Flexible solutions (up to 16 GPU per node) for every budget: GTX1080 / GTX1080Ti / Tesla P100 / Tesla K80 / Tesla M60 / DGX1 

Boosted decision trees are responsible for more than half of the winning solutions in machine learning challenges hosted at Kaggle, according to KDNuggets. In addition to superior performance, these algorithms have practical appeal as they require minimal tuning. In this post, we evaluate two popular tree boosting software packages: XGBoost and LightGBM, including their GPU implementations.

How to evolve your neural network. AutoML: time to evolve.

Python for data analysis. Why this language is popular among data analytics ? This tutorial is Complete overview for learning python for data analysis.

This blogpost will give an introduction to the architecture and ideas behind TensorForce, a new reinforcement learning API built on top of TensorFlow.

The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.

This report is targeted to groups who are subject matter experts in their application but deep learning novices. It contains practical advice for those interested in testing the use of deep neural networks on applications that are novel for deep learning. We suggest making your project more manageable by dividing it into phases. For each phase this report contains numerous recommendations and insights to assist novice practitioners.

Ensemble Learning is a type of Supervised Learning Technique in which the basic idea is to generate multiple Models on a training dataset and then simply combining(average) their Output Rules or their Hypothesis Hx to generate a Strong Model which performs very well and does not overfits and which balances the Bisa-Variance Tradeoff too.

This post is adapted from Section 3 of Chapter 9 of book, Deep Learning with Python (Manning Publications). It is part of a series of two posts on the current limitations of deep learning, and its future.

Data products have always been an instrumental part of Airbnb’s service. However, we have long recognized that it’s costly to make data products. For example, personalized search ranking enables…

Most successful examples of neural nets today are trained with supervision. However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly. An alternative to labelling huge amounts of data is to use synthetic images from a simulator. This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images. To help close this performance gap, we’ve developed a method for refining synthetic images to make them look more realistic. We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.

iNaturalist Dataset + Kaggle competition results.

AI Ukraine – is a professional forum for meeting peers, sharing experiences and discussing the current issues in the field of data mining, machine learning, text mining, Big Data, Robotics, Computer Vision, and other areas of AI.

In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously.

We are looking for a smart Data Scientist with excellent mathematics skills to help us to build the most sophisticated data leak protection cloud-based solution.

We’re looking for a Data Scientist to join Snap Ukraine team! You will be tasked with creating inventive, data-based approaches to solving difficult business problems. Working from our office in Odessa, Ukraine you’ll collaborate with the Revenue Product and Product Marketing teams to transform business questions into solutions backed by data analysis.