This is a concise introduction to deep learning that sets the reader on the path to learning the subject in
Reza Rezazadegan
I am a professor of applied mathematics and compute science at Shiraz University, Shiraz, Iran. I received my PhD in math from Rutgers University, New Brunswick and formerly worked at the Biocomplexity Institute of the University of Virginia. My research interests include Technological Analysis and RNA bioinformatics, which makes use of Machine Learning, Complex Networks and Natural Language Processing methods. I’m also an AI enthusiast. More on my research and teaching.
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• The Berkeley Crossword Solver - Berkeley AI Research • Artificial Intelligence is a growing threat to authenticity in art - Bulletin • Artificial Intelligence in Manufacturing: Industrial AI Use Cases - IoT For All • Apple is Using Artificial Intelligence and Music to Win the Music App Arms Race - The Debrief • Google I/O 2022: Advancing knowledge and computing - Google AI • Unlimited Data, Unlimited Possibilities: UF Health and NVIDIA Build World’s Largest Clinical Language Generator - NVIDIA AI • Welcome ‘In the NVIDIA Studio’: A Weekly Celebration of Extraordinary Artists, Their Inspiring Art and Innovative Techniques - NVIDIA AI • GAN lawyers: An online scam operation posing as a Boston law firm - Tech Talks • 12 examples of artificial intelligence in everyday life - ITProPortal
• Understanding Conditional Variance and Conditional Covariance - Towards Data Science • 19 Hidden Sklearn Features You Were Supposed to Learn The Hard Way - Towards Data Science • AI Application Development Guide for Business Owners - Becoming Human • Introducing Mephisto: A new platform for more open, collaborative data collection - Meta AI • How to Determine the Best Fitting Data Distribution Using Python - KDnuggets • Amazon Comprehend Custom Classification - Towards Data Science • 3 types of data architecture components you should know about; applications, warehouses and lakes - Towards Data Science • How to Select Loss Function and Activation Function for Classification Problems - Towards Data Science