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All Blog Posts Tagged 'neural' (41)

Object detection with neural networks — a simple tutorial using keras

This article was written by Johannes Rieke.

Abstract

A very lightweight tutorial to object detection in images. We will bootstrap simple images and apply increasingly complex neural networks to them. In the end, the algorithm will be able to detect multiple…

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Added by Andrea Manero-Bastin on December 24, 2020 at 12:28pm — No Comments

What are Recurrent Neural Networks

Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.

Traditional neural networks can’t do…

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Added by Hitesh Dsouza on November 29, 2020 at 9:00am — No Comments

AI Has Become So Human, That You Can’t Tell the Difference

You might be wondering if machines are a threat to the world we live in, or if they’re just another tool in…

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Added by Diego Lopez Yse on September 19, 2020 at 2:00pm — 1 Comment

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

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

Faster Better Cheaper Image Recognition

Summary:  In the literal blink of an eye, image-based AI has gone from high cost, high risk projects to quick and reasonably reliable.  C-level execs looking for AI techniques to exploit need to revisit their assumptions and move these up the list.  Here’s what’s changed.

 

For data scientists these are miraculous times.  We tend to think of miracles as something that occurs instantaneously but in our world that’s not quite so.  Still the rate…

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Added by William Vorhies on March 4, 2019 at 9:41am — No Comments

One Shot Learning and Other Strategies for Reducing Training Data

Summary: Not enough labeled training data is a huge barrier to getting at the equally large benefits that could be had from deep learning applications.  Here are five strategies for getting around the data problem including the latest in One Shot Learning.

 

For at least the last two years we’ve been in an…

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Added by William Vorhies on January 28, 2019 at 9:56am — 1 Comment

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 — No Comments

Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, IBM Watson

planets

For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you’re aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit.

One of ML's…

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Added by Olexander Kolisnykov on September 18, 2018 at 2:52am — No Comments

Democratizing Deep Learning – The Stanford Dawn Project

Summary:  How about we develop a ML platform that any domain expert can use to build a deep learning model without help from specialist data scientists, in a fraction of the time and cost.  The good news is the folks at the Stanford DAWN project are hard at work on just such a platform and the initial results are extraordinary.

 …

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Added by William Vorhies on September 4, 2018 at 8:02am — No Comments

How the incorporation of prior information can accelerate the speed at which neural networks learn while simultaneously increasing accuracy

Deep neural nets typically operate on “raw data” of some kind, such as images, text, time series, etc., without the benefit of “derived” features. The idea is that because of their flexibility, neural networks can learn the features relevant to the problem at hand, be it a classification problem or an estimation problem.  Whether derived or learned, features are important. The challenge is in determining how one might use what one learned from the features in future work (staying…

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Added by Jonathan Symonds on August 30, 2018 at 7:00am — No Comments

AI in Insurance: Business Process Automation Brings Digital Insurer Performance to a New Level

The insurance industry – one of the least digitalized – is not surprisingly one of the most ineffective segments of the financial services industry. Internal business processes are often duplicated, bureaucratized, and time-consuming. As the ubiquity of machine learning and artificial intelligence systems increases, they have the potential to automate operations in insurance companies thereby cutting costs and increasing productivity. However, organizations have plenty of reasons to resist…

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Added by Denys Harnat on August 28, 2018 at 3:35am — No Comments

The Artificial Neural Networks handbook: Part 2

In last part we have seen the basics of Artificial intelligence and Artificial Neural Networks. As mentioned in the last part this part will be focused on applications of Artificial neural networks. ANN is very vast concept and we can find its…

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Added by Jayesh Bapu Ahire on August 25, 2018 at 9:00pm — No Comments

Combining CNNs and RNNs – Crazy or Genius?

Summary: There are some interesting use cases where combining CNNs and RNN/LSTMs seems to make sense and a number of researchers pursuing this.  However, the latest trends in CNNs may make this obsolete.

 

There are things that just don’t seem to go together.  Take oil and water for instance.  Both valuable, but try putting…

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Added by William Vorhies on August 14, 2018 at 7:34am — 3 Comments

Going Deeper: More Insight Into How and What Convolutional Neural Networks Learn

In my earlier post I discussed how performing topological data analysis on the weights learned by convolutional neural nets (CNN’s) can give insight into what is being learned and how it is being learned.  

The significance of this work can be summarized as follows:

  1. It…
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Added by Jonathan Symonds on August 9, 2018 at 11:30am — No Comments

Best Machine Learning Tools: Experts’ Top Picks

The best trained soldiers can’t fulfill their mission empty-handed. Data scientists have their own weapons  machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts.

And that’s why we interviewed data science practitioners — gurus, really —regarding the useful tools they…

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Added by Kateryna Lytvynova on July 13, 2018 at 2:00am — No Comments

Building Recurrent Neural Networks in Tensorflow

Recurrent Neural Nets (RNN) detect features in sequential data (e.g. time-series data). Examples of applications which can be made using RNN’s are anomaly detection in time-series data, classification of ECG and …

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Added by Ahmet Taspinar on July 5, 2018 at 11:48am — No Comments

Using Topological Data Analysis to Understand the Behavior of Convolutional Neural Networks

TLDR: Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way. The implications of the finding are profound and can accelerate the development of a wide range of applications from self-driving everything to GDPR.

Introduction

Neural networks have demonstrated a great…

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Added by Jonathan Symonds on June 21, 2018 at 9:30am — No Comments

Image identification using a convolutional neural network

This blog  explores a typical image identification task using a convolutional ("Deep Learning") neural network. For this purpose we will use a simple JavaCNN packageby D.Persson, and make our example small and concise using the Python scripting language. This example can also be rewritten in Java, Groovy, JRuby or any scripting language supported by the Java virtual machine.



This example will use images in the grayscale format (PGM). The name "PGM" is an acronym derived from…

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Added by jwork.ORG on May 31, 2018 at 1:30pm — No Comments

Using Neural Networks for sales prospecting

"No one wants to be sold but everyone wants to buy."

Most of us hate being sold. The moment we know someone is selling something, we keep our guards up. 

In the book, The Challenger Sale, authors Mathew Dixon and Brent Adamson surveyed over 6000 salespeople from around the world and found that ‘challenger salespeople’ outperformed every other group. Who are these challenger salespeople? These…

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Added by Rudradeb Mitra on March 1, 2018 at 9:00pm — No Comments

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