Welcome! In our tech-savvy world, sharing knowledge is as crucial as gathering it. Today, we present you with an extensive compilation of resources in mathematics, machine learning (ML), and deep learning (DL). It’s a treasure trove of tutorials, communities, courses, and more!
Outstanding ML and DL Tutorials
Here are some exceptional resources created by renowned educators:
- Andrej Karpathy blog
- Brandon Roher
- Andrew Trask
- Jay Alammar
- Sebastian Ruder
- Distill
- StatQuest with Josh Starmer
- sentdex
- Lex Fridman
- 3Blue1Brown
- Alexander Amini
- The Coding Train
- Christopher Olah
Communities to Follow
Engage with fellow enthusiasts and professionals in these communities:
- AI Coimbatore – Join here
- Telegram: For Daily Updates
- Facebook: Coimbatore School of AI
- TensorFlow User Group Coimbatore
- Meetup: TFUGCbe
- Facebook: TFUGCbe
Getting Started with Data Science
Embarking on your data science journey? Here are some guides to help you kickstart:
- How to Get Started with Machine Learning
- How to Build a Meaningful Career in Data Science
- My Self-Created Artificial Intelligence Masters Degree
- PyImageSearch
- 5 Beginner Friendly Steps to Learn ML and Data Science with Python
Introduction to Machine Learning
If you’re new to ML, check out the following resources:
- Luis Serrano: A Friendly Introduction to Machine Learning
- StatQuest: A Gentle Introduction to Machine Learning
- Machine Learning For Everyone – Summarizes machine learning algorithms with real-world examples.
Training Your Own Models with Teachable Machine
With Teachable Machine, you can quickly train a computer to recognize your images, sounds, or poses without needing any technical expertise!
Online Courses and MOOCs
Several companies offer excellent online courses. Here are just a few:
- Machine Learning by Andrew Ng, Stanford
- Datacamp: Data Engineer with Python
- Intro to Machine Learning – Covers topics such as Naive Bayes, SVM, Decision Trees, Regressions, and more.
- Intro to TensorFlow for Deep Learning – The best course for learning TensorFlow.
- End-to-End Machine Learning
Deep Learning Resources
Deep learning enthusiasts can dive into these resources:
- The Deep Learning Textbook by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Neural Networks And Deep Learning by Michael Nielsen
- Grokking Deep Learning by Andrew Trask
Understanding Algorithms: Analogy Explained
Imagine you are a chef. In the kitchen, you have several recipes at your disposal, each one tailored for a specific dish. A recipe is like an algorithm in ML. It lays out the steps and ingredients needed to cook up a delicious meal — in our case, a predictive model. Just as some recipes require you to combine ingredients like salt and sugar precisely, algorithms blend inputs (data) to produce outputs (predictions). The success of your dish (model) depends on the quality of your ingredients (data) and the correct application of the recipe (algorithm). Imagine you make a slight modification to a recipe. This could lead to something unexpected, good or bad, just like tuning parameters in an algorithm can impact its performance.
Troubleshooting Common Issues
If you encounter any trouble while navigating these resources, consider the following troubleshooting tips:
- Ensure that your internet connection is stable.
- Clear your browser’s cache and cookies for better loading performance.
- Check if your browser is updated to the latest version.
- Try accessing the resources from another device if issues persist.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.