Author(s: Tobi Olabode Machine Learning. Mathematics photo by Jeswin Thomas. Unsplash. I’ve been searching for material to enhance my maths skills to aid me with ML. I noticed that many users couldn’t find the right content to explain maths clearly, even after scrolling through a few threads. This leads to people not believing in learning ML. This is not a good thing. You’re right, strange-looking letters and Greek characters don’t seem welcoming. There are many good online teachers that can help you understand this experience. These are some of the materials: General Maths Videos Series 3blue1brown Calculus, Linear Algebra series. I have seen both these videos before and will continue to watch them. Without getting too involved in details, the narrator discusses the subject. It’s almost like learning the mathematics with the people who invented it. He does a fantastic job of visualizing vector space in the linear algebra series. The various operations to vectors or matrices can be seen in pictures. This will allow you to see the different operations required and their functions. Khan Academy Sal Kahn is something I am sure you already know. You’ve probably seen a few of his videos. His videos intuitively explain various topics. You will also see the different actions that you must take in order to perform various calculations. You can use matrix multiplication to calculate derivatives. You can find math topics that are relevant to ML at Calculus 1 Calculus 2. Linear Algebra practice questions will allow you to quickly test your understanding and provide feedback. These questions will help you cement the information that you have just learned in the videos. Understanding Calculus in 35 minutes — Organic Chemistry Tutor An overview of the topic. This will allow you to be familiarized with concepts that can lead to deeper learning. You don’t need to know much about calculus if you aren’t sure where to begin. This video is the right one. It takes less than one hour to learn what calculus really is. You can then go on to further research. You won’t be able to learn every aspect of calculus within 30 minutes. The video can help you to get familiar with the basic ideas. Deep Learning Specific Maths 3blue1brown Deep Learning Series Using the concepts of the previous series, and applying them in deep learning. 3blue1brown is a master at visualizing and explaining the nature of neural networks. The series is easy to understand for a beginner as it explains what neural networks are. It may be of benefit to ML experts who are more experienced. This book demonstrates fundamental concepts such as Linear algebra and gradient descent in an ML network. Mathematics for Machine Learning: I use it as a reference book if I’m looking for a particular concept. The book covers the key topics of machine learning in depth and is well-written. Example of the notation page: https://mml-book.github.io/book/mml-book.pdf Mathematics for Machine Learning — Multivariate Calculus — Imperial College London A multi-hour series explaining how calculus is used in deep learning. This material provides a comprehensive overview of the topic. It is well-written and covers enough information to allow you to learn much. This will increase your curiosity about learning calculus. It’s important not to get lost in all the finer details. These are materials that were recommended by others to me, but I’ve not used them myself. Check them out. Computational Linear Algebra – This course covers Linear algebra in a top-down view. Linear algebra is used in deep learning applications. Although maths is important, you won’t have to learn as much theory. You will also be using a lot code. Their website is [the course is]. It’s all about practical applications. They also encourage you to utilize cutting-edge algorithms, such as PyTorch and Numba. The course also includes foundational concepts in numerical linear algebra such as machine epsilon and singular value decomposition as well as QR decomposition. The course focuses on the use of linear algebra in real computation. This is not just linear algebra that’s done manually. The typical course in linear algebra focuses on solving matrix problems manually. For example, students will use Gaussian Elimination to find small systems of equations using pencil and paper. It turns out, however that there are many different methods and concerns when solving large matrix problems using a computer. It is not known if this code has been updated. The concepts seem to be fine. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. From their website. The Deep Learning textbook is an educational resource that will help both students and professionals enter the world of deep learning and machine learning. The book is not complete. However, I did use the notation page in order to grasp math symbols used in deep learning projects. They are deep-learning experts. They know exactly what they’re talking about, and I can attest to that. Ian Goodfellow is the inventor of GANs. Yoshua Bengio is one of the pioneers in deep learning. This book is recommended by a few in the ML community. Statistics is an important subject. This will help you to analyse and improve your data. It is worth learning about this topic. Check out my mailing list if you liked this article. Here you will find more of my writings. This article, Some Maths Resources for Helping You in Your ML Journey, was first published on Medium by . People are responding and highlighting this story. Published via
Some Maths Resources that will help you in your ML Journey
