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In this article, we’ll have a look at how you can implement a content-based recommendation system using Python and the scikit-learn library. But before diving straight into this, it’s important to have some prerequisite knowledge of the different ways by which recommendation systems can recommend an item to users. Content-based: A content-based recommendation finds similar items to a given item by examining the item’s properties, such as its title or description, category, or dependencies on other items (for example, electronic toys require batteries).

Uber built Manifold, a model-agnostic visualization tool for ML performance diagnosis and model debugging, to facilitate a more informed and actionable model iteration process. Machine learning (ML) is widely used across the Uber platform to support intelligent decision making and forecasting for features such as ETA prediction and fraud detection. For optimal results, we invest a lot of resources in developing accurate predictive ML models.

Some of the most exciting advances in AI recently have come from the field of deep reinforcement learning (deep RL), where deep neural networks learn to perform complicated tasks from reward signals. RL operates similarly to how you might teach a dog to perform a new trick: treats are offered to reinforce improved behavior. Recently, deep RL agents have exceeded human performance in benchmarks like classic video games (such as Atari 2600 games), the board game Go, and modern computer games like DOTA 2.

At Facebook, we think that artificial intelligence that learns in new, more efficient ways – much like humans do – can play an important role in bringing people together. That core belief helps drive our AI strategy, focusing our investments in long-term research related to systems that learn using real-world data, inspiring our engineers to share cutting-edge tools and platforms with the wider AI community, and ultimately demonstrating new ways to use the technology to benefit the world. In 2018, we made important progress in all these areas.

 With the problem of Image Classification is more or less solved by Deep learning, Text Classification is the next new developing theme in deep learning. For those who don’t know, Text classification is a common task in natural language processing, which transforms a sequence of text of indefinite length into a category of text. How could you use that? Source: mlwhiz.com

Uber AI Labs introduces a method for making neural networks that process images faster and more accurately by leveraging JPEG representations. Neural networks, an important tool for processing data in a variety of industries, grew from an academic research area to a cornerstone of industry over the last few years. Convolutional Neural Networks (CNNs) have been particularly useful for extracting information from images, whether classifying them, recognizing faces, or evaluating board positions in Go.

DeepMind, the London-based subsidiary of Alphabet, has createda system that can quickly master anygame in the classthat includes chess, Go, and Shogi, and do sowithouthuman guidance. The system, called AlphaZero, began its lifelast yearby beating a DeepMind system that had been specialized just for Go. That earlier system haditself made history by beating one of the world’s best Go players, but it needed human help to get through a months-long course of improvement.

A framework for using convolutional neural networks (CNNs) on satellite imagery to identify the areas most severely affected by a disaster. This new method has the potential to produce more accurate information in far less time than current manual methods. Ultimately, the goal of this research is to allow rescue workers to quickly identify where aid is needed most, without relying on manually annotated, disaster-specific data sets. Researchers train models on CNNs to detect human-made features, such as roads. Existing approaches for disaster impact analysis require data sets that can be expensive to produce because they require time-consuming manual annotation (for instance, annotating buildings damaged by fire as a new class) to train on. This new method only uses general road and building data sets.

Data visualization is a key step to understand the dataset and draw inferences from it. While one can always closely inspect the data row by row, cell by cell, it’s often a tedious task and does not highlight the big picture. Visuals on the other hand, define data in a form that is easy to understand with just a glance and keeps the audience engaged. Matplotlib is a basic library that provides options for various plots along with extensive customizations in the form of labels, title, font size etc. I watched numerous videos and read articles online for visualization. To understand Matplotlib better, I took the population density dataset from Kaggle and started creating my own visualizations.

Trying to keep up with AI research papers can feel like an exercise in futility given how quickly the industry moves. If you’re buried in papers to read that you haven’t quite gotten around to, you’re in luck. To help you catch up, we’ve summarized 10 important AI research papers from 2018 to give you a broad overview of machine learning advancements this year. There are many more breakthrough papers worth reading as well, but we think this is a good list for you to start with. We’ve done our best to summarize these papers correctly, but if we’ve made any mistakes, please contact us to request a fix. If these summaries of scientific AI research papers are useful for you, you can subscribe to our AI Research mailing list at the bottom of this article to be alerted when we release new summaries.