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Towards Data Science 10/16/2019 23:25
The primary way to incorporate machine learning into applications is by deploying a trained model as a web API on cloud infrastructure. Running inference at scale requires an understanding of several engineering design principles that may have more to do with DevOps than Data Science. Running machine learning in production is not just about deploying models, but also ensuring your deployments are maintainable, your spend is optimized, and your web services are scalable. In this post, I’m going to highlight and explain concepts that are relevant for building infrastructure for real-time inference at scale on AWS. 1. Containerization. The atomic unit of an efficient inference cluster should be a container per model. This abstraction enables au.
Towards Data Science 10/16/2019 22:53
There are many open-source face recognition packages like which you can easily install on Linux servers. But it is very difficult or impossible to deploy them on mobile and IoT devices. One option is to use machine learning mobile frameworks such as to call pre-trained models. But are there easier options? Yes! With 5G coming, it will take only 0.01 second to upload a 100KB image at a speed of about , so we can deploy almost everything including face recognition as a service on server-side and a light app on client-side. This post will demonstrate how to build a RESTful API for on Linux servers using Python . Face_recognition Project. is an awesome open source project for face recognition based on , just as described by itself:. The world’s s.
Towards Data Science 10/16/2019 19:15
Setting the right course and steering responsibly. This article is coauthored by and . Setting the right course. Rapid adoption of complex machine learning (ML) models in recent years has brought with it a new challenge for today’s companies: how to interpret, understand, and explain the reasoning behind these complex models’ predictions.
Towards Data Science 10/16/2019 13:13
This post is a summary of the theory of nonlinear dynamics and chaos that I have recently learned from an online course by the Santa Fe Institute. All materials are credited to the institute alone. Dynamics is a branch of mathematics that studies how systems change over time. Up until the 18th century, people believed that the future could be perfectly predicted given that one knows “all forces that set nature in motion, and all positions of all items of which nature is composed” (that one being is referred to as a Laplace Demon). Now, provided that we believe that the world is fully deterministic, then the statement makes sense. The problem is that in reality, measured values (e.g. of forces and positions) are often approximated. What we d.
Towards Data Science 10/16/2019 12:42
Feature selection always plays a key role in machine learning. image by. We always wonder where the Chi-Square test is useful in machine learning and how this test makes a difference. Feature selection is an important problem in machine learning, where we will be having several features in line and have to select the best features to build the model. The chi-square test helps you to solve the problem in feature selection by testing the relationship between the features. In this article, I will guide through. a. Chi-Square distribution. b. Chi-Square Test for Feature Selection. c. Chi-Square Test using Python. Chi-Square distribution. A random variable ꭓ follows chi-square distribution if it can be written as a sum of squared standard normal varia.
Towards Data Science 10/16/2019 12:32
Meta-Learning describes the abstraction to designing higher level components associated with training Deep Neural Networks. The term “ Meta-Learning ” is thrown around in Deep Learning literature frequently referencing “ AutoML ”, “ Few-Shot Learning ”, or “ Neural Architecture Search ” when in reference to the automated design of neural network architectures. Emerging from comically titled papers such as “ Learning to learn by gradient descent by gradient descent ”, the success of OpenAI’s rubik’s cube robotic hand demonstrates the maturity of the idea. Meta-Learning is the most promising paradigm to advance the state-of-the-art of Deep Learning and Artificial Intelligence. OpenAI set the AI world on fire by demonstrating ground-breaking c.
Towards Data Science 10/16/2019 12:21
Recently, more and more attention is focussed on the interpretability of the Machine Learning models mainly Deep Learning ones because of their black-box nature. One such important Deep Learning architecture used is Convolutional Neural Networks(CNNs) which has made a breakthrough in Computer Vision including image classification, object detection, semantic segmentation, Instance segmentation, image captioning etc. The progress in the refinement and development of CNNs has been exponential and the architectures have been greatly simplified but the prediction results cannot be decomposed into intuitive and completely understandable parts. Understanding and Interpreting models are crucial to building people’s confidence in our system. The mos.
Towards Data Science 10/16/2019 11:29
Photo by on. A few months ago, Fr Philip Larrey published his book called “”. It discusses the need for developing humane Artificial Intelligence (AI). In this article, we will explain what would happen if we have an inhumane AI. First of all, what does inhumane mean? Inhuman — “lacking human qualities of compassion and mercy; cruel and barbaric.”Primarily, when we say Artificial Inhumanity, we are referring to an AI which is not concerned with humans. It does not exhibit any human feeling, and humans are just animate objects roaming our world. Even though AI was initially conceived to serve humans, we do not exclude the possibility of eventually having an AI, which ultimately only serves its interests. If that happens, then we are definite.
Towards Data Science 10/16/2019 11:29
Side Notes: Releasing a 4 hour Reinforcement Learning course for beginners and pros. Note: If you want robots 🤖 in your home, and would like to see that happen sooner rather than later , then please take our very short survey. Your responses help guide our simulated environment research and robotics projects 👇👇👇. Give 3 minutes of your time:. Thank you kindly! In addition to our serialised blogs, ‘ ’ & ‘ ’, we’ll occasionally be releasing pieces & materials as stand-alones or short-run series. Speaking to the off-topic, and somewhat ambiguous nature of these materials when contrasted with our other publications, we’ve elected to call it ‘ Side Notes ’. ‘ Side Notes ’ will be just that. Ancillary pieces related to & AI, that we think our reader.
Towards Data Science 10/16/2019 10:56
How to Create Multiple Worksheets From a List of Column Values and Delete Any Empty Columns Automatically Using R and VBA. Photo by on. Sometimes, we face problems that we think are very easy to solve, but actually not so easy in real life. Two good examples of these are splitting a large worksheet into multiple worksheets based on cell values and deleting a big number of empty columns for each worksheet. Let me explain to you in more detail. What are the problems? Let’s say we received a file like this and want to create multiple worksheets based on family values. There are 189 different family values (=189 sheets). Manually doing this task or using a pivot table is not so efficient and can possibly lead to some memory issues. For this reaso.
Towards Data Science 10/16/2019 10:54
Today, with the deepening of the concept of data operation, more and more companies are aware of the value of data-driven business strategies and emphasize the participation of all employees in data operations. Developing the ability to analyze data is also a future trend. So do you know how to be a good data analyst and how to stay competitive in the job market? Here are the 6 key skills that data analysts need to master. From. 1. Excel. Excel is a common in daily work. And it is easy to get started with this tool. It can not only do simple two-dimensional tables, complex nested tables, but also create line charts, column charts, bar charts, area charts, pie charts, radar charts, combo charts, scatter charts, etc. Besides, Excel can implemen.

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