We investigate contextual online learning with nonparametric (Lipschitz) comparison classes under different assumptions on losses and feedback information. For full information feedback and Lipschitz losses, we design the first explicit algorithm achieving the minimax regret rate (up to log factors). In a partial feedback model motivated by second-price auctions, we obtain algorithms for Lipschitz and semi-Lipschitz losses with regret bounds improving on the known bounds for standard bandit feedback. Our analysis combines novel results for contextual second-price auctions with a novel algorithmic approach based on chaining. When the context space is Euclidean, our chaining approach is efficient and delivers an even better regret bound.

Keras is a powerful easy-to-use Python library for developing and evaluating deep learning models. It wraps the efficient numerical computation libraries Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code. In this post, you will discover how to create your first neural network model in Python using Keras. Develop Your First Neural Network in Python With Keras Step-By-Step Photo by Phil Whitehouse, some rights reserved. There is not a lot of code required, but we are going to step over it slowly so that you will know how to create your own models in the future.

For all the excitement around artificial intelligence and other automation technologies, we've only seen a fraction of the opportunities automation will create. With all the change still to come, it's worth considering now the role these technologies will play in your company's future. The practical benefits of automation are many: fewer data-entry errors, faster customer service response times, workload automation, better resource management, and the ability to turn legacy data into powerful insights. These functions enable your company to operate more efficiently. They also empower your employees to excel in new and exciting ways.

Editor's note: The TensorFlow API has undergone changes since this series was first published. However, the general ideas are the same, and an otherwise well-structured tutorial such as this provides a great jumping off point and opportunity to consult the API documentation to identify and implement said changes. In this tutorial I'll explain how to build a simple working Recurrent Neural Network in TensorFlow. This is the first in a series of seven parts where various aspects and techniques of building Recurrent Neural Networks in TensorFlow are covered. A short introduction to TensorFlow is available here.

This online course is designed to teach you how to create deep learning Algorithms in Python by two expert Machine Learning & Data Science experts. This course is split into 32 sections which cover over 179 Artificial Neural Network topics using a video format – receive a certificate of completion at the end of the course. Online learning is very flexible (expiry dates may vary from course to course depending on the course provider). Artificial intelligence is growing exponentially. There is no doubt about that.

Which machine learning algorithm should I use?

Recitations from Tel-Aviv University introductory course to computer science, assembled as IPython notebooks by Yoav Ram. Exploratory Computing with Python, a set of 15 Notebooks that cover exploratory computing, data analysis, and visualization. No prior programming knowledge required. Each Notebook includes a number of exercises (with answers) that should take less than 4 hours to complete. Developed by Mark Bakker for undergraduate engineering students at the Delft University of Technology.

I have a background in Computer Science and worked with nearly every programming language on the planet. I graduated with highest distinction during my masters program. I've worked on projects ranging from Robotics, Web Apps, Mobile Apps to Embedded Systems. These courses will help you achieve your goals.

I've split this post into four sections: Machine Learning, NLP, Python, and Math. For future posts, I may create a similar list of books, online videos, and code repos as I'm compiling a growing collection of those resources too. What's the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?

Which machine learning algorithm should I use?