1. Comprehensive ML Textbook Available for Free Online

A newly released book titled "Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning" is now available as a free PDF. Authored by Jean Gallier and Jocelyn Quaintance from the University of Pennsylvania, this extensive 2,100-page resource covers the essential mathematics for understanding ML from its foundational concepts.

While it may not be the most practical guide for hands-on applications, this book serves as a crucial reference for those interested in the theoretical aspects of ML. The draft can be accessed online here:

You can support the authors by purchasing a copy once it becomes available or by preordering. The index alone spans 15 pages, showcasing the depth of material covered. Although certain topics like statistics and information theory are somewhat underrepresented, this book remains an invaluable resource.

2. High-Quality Online Publications

Towards Data Science and Towards AI are two premier online platforms that offer well-curated articles on data science and related topics. Both publications are known for their rigorous editorial standards, ensuring that only credible content is published.

Having contributed to both myself, I can attest to their commitment to quality, which has attracted contributions from notable figures like DeepMind's chair, Prof. Michael Bronstein. Here, you’ll find:

• Accessible summaries of the latest research and peer-reviewed papers without misleading analogies.
• Articles covering statistical techniques, data processing, modeling, and simulations.
• Practical code snippets for immediate use in your projects, particularly for those focusing on JavaScript.
• A wealth of datasets and additional resources.
• Insightful opinion pieces, especially those authored by Alberto Romero.

3. A Free Resource for Chemistry Applications

"Deep Learning for Molecules & Materials," authored by Prof. Andrew White from the University of Rochester, is tailored for individuals with a chemistry background looking to enhance their deep learning skills.

The book begins with a straightforward review of the necessary math, followed by an introduction to ML concepts and deep learning applications. All examples utilize Python and various libraries such as numpy, Jax, TensorFlow, Keras, and scikit-learn.

4. The Value of Wikipedia

Wikipedia is an indispensable tool for learning, providing a starting point for nearly any topic and a means to gather key references for deeper exploration. For instance, an introduction to AlphaFold can be found through its Wikipedia entry, alongside numerous articles I have authored—my own bit of self-promotion!

5. A Classic on Neural Networks

Back in 1999, I was introduced to neural networks through a foundational text that provided insights into their architecture and applications. This book served as a guide during a time when resources for training complex networks were scarce.

Though primarily aimed at chemists, its discussions on mathematical principles are accessible to everyone. I was merely 22 years old and had limited math knowledge, yet I successfully wrote my first neural networks in C following its explanations.

The topics covered range from perceptrons to Kohonen networks, with applications in spectroscopies and molecular property predictions.

6. Formal Algorithms for Transformers

For those interested in modern neural network architectures, a free article from DeepMind released in July 2022 provides an in-depth look at transformer modules. This resource features 16 pages detailing 15 algorithms essential for understanding transformers.

7. Stay Updated with Leading Innovators

Keep an eye on major players in the field, such as OpenAI with DALL-E 2 and GPT-3, and DeepMind with AlphaFold and GATO. Regularly checking their blogs can lead to exciting insights, including advancements in how AI can enhance human intelligence.

8. Follow Influential Figures on Social Media

Engaging with key figures in your areas of interest on platforms like Twitter can be beneficial. Many experts actively share valuable information, especially in the fields of AI, chemistry, structural biology, and bioinformatics.