Climate change has increased the intensity, frequency, and duration of extreme weather events and natural disasters across the world. While the increased data on natural disasters improves the scope of machine learning (ML) in this field, progress is relatively slow. One bottleneck is the lack of benchmark datasets that would allow ML researchers to quantify their progress against a standard metric. The objective of this short paper is to explore the state of benchmark datasets for ML tasks related to natural disasters, categorizing them according to the disaster management cycle. We compile a list of existing benchmark datasets introduced in the past five years. We propose a web platform - NADBenchmarks - where researchers can search for benchmark datasets for natural disasters, and we develop a preliminary version of such a platform using our compiled list. This paper is intended to aid researchers in finding benchmark datasets to train their ML models on, and provide general directions for topics where they can contribute new benchmark datasets.

I like to stay up-to-date with what's happening in the field of ML because this is a field that can surprise you almost everyday! Which is better OpenCV or Tensorflow? To some, this is not a valid question. To others, this is a question worth thinking about. The simplest answer is that Tensorflow is better than OpenCV and OpenCV is better than Tensorflow!

See how we can analyse movie reviews using DL4J in Java (NLP feature) just using command-line tools and a cloud platform to train and evaluate the language model (using a pre-trained model).

In machine learning tasks, especially in the tasks of prediction, scientists tend to rely solely on available historical data and disregard unproven insights, such as experts' opinions, polls, and betting odds. In this paper, we propose a general three-step framework for utilizing experts' insights in machine learning tasks and build four concrete models for a sports game prediction case study. For the case study, we have chosen the task of predicting NCAA Men's Basketball games, which has been the focus of a group of Kaggle competitions in recent years. Results highly suggest that the good performance and high scores of the past models are a result of chance, and not because of a good-performing and stable model. Furthermore, our proposed models can achieve more steady results with lower log loss average (best at 0.489) compared to the top solutions of the 2019 competition (>0.503), and reach the top 1%, 10% and 1% in the 2017, 2018 and 2019 leaderboards, respectively.

Here's what we need to be able to get started: The how part of this task is not hard once we have our goals and requirements clear, we will expand on this in the following sections. We have all of the code and instructions needed to get started with this post, captured for you on GitHub.

Every machine learning lifecycle starts with a business problem. It can be about the increase in sales, make predictions, cutting costs or whatever it is that brings profits to the organisation. Once a business problem is classified under artificial intelligence or machine learning, the next question is -- what data will be used to solve it? Answering this question is followed by integrating pipelines where the data gets processed, exploratory data analysis is carried out and feature engineering is done. In order to meet the growing demands of the customers, companies which use data platforms have to handle the scale, agility, and flexibility to combine different types of data and analytics approaches which will allow them to transform data into a valuable corporate asset.

With the war on AI talent heating up, the new "unicorns" of Silicon Valley are high-performing data scientists. Although as recently as 2015 there was a surplus of data scientists, in the most recent quarter there was a 150,000 deficit. This quant crunch will only grow deeper as the gap between the demand for these experts in developing machine learning models is not met with the supply from graduate programs. How do leading companies take steps to mitigate the damage of the quant crunch on their ability to earn a return on machine learning and AI investments? They empower the experts that they do have with a combination of tools and techniques that automate as much of the tedious components of the modeling process as possible.

Editor's note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. While our implementations are decent enough, they are not optimized enough to work well on large corpora. The gensim framework, created by Radim Řehůřek consists of a robust, efficient and scalable implementation of the Word2Vec model. We will leverage the same on our Bible corpus. In our workflow, we will tokenize our normalized corpus and then focus on the following four parameters in the Word2Vec model to build it.

So in today's post I am not going to perform any Machine Learning task rather it is preprocessing step for DNA and protein data. Please note, I took portion of the code from this blog post and this blog post. So if you want to know more about bioinformatic or one hot encoding, check those blog posts out.