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Computer Vision is a diverse research domain encompassing various topics. This guide compiles essential and up-to-date tutorials, carefully selected from those presented at top-tier conferences in the field, including CVPR, ICCV, ECCV, and SIGGRAPH.

Context: Welcome to the reading guide for “Probabilistic Machine Learning” by Kevin Murphy. The first version of this book, published in 2012 (probml.github.io/pml-book/book0.html) gained widespread recognition in the Machine Learning community and was often referred to as the “Bible of Machine Learning.

This is the PhD guidance from my own experience including: (1) Defining your goals (2) How to choose a university and a professor (3) The needed mindset (4) The worth noting things in research (5) Career paths after graduation (6) Attitude after graduation It is specially written for Vietnamese student, but you can use Google Translate for your convinence.

This is my first post on Machine Learning, Deep Learning and Computer Vision series in Medium. I am currently a Ph.D. Student in Computer Science with research interests are Computer Vision and Machine Learning.

One of the most powerful and common tools for your research is Google Scholar. It contains many useful features that are necessary for your research. If you can make use of it, you do not need any proprietary software.

This is the overview of basic and important machine learning models, methods and concepts and theories. I acknowledge all information and knowledge including images, data… I have taken from those two courses: https://www.