would be "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurelian Geron. It doesn't overwhelm you with math, yet remains very intuitive as you get hands-on with implementing all the most important machine learning algorithms to many of the state-of-the-art neural network models. Beyond that, it also teaches you how to deploy these into production, how to evaluate and monitor your models, how to set up your data pipelines, and more. It is the only book that I can confidently say teaches you how to become a competent AI engineer, and I can't recommend it enough.

Books indeed serve as an excellent starting point for those keen on delving into the realm of artificial intelligence. They provide a solid foundation of the basics and offer insights into the latest advancements in the field. Two noteworthy titles that I would suggest are “Mission AI: The New System Technology” from the research Policy studies series by the Netherlands Council for Government Policy, and “AI Ethics in Higher Education: Insights from Africa and Beyond” published by Springer Briefs in Ethics.

Have fun, honestly. Read books that are fun to read. Watch videos that don't capture your attention. If you aren't having fun, you won't be as engaged and thus won't learn that much. And you can afford to be picky, because there are so many free resources out there. Pick and choose what works for you :)