Nishant Shukla
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Over 130 Practical Recipes For Data Analysis and Machine Learning. Harness Haskell in the real world. Data is everywhere, logging is cheap, and analysis is inevitable. The recipes in this chapter cover how to gather useful data. Learn how to validate and clean. Ensure sanity checks on data before analyzing it. Or the contents of a book.
We start from Computer Vision and expand to other disciplines. Our objective is to pursue a unified framework for representation, learning, inference and reasoning, and to build intelligent computer systems for real world applications. Functionality, intuitive physics, intentionality, perceptual causality, theory-of-mind, and visual persuasion;.
Early access draft now available! Download the source code. To make it less boring, check out that pretty illustration. This chapter has no code whatsoever.
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Nishant ShuklaDESCRIPTION
Graduate Student at UCLA. I am a Computer Science Masters. Student continuing as a Ph.D. candidate. My research interests are Artificial Intelligence. Computer Vision, Natural Language Processing, and Knowledge Representation. At the University of Virginia, I graduated with a B.S. in Computer Science. And a B.A. in Mathematics. There, I was a founding member of HackUVA. I also lectured a course on Haskell. Currently, I work at the Center for Vision, Cognition, Learning, and Art. With Dr. Caiming Xiong.PARSED CONTENT
The website states the following, "I am a Computer Science Masters." We viewed that the web page also stated " Student continuing as a Ph." It also said " My research interests are Artificial Intelligence. Computer Vision, Natural Language Processing, and Knowledge Representation. At the University of Virginia, I graduated with a B. There, I was a founding member of HackUVA. I also lectured a course on Haskell. Currently, I work at the Center for Vision, Cognition, Learning, and Art."SEEK SUBSEQUENT WEB PAGES
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