- Deep Learning: The Free eBook - May 4, 2020.
"Deep Learning" is the quintessential book for understanding deep learning theory, and you can still read it freely online.
Aaron Courville, Book, Deep Learning, Free ebook, Ian Goodfellow, Neural Networks, Yoshua Bengio
- 12 Deep Learning Researchers and Leaders - Sep 23, 2019.
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. From early practitioners and established academics to entrepreneurs and today’s top corporate influencers, this diverse group of individuals is leading the way into tomorrow’s deep learning landscape.
Andrej Karpathy, Andrew Ng, Deep Learning, Demis Hassabis, Fei-Fei Li, Geoff Hinton, Ian Goodfellow, Influencers, Jeremy Howard, Research, Yann LeCun
- ICLR 2019 highlights: Ian Goodfellow and GANs, Adversarial Examples, Reinforcement Learning, Fairness, Safety, Social Good, and all that jazz - May 27, 2019.
We provide an overview of the main themes and topics discussed at this years International Conference on Learning Representations (ICLR).
Adversarial, GANs, Ian Goodfellow, ICLR, New Orleans, Reinforcement Learning, Social Good
- The Rise of Generative Adversarial Networks - Apr 19, 2019.
A comprehensive overview of Generative Adversarial Networks, covering its birth, different architectures including DCGAN, StyleGAN and BigGAN, as well as some real-world examples.
Art, Deepfakes, GANs, Generative Adversarial Network, Ian Goodfellow
- The Essence of Machine Learning - Dec 28, 2018.
And so now, as an exercise in what may seem to be semantics, let's explore some 30,000 feet definitions of what machine learning is.
Aaron Courville, Classification, Ian Goodfellow, Machine Learning, Tom Mitchell, Yoshua Bengio
- Learn from the experts at Google Brain, UC Berkley, Adobe Research & FAIR - Aug 28, 2018.
The World's Biggest Deep Learning Summit is returning to San Francisco in January 2019. Use code SUMMER for an additional 25% off the Super Early Bird Ticket rate by September 7.
CA, Deep Learning, Ian Goodfellow, RE.WORK, San Francisco
- 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning - Apr 17, 2018.
It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.
Book, Data Science, Ian Goodfellow, Machine Learning, Mathematics, Robert Tibshirani, Vladimir Vapnik
- Top 10 Quora Machine Learning Writers and Their Best Advice, Updated - Jun 26, 2017.
Gain some insight on a variety of topics with select answers from Quora's current top machine learning writers. Advice on research, interviews, hot topics in the field, how to best progress in your learning, and more are all covered herein.
Advice, Ian Goodfellow, Machine Learning, Quora, Top 10, Xavier Amatriain, Yoshua Bengio
- AI, Data Science, Machine Learning: Main Developments in 2016, Key Trends in 2017 - Jan 10, 2017.
2017 is here. Check out an encore installation in our "Main Developments in 2016 and Key Trends in 2017" series, where experts weigh in with their opinions.
2017 Predictions, AI, Data Science, Hilary Mason, Ian Goodfellow, Machine Learning, Michael O'Connell, Women
- New Deep Learning Book Finished, Finalized Online Version Available - Apr 12, 2016.
What will likely become known as the seminal book on deep learning is finally finished, with the online version finalized and freely-accessible to all those interested in mastering deep neural networks.
Aaron Courville, Book, Deep Learning, Free ebook, Ian Goodfellow, Yoshua Bengio
- Deep Learning Adversarial Examples – Clarifying Misconceptions - Jul 15, 2015.
Google scientist clarifies misconceptions and myths around Deep Learning Adversarial Examples, including: they do not occur in practice, Deep Learning is more vulnerable to them, they can be easily solved, and human brains make similar mistakes.
Adversarial, Deep Learning, Ian Goodfellow, Myths, Regularization
- (Deep Learning’s Deep Flaws)’s Deep Flaws - Jan 26, 2015.
Recent press has challenged the hype surrounding deep learning, trumpeting several findings which expose shortcomings of current algorithms. However, many of deep learning's reported flaws are universal, affecting nearly all machine learning algorithms.
convnet, Deep Learning, Ian Goodfellow, Machine Learning, Neural Networks, Yoshua Bengio, Zachary Lipton