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All Blog Posts Tagged 'deep' (59)

Can Deep Learning Address Limited Bandwidth and Other Video Streaming Challenges?

We all experience video streaming issues, probably on a daily basis. But now that people aren’t just streaming for entertainment, but are relying on it in many cases to perform their jobs, it ups the ante to address some of these issues and provide an improved experience. Deep…

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Added by John Burke on March 19, 2021 at 2:00pm — 1 Comment

From Data Collection to Text Interpretation. An interview on exploring techniques and use cases for text mining

Meet two text mining experts in today’s interview, which explores some of the common issues faced by data scientists in text analytics. Prof. Dursun Delen and…

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Added by Rosaria Silipo on November 15, 2020 at 12:00am — No Comments

Genius Tool to Compare Best Time-Series Models For Multi-step Time Series Modeling

Predict Number of Active Cases by Covid-19 Pandemic based on Medical Facilities (Volume of Testing, ICU beds, Ventilators, Isolation Units, etc) using Multi-variate…

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Added by Sharmistha Chatterjee on October 17, 2020 at 10:30pm — No Comments

4 Steps to Building a Video Search System

Image for post

As its name suggests, searching for videos by image is the process of retrieving from the repository videos containing similar frames to the input image. One of the key steps is to turn…

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Added by Kate Shao on September 11, 2020 at 10:30pm — No Comments

The Journey to Optimizing Billion-scale Image Search



Yupoo Picture Manager serves tens of millions of users and manages tens of billions of pictures. As its user gallery is growing larger, Yupoo has an urgent business need for a solution that can quickly locate the image. In other words, when a user inputs an image, the system should find its original…

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Added by Kate Shao on August 12, 2020 at 11:00pm — No Comments

How does the Automated Valuation Model work in the real estate industry?

If you were to invest in real estate, what would be the most important factor you’d take into account? Would it be the age of the building, its location, or maybe how many owners it previously had?

While all of the above will certainly be important to you (though to varying extents), there’s one universal factor that will either be a deal-breaker or make you want to go all in.

You’ve guessed it — the price.

We all have a rough overview of…

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Added by Mario Inter on July 29, 2020 at 1:42am — No Comments

Introduction to Dropout to regularize Deep Neural Network

Dropout means to drop out units which are covered up and noticeable in a neural network. Dropout is a staggeringly in vogue method to overcome overfitting in neural networks.

Deep Learning framework is now getting further and more profound. With these bigger networks, we can accomplish better prediction exactness. However, this was not the case a few years ago. Deep…

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Added by saurav singla on July 28, 2020 at 12:12am — No Comments

Vector Similarity Search Engine

Milvus aims to achieve efficient similarity search and analytics for massive-scale vectors. A standalone Milvus instance can easily handle vector search for billion-scale vectors. However, for 10 billion, 100 billion, or even larger datasets, a Milvus cluster is needed. The cluster can be used as a standalone instance for upper-level applications and can meet the business needs of low latency, high concurrency for massive-scale data. A Milvus cluster can resend requests, separate reading…

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Added by Kate Shao on July 24, 2020 at 9:30pm — No Comments

Building a Deep-Learning-Based Movie Recommender System

With the continuous development of network technology and the ever-expanding scale of e-commerce, the number and variety of goods grow rapidly and users need to spend a lot of time to find the goods they want to buy. This is information overload. To solve this problem, the recommendation system came into being.

The recommendation system is a…

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Added by Kate Shao on July 5, 2020 at 11:30pm — No Comments

The Easiest Way to Search Among 1 Billion Image Vectors And Why Vector Search Is Important?

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Added by Kate Shao on May 26, 2020 at 6:00pm — No Comments

Differential ML on TensorFlow and Colab

Brian Huge and I just posted a working paper following six months of…

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Added by Antoine Savine on May 25, 2020 at 11:30am — No Comments

Wind Turbine Surface Damage Detection using Deep Learning Algorithm

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Added by Ramesh on April 10, 2020 at 2:00pm — No Comments

Visually Explained: How Can Executives Grasp What Programming Is All About?

Quite often, non-technical executives have difficulties understanding what programming, on a very fundamental level, is all about. Because of that knowledge-gap, they tend to hire and overburden experienced data professionals with tasks which they are hopelessly overqualified for. Such as, for example, doing ad-hoc SQL queries on CRM data: "You're the go-to-guy for all things data, and we need the results for the board meeting tomorrow." That's a quite humbling and frustrating…

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Added by Rafael Knuth on December 5, 2019 at 6:30am — No Comments

No Matter What You Call It, It’s all the Same Thing

Summary:  A little history lesson about all the different names by which the field of data science has been called, and why, whatever you call it, it’s all the same thing.

 

A little reminiscence, or for those of you who are only recently data scientists, a little history lesson. 

Our profession of…

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Added by William Vorhies on December 4, 2019 at 3:12pm — No Comments

Visually Explained: How Can Executives Make Sense of Machine Learning & Deep Learning?

Many executives struggle to make sense of machine learning (ML) and deep learning (DL). Having a pragmatic relationship with technology, executives need to know on a very fundamental level: "What problems do ML & DL try to solve?" A simple, high-level answer to that question is: "It's all about building systems that do certain things better than humans, with as little intervention by humans as possible." That being said, the simplest way to distinguish between ML and its branch DL…

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Added by Rafael Knuth on November 19, 2019 at 12:30pm — No Comments

Recognizing Animals in Photos: Building an AI model for Object Recognition

Our model for recognizing specific animals in images is a neural network consisting of multiple layers, and the initial layers are already good at understanding the world in general. So instead of “re-inventing the wheel,” we only need to train the final layers.

I was excited to work on a recent project with one of our partners, Wild Detect, because it aligns with one of our goals at Appsilon — to use data science consulting to aid in the…

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Added by Michał Frącek on June 25, 2019 at 1:13am — No Comments

Should You Be Recommending Deep Learning Solutions in Your Company?

Summary:  If you are guiding your company’s digital journey, to what extent should you be advising them to adopt deep learning AI methods versus traditional and mature machine learning techniques.

 

By now everyone is at least familiar with using AI/ML as a required cornerstone of company strategy.  Frequently…

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Added by William Vorhies on May 20, 2019 at 8:33am — 1 Comment

Where Do We Stand With GANs?

Summary:  GANs (Generative Adversarial Nets) originally thought to be a tool for inexpensively creating DNN training data have instead become the tools for creating deep fake images.  However the deep fake technology is now finding uses in the commercial world and promises valuable results.

 

Generative…

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Added by William Vorhies on May 6, 2019 at 7:57am — No Comments

Deep Learning picking momentum in option pricing and financial risk management

Deep Learning is picking momentum in Quantitative Finance, outside the obvious application to the prediction of asset prices (where to my knowledge it is not particularly effective) and spreading into the more serious application area of option pricing and risk management.

These two recent papers clearly demonstrate the benefits of DL as a pricing technology alternative to the classical FDM and Monte-Carlo in certain contexts:…

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Added by Antoine Savine on January 11, 2019 at 5:30am — No Comments

Things that Aren’t Working in Deep Learning

Summary:  This may be the golden age of deep learning but a lot can be learned by looking at where deep neural nets aren’t working yet.  This can be a guide to calming the hype.  It can also be a roadmap to future opportunities once these barriers are behind us.

 

We are living in the golden age of deep…

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Added by William Vorhies on November 18, 2018 at 11:14am — 1 Comment

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