# Tag: Ian Goodfellow (10)

**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).**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.**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.**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.**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.**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.**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.**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.**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.**(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.