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Towards Data Science 08/20/2019 21:31
Sugar, Flower, Fish or Gravel — Now a Kaggle competition. I am very happy to announce the launch of our Kaggle competition “”. This competition is the culmination of literally hundreds of hours of human labor from dozens of scientists. The challenge is to segment satellite images into one of four classes. Typically, when we think about different cloud types we think of cumulus, stratus and cirrus. The four classes in this challenge, however, are called Sugar, Flower, Fish and Gravel. So, what the heck are those? It all started around two years ago at a workshop where 12 cloud experts came together to discuss shallow clouds over the ocean. These clouds look benign compared to big thunderstorms but, in fact, for the Earth’s climate they play a.
Towards Data Science 08/20/2019 18:43
Photo by on. Reinforcement learning as-is is a pretty hard topic. When I started to dig deeper, I realized the need for a good explanation. This article, coupled with the code is my school project. I am currently in a sophomore year of high school, and I understand the hard mathematical concepts in a more ‘social studies’ kind of way. I hope this article proves to be helpful for newcomers like me. I created a GitHub project you can clone and follow along! Make sure to You can download everything that I processed on my PC in the downloads section. As well as FAQ, dataset description, some docs, how-tos and more. It is frequently updated. I haven’t pushed for a week because I was writing this article. I hope you like it! This article provides
Towards Data Science 08/20/2019 18:43
Photo by. Towards a more responsible development of artificial intelligence with a research paper from OpenAI. The 10th of July team members of OpenAI released a paper on arXiv called by , Miles Brundage and . One of the main statements in the article goes as follows: “Competition between AI companies could decrease the incentives of each company to develop responsibly by increasing their incentives to develop faster. As a result, if AI companies would prefer to develop AI systems with risk levels that are closer to what is socially optimal — as we believe many do — responsible AI development can be seen as a collective action problem” Therefore how is it proposed we approach this problem? Responsible AI development? AI in health and wellbein.
Towards Data Science 08/20/2019 18:40
AI is transforming politics — for both good and bad. Big Data powering Big Money, the return of direct democracy, and the tyranny of the minority. Source: Pixabay. Nowadays, artificial intelligence (AI) is one of the most widely discussed phenomena. AI is poised to fundamentally alter almost every dimension of human life — from healthcare and social interactions to military and international relations. However, it is worth considering the effects of the advent of AI in politics — since politics are one of the fundamental pillars of today’s societal system, and understanding the dangers that AI poses for politics is crucial to combat AI’s negative implications, while at the same time maximizing the benefits stemming from the new opportunities i.
Towards Data Science 08/20/2019 13:49
Meta-transfer Learning for Few-shot Learning. Abstract. Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, meta-learning typically uses shallow neural networks (SNNs), thus limiting its effectiveness. In this paper we propose a novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks . Specifically, meta refers to training multiple tasks, and transfer is achieved by learning
Towards Data Science 08/20/2019 13:48
Learn how the perceptron algorithms work and the intuition behind them. The basic perceptron algorithm was first introduced by in the late 1950s. It is a binary linear classifier for supervised learning. The idea behind the binary linear classifier can be described as follows. where x is the feature vector, θ is the weight vector, and θ ₀ is the bias. The sign function is used to distinguish x as either a positive (+1) or a negative (-1) label. There is the decision boundary to separate the data with different labels, which occurs at. The decision boundary separates the hyperplane into two regions. The data will be labeled as positive in the region that θ⋅ x + θ ₀ > 0, and be labeled as negative in the region that θ⋅ x + θ ₀ < 0. If all the
Towards Data Science 08/20/2019 13:47
This is a quick dive into the trove of Chinese state troll tweets released by Twitter on Aug 19. More to come in the coming days and weeks. An example of Chinese state troll tweet exposed by Twitter on Aug 19. On August 19, that the company said were from “a significant state-backed information operation focused on the situation in Hong Kong, specifically the protest movement and their calls for political change.". The tweets deserve a deeper examination, and clearly more can be done with the material. I had previously worked on a . Due to time constraints, here’s a quick and dirty first exploratory look at the data. More to come in the coming days and weeks. 1. DATA, NOTEBOOK AND ASSUMPTIONS. My rough notebook is , and the repo will be updat.
Towards Data Science 08/20/2019 13:47
Part 1 of a two part series. The weight initialization technique you choose for your neural network can determine how quickly the network converges or whether it converges at all. Although the initial values of these weights are just one parameter among many to tune, they are incredibly important. Their distribution affects the gradients and, therefore, the effectiveness of training. In neural networks, weights represent the strength of connections between units in adjacent network layers. The linear transformation of these weights and the values in the previous layer passes through a non-linear activation function to produce the values of the next layer. This process happens layer to layer during forward propagation; through back propagatio.
Towards Data Science 08/20/2019 13:46
tl;dr 1. data science practice is full of waste 2. explicit hypothesis testing helps to finetune ideas 3. communication is the key for integrating data scientists into the software development lifecycle. I joined this field because of the excitement that we feel upon discovering new patterns in data. With time though, it became clear that identifying patterns is just the first half of this journey. There is another part, which is about sharing this discovery. ‘Sharing’ can be in the form of a presentation, in the form of a change in an existing product or even an entirely new product. Thus the two parts together — nurturing ideas from inception to a product — is the full stack of data science. In this post, I’m sharing my experience on how t.
Towards Data Science 08/20/2019 09:45
Photo by on. Reduce your test execution time with mock. Agenda. This post will cover when and how to use unittest.mock library. Python docs aptly describe the mock library:. unittest.mock allows you to replace parts of your system under test with mock objects and make assertions about how they have been used. When to use mock library. The short answer is ‘most of the times’.
Towards Data Science 08/20/2019 09:45
Introducing “data downtime” and its importance to data-driven organizations. Photo by on. Has it ever happened to you that your CEO looked at a report you showed and said the numbers look way off? Has a customer ever called out incorrect data in your product’s dashboards? I’m sure it hasn’t happened to you specifically, but perhaps you have a friend who had this problem? :). In 2016, while I was leading a team at Gainsight, fondly called Gainsight on Gainsight (GonG), I became all too familiar with these issues. We were responsible for our customer data and analytics, including key reports reviewed weekly by our CEO and quarterly by our board of directors. Seemingly every Monday morning, I would wake up to a series of emails about errors in th.
Towards Data Science 08/20/2019 09:45
Ultimately it is small steps which make huge impact. Data imputation is an important part of data preparation stage while executing any machine learning project. In pure statistical term, it is a process of replacing missing data with some meaningful substitute. For example, look at below sample data which is subset of auto data set picked from Kaggle. Source:. There are 6 rows which have missing data for horsepower feature. So as part of data preparation stage, we need to get rid of missing data. There can be two approaches by which we can get rid of missing data:. We can drop off all 6 rows from actual data set. Add some meaningful substitute for missing data. Any guess!!! which approach will give improvement in terms of model accuracy? Keep.
Towards Data Science 08/20/2019 09:45
R² is a popular statistic for evaluating linear regression models, but it can be easily misapplied, as I discovered. Data science is an iterative process, which is especially true for new practitioners. We can spend weeks on a project, only to later discover a fundamental flaw in the analysis. This post explores a basic mistake I made on a project, and working through how it happened and why it matters. My was a predictive analytics tool that utilized historical sales data and weather data to forecast nightly and weekly sales for an award-winning restaurant in Brooklyn. Historical sales data were pulled directly from the restaurant’s point of sale system (two separate systems that had to be aggregated, actually), and while the project was f.

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