Feifei li deep learning books

Director of ai at tesla, where i lead the team responsible for all neural networks on the autopilot. Andrej karpathy, andrew ng, deep learning, demis hassabis, feifei li, geoff hinton, ian goodfellow, influencers, jeremy howard, research, yann lecun our list of deep learning researchers and industry leaders are the people you should follow to stay current with this wildly expanding field in ai. Top american libraries canadian libraries universal library community texts project gutenberg biodiversity heritage library childrens library. Zhe li, chong wang, mei han, yuan xue, wei wei, li jia li, feifei li.

Feifei li is professor of computer science at stanford university, and director of the stanford artificial intelligence lab sail. We are tackling fundamental open problems in computer vision research and are intrigued by visual functionalities that give rise to semantically meaningful interpretations of the visual world. I am a member of the data group and the flux group. Distributed trajectory similarity search, project website, talk, poster by d. She is a recipient of the 2017 athena award for academic leadership and the 2016 ieee pami mark everingham award, among many others. Feifei li, the internationally acclaimed scientist, speaks to cnbc about the vast opportunities as well as the perils of artificial intelligence in our future. Shes always done such great work, and i loved cs231n. Karpathy and feifei, deep visualsemantic alignments for generating image descriptions, cvpr 2015. When a very young child looks at a picture, she can identify simple elements. My research focus is on applying data analytics in computer system area, especially cloud systems, for the purpose of ensuring security and reliability. How artificial intelligence can make healthcare human again 1st edition.

Li has published more than 200 scientific articles in. This is a collection of resources for deep reinforcement learning, including the following sections. Recent developments in neural network aka deep learning approaches have greatly advanced the performance of these stateoftheart visual recognition systems. Feifei li speaks at the 2017 grace hopper celebration ghc 17 on the importance of algorithms in the fields of artificial intelligence and machine learning. Lecture 1 introduction to convolutional neural networks. Dive into deep learning d2l book this opensource book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. Lance downing, william beninati, terry platchek, arnold milstein, li feifei. Previously, i was a research scientist at openai working on deep learning in computer vision, generative modeling and reinforcement learning.

Technologist jeremy howard shares some surprising new developments in the fastmoving field of deep learning, a technique that can give computers the ability to learn chinese, or to recognize objects in photos, or to help think through a medical diagnosis. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and nlp is also provided. Working in areas of computer vision and cognitive neuroscience, feifei builds smart algorithms that enable computers and robots to see and think, inspired by the way the human brain works in the real world. Feifei professor director, stanford ai lab computer science department office. Please practice handwashing and social distancing, and check out our resources for adapting to these times. I received my phd from stanford, where i worked with feifei li.

In a thrilling talk, computer vision expert feifei li describes the state of the art including the database of 15 million photos her team built to teach a computer to understand pictures and the key insights yet to come. The book appeals to advanced undergraduate and graduate students, postdoctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing. The stanford vision and learning lab svl at stanford is directed by professors feifei li, juan carlos niebles, and silvio savarese. Deep learning and convolutional neural networks for medical imaging and clinical informatics 2019. Animesh garg postdoc garg at cs dot stanford dot edu.

Nicholas thompson, editor in chief of wired, moderated the 90minute conversation in the packed memorial auditorium, filled to its 1705seat capacity the purpose was to discuss how. Thoracic disease identification and localization with limited supervision. Side projects neuralstyle a torch implementation of the neural style transfer algorithm from the paper a neural algorithm of artistic style by leon a. In the past she has also worked on cognitive and computational neuroscience. Find all the books, read about the author, and more. A quest for visual intelligence in computers feifei li get deep learning now with oreilly online learning. Deeplearningbooks at master mikoto10032deeplearning. Nearly all of the faculty members doing work in ml and pretty much all of those involved in deep learning have been lured into more lucrative gigs the. Lecture 1 introduction to convolutional neural networks for visual recognition. Feifei li on ai and machine learning the engineering of. Deep learning book by ian goodfellow, yoshua bengio, and aaron courville. The deep learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.

I received my bachelor and master degrees from the beihang university at beijing, china. Feifei li is the inaugural sequoia professor in the computer science department at stanford university, and codirector of stanfords humancentered ai institute. The online version of the book is now complete and will remain available online for free. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning endtoend models for these tasks, particularly image classification.

The deep learning textbook can now be ordered on amazon. She is a professor of computer science at stanford university and the codirector of stanfords humancentered ai institute and the stanford vision and learning lab. In a thrilling talk, computer vision expert feifei li describes the state of the art including the database of 15 million photos her team. Unsupervised visuallinguistic reference resolution in instructional videos. The entire book is drafted in jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with selfcontained code. Lecture 8 deep learning software video lecture by prof. Li is the inventor of imagenet and the imagenet challenge, a critical largescale dataset and benchmarking effort that has contributed to the latest developments in deep learning and ai.

Books, surveys and reports, courses, tutorials and talks, conferences, journals and workshops. Feifei li 4 12 deep learning researchers and leaders sep 23, 2019. This course is a deep dive into details of the deep learning architectures with a focus on learning endtoend models for these tasks, particularly image classification. Resources for deep reinforcement learning yuxi li medium. She is a professor at stanford university and the codirector of stanfords humancentered ai institute and the stanford vision and learning lab.