teachers & mentors

WE ATTRACT THE VERY BEST EXPERTS FROM INDUSTRY TO PROVIDE TUITION IN GROUND BREAKING MACHINE LEARNING TECHNOLOGIES

Teachers

DR. JOSÉ QUESADA

Jose Quesada is the founder of DSR and AI Deep Dive. He did his PhD work at UC Boulder and then Carnegie Mellon. Jose mentored > 200 machine learning portfolio projects. Some resulted in startups, others ended up being non-profits with significant social impact. 

Paco Tornay

Paco is the Dean of data science at AI Deep Dive. He is a professor in the University of Granada, He’s comfortable speaking six languages. Paco has kept up with the research in deep learning, and  applied NN models in several practical applications.

DR. MAREK GAGOLEWSKI

Dr. Marek Gagolewski is a Professor at the Polish Academy of Sciences and Warsaw University of Technology, researching and teaching on Data Science, Big Data, and Machine Learning. He teaches introductory and advanced courses in R, Python, and C++, and supervises PhD and MSc students in Computer and Data Science. He is the author of best-selling books on Python and R programming and many R packages, including the famous stringi package. Marek holds a PhD in Computer Science, specializing in data aggregation, fusion, and mining, as well as computational statistics and uncertainty modeling.

Daniel Godoy

Daniel is the Dean of Data Science Retreat (DSR). He is passionate about Machine Learning, teaching and food. At DSR/DLR he teaches scalable machine learning on Spark. This lead to him writing a library to make Spark Dataframes behave more like Pandas’, HandySpark.

He’s written a really good posts series on how to understand deep learning from first principles, 

You can find Daniel on LinkedInGitHub or Twitter

JAKUB CIESLIK

Jakub works as a Senior Data Scientist at New Yorker where he works on various project mainly related to computer vision and object detection. Previously he worked in the Fintech sector. Jakub holds a masters degree in biomedical signal processing. He is passionate about practical hands on deep learning as well as python development and bringing deep learning into production systems.

QINGCHEN WANG

Qingchen is an award-winning data scientist with rigorous training in machine learning, artificial intelligence, statistics, and econometrics. He is one of only 87 grandmasters (as of mid-2017) on the Kaggle data science competition platform (top rank of 14th out of 52,000+ active competitors on Kaggle), and he also have significant experience in software engineering (C++, Java, Python). Currently he is working on research and development of data-driven solutions to problems in digital marketing.

Daniel Nouri

Daniel Nouri
Daniel is an expert software engineer, Python programmer, and machine learning specialist. When he’s not developing high-performing, end-to-end pattern recognition and predictive analytics systems for his clients, Daniel’s learning new tricks to train deep neural networks more efficiently. Through his company Natural Vision, he’s been successfully applying deep learning to problems in bioacoustics, computer vision, and text mining. He’s the designer and coauthor of skorch, A scikit-learn compatible neural network library that wraps pytorch.

mentors

Gilberto Titericz

Machine Learning Expert – USA O-1 Visa Holder – Ranked #1 out of 78,200 active data scientists at Kaggle for more than 2 years (https://www.kaggle.com/titericz) 14x Prize Winner 29x Top-1%-placed in competitions

Kelvin Lwin

After spending nearly a decade at UC Berkeley, Kelvin decided to repay his debt to the public education system by helping build UC Merced. He spent seven years teaching 4,500 students across 55 classes, while redesigning the undergraduate Computer Science curriculum. He is now busy designing curricula at NVIDIA’s Deep Learning Institute (DLI) to democratize access to the latest technologies across many disciplines, industries and geographies. Kelvin helped DLI reach over 100K developers worldwide directly and in collaboration with Udacity and Coursera/Deeplearning.ai. He continues to search for ways to leverage AI to solve the Paradox of Progress.