I think this is the best introductory course on Machine Learning. Although it's somewhat old (from 2012), it's definitely still relevant and does an excellent job of explaining the fundamentals and setting the stage as well as introducing the key techniques clearly. (I think its Support Vector Machine (SVM) lecture is the best on YouTube, and the other lectures are also among the best in their respective topics :) ) In addition, it covers the often-overlooked concept of VC dimension in a very intuitive and friendly way. These lectures are outstanding, and I highly recommend watching the series. There's a lot to learn, even for those already proficient in the field.
This is a nice and friendly book for those who need hands-on experience in training various network architectures. The book uses TensorFlow 2 and its Keras API. The book doesn't cover the theoretical background of deep learning in detail. Hence, if you do not have a strong mathematical background, the book may feel like it skips a lot, and you may have a hard time implementing things on your own. If you have experience in deep learning but have a hard time setting up the architecture, training the model, loading the data, etc., this book is for you.
This website is one of the best learning resources for C++, especially for beginners. It has a nice Q/A section at the end of each page and Alex and nascardriver do their best to answer questions there. The website not only covers the semantics of C++; it also mentions common pitfalls, best practices, etc. I think even if one is proficient in C++, there is still a lot to learn from this website. However, covering the whole website may be really time-consuming and hard; I know, I did :) One can also use the website as a reference. It has nice indexing and search functionality.
This is the website of the Stanford course "CS231n: Convolutional Neural Networks for Visual Recognition". It is the best introductory deep learning course out there. It clearly explains the basics and theory. Nothing will feel like magic or missing. You will be ready to do neural network tasks with confidence.
This website is a tutorial website for teaching C++ to beginners. These tutorials are not as verbose as Learn Cpp tutorials. If C++ is not your first language or if you are generally familiar with programming concepts, this website is a practical and fast way to gain C++ skills.
Tracking algorithms are used to track targets, smooth out noisy signals, calculate the likelihood of an observation, and countless other applications. The chances that you will need to implement, use, or apply a tracking algorithm at some point in your career are quite high. This tutorial is a very good starting point to understand what tracking (filtering) algorithms do.