# Tag: Convolutional Neural Networks

**Deep Learning for Object Detection: A Comprehensive Review**- Oct 6, 2017.

By the end of this post, we will hopefully have gained an understanding of how deep learning is applied to object detection, and how these object detection models both inspire and diverge from one another.**Object Detection: An Overview in the Age of Deep Learning**- Sep 13, 2017.

Like many other computer vision problems, there still isn’t an obvious or even “best” way to approach the problem of object recognition, meaning there’s still much room for improvement.**Detecting Facial Features Using Deep Learning**- Sep 4, 2017.

A challenging task in the past was detection of faces and their features like eyes, nose, mouth and even deriving emotions from their shapes. This task can be now “magically” solved by deep learning and any talented teenager can do it in a few hours.**Connecting the dots for a Deep Learning App**- Aug 31, 2017.

We show how to build a Deep Learning app which does sentiment analysis on movie reviews. Try it yourself!**Using AI to Super Compress Images**- Aug 21, 2017.

Neural Network algorithms are showing promising results for different complex problems. Here we discuss how these algorithms are used in image compression.**KDnuggets™ News 17:n31, Aug 16: Data Science Primer: Basic Concepts; Python vs R vs rest**- Aug 16, 2017.

Also: What Artificial Intelligence and Machine Learning Can Do-And What It Can't; How Convolutional Neural Networks Accomplish Image Recognition?; Making Predictive Models Robust: Holdout vs Cross-Validation; The Machine Learning Abstracts: Support Vector Machines**How Convolutional Neural Networks Accomplish Image Recognition?**- Aug 9, 2017.

Image recognition is very interesting and challenging field of study. Here we explain concepts, applications and techniques of image recognition using Convolutional Neural Networks.**Mind Reading: Using Artificial Neural Nets to Predict Viewed Image Categories From EEG Readings**- Aug 9, 2017.

This post outlines the approach taken at a recent deep learning hackathon, hosted by YCombinator-backed startup DeepGram. The dataset: EEG readings from a Stanford research project that predicted which category of images their test subjects were viewing using linear discriminant analysis.**KDnuggets™ News 17:n29, Aug 2: Machine Learning Exercises in Python; 8 Reasons Why Many Big Data Analytics Solutions Fail**- Aug 2, 2017.

Machine Learning Exercises in Python: An Introductory Tutorial Series; The BI & Data Analysis Conundrum: 8 Reasons Why Many Big Data Analytics Solutions Fail to Deliver Value; The Internet of Things: An Introductory Tutorial Series; How to squeeze the most from your training data**Visualizing Convolutional Neural Networks with Open-source Picasso**- Aug 1, 2017.

Toolkits for standard neural network visualizations exist, along with tools for monitoring the training process, but are often tied to the deep learning framework. Could a general, easy-to-setup tool for generating standard visualizations provide a sanity check on the learning process?**Picking an Optimizer for Style Transfer**- Jul 21, 2017.

Gradient Descent, Adam or Limited-memory Broyden–Fletcher–Goldfarb–Shanno? Which will optimize your style transfer neural network faster and better? Read this post for a data-backed discussion.**Top KDnuggets tweets, May 17-23: Beginner Guide To Understanding Convolutional Neural Networks; Big Data 2017: Top Influencers and Brands**- May 24, 2017.

#BigData 2017: Top Influencers and Brands; #ICYMI 10 Free Must-Read Books for #MachineLearning and #DataScience; Good Test for #DeepLearning #ImageRecognition? #Chihuahua or #Muffin**Using Deep Learning To Extract Knowledge From Job Descriptions**- May 9, 2017.

We present a deep learning approach to extract knowledge from a large amount of data from the recruitment space. A learning to rank approach is followed to train a convolutional neural network to generate job title and job description embeddings.**Medical Image Analysis with Deep Learning , Part 2**- Apr 13, 2017.

In this article we will talk about basics of deep learning from the lens of Convolutional Neural Nets. We plan to use this knowledge to build CNNs in the next post and use Keras to develop a model to predict lung cancer.**3 practical thoughts on why deep learning performs so well**- Feb 3, 2017.

Why does Deep Learning perform better than other machine learning methods? We offer 3 reasons: integration of integration of feature extraction within the training process, collection of very large data sets, and technology development.**Top arXiv Papers, January: ConvNets Advances, Wide Instead of Deep, Adversarial Networks Win, Learning to Reinforcement Learn**- Feb 3, 2017.

Check out the top arXiv Papers from January, covering convolutional neural network advances, why wide may trump deep, generative adversarial networks, learning to reinforcement learn, and more.**ResNets, HighwayNets, and DenseNets, Oh My!**- Dec 19, 2016.

This post walks through the logic behind three recent deep learning architectures: ResNet, HighwayNet, and DenseNet. Each make it more possible to successfully trainable deep networks by overcoming the limitations of traditional network design.**KDnuggets™ News 16:n41, Nov 16: Top 10 Amazon Books in Data Mining; Intuitive Explanation of Convolutional Neural Nets**- Nov 16, 2016.

Also An Intuitive Explanation of Convolutional Neural Networks; Data Scientists vs Data Analysts - Part 1; How to Rank 10% in Your First Kaggle Competition.**An Intuitive Explanation of Convolutional Neural Networks**- Nov 11, 2016.

This article provides a easy to understand introduction to what convolutional neural networks are and how they work.**Deep Learning cleans podcast episodes from ‘ahem’ sounds**- Nov 8, 2016.

“3.5 mm audio jack… Ahem!!” where did you hear that? ;) Well, this post is not about Google Pixel vs iPhone 7, but how to remove ugly “Ahem” sound from a speech using deep convolutional neural network. I must say, very interesting read.**Deep Learning Key Terms, Explained**- Oct 12, 2016.

Gain a beginner's perspective on artificial neural networks and deep learning with this set of 14 straight-to-the-point related key concept definitions, including Biological Neuron, Multilayer Perceptron (MLP), Feedforward Neural Network, and Recurrent Neural Network.

**Up to Speed on Deep Learning: August Update, Part 2**- Sep 23, 2016.

This is the second part of an overview of deep learning stories that made news in August. Look to see if you have missed anything.**Deep Learning Reading Group: Deep Residual Learning for Image Recognition**- Sep 22, 2016.

Published in 2015, today's paper offers a new architecture for Convolution Networks, one which has since become a staple in neural network implementation. Read all about it here.**Up to Speed on Deep Learning: August Update**- Sep 21, 2016.

Check out this thorough roundup of deep learning stories that made news in August, and see if there are any items of note that you missed.**KDnuggets™ News 16:n33, Sep 14: Top Algorithms Used by Data Scientists; (Not So) New Data Scientist Venn Diagram**- Sep 14, 2016.

Top Algorithms Used by Data Scientists; Guide To Understanding Convolutional Neural Nets; The (Not So) New Data Scientist Venn Diagram; Deep Learning Networks with Stochastic Depth.**A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2**- Sep 8, 2016.

This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.**Up to Speed on Deep Learning: July Update, Part 2**- Sep 7, 2016.

Check out this second installation of deep learning stories that made news in July. See if there are any items of note you missed.**KDnuggets™ News 16:n32, Sep 7: Cartoon: Data Scientist was sexiest job until…; Up to Speed on Deep Learning**- Sep 7, 2016.

Cartoon: Data Scientist - the sexiest job of the 21st century until...; Up to Speed on Deep Learning: July Update; How Convolutional Neural Networks Work; Learning from Imbalanced Classes; What is the Role of the Activation Function in a Neural Network?**A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1**- Sep 6, 2016.

Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.**How Convolutional Neural Networks Work**- Aug 31, 2016.

Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective.**3 Thoughts on Why Deep Learning Works So Well**- Aug 10, 2016.

While answering a posed question in his recent Quora Session, Yann LeCun also shared 3 high-level thoughts on why deep learning works so well.**Peeking Inside Convolutional Neural Networks**- Jun 29, 2016.

This post discusses using some tricks to peek inside of the neural network, and to visualize what the individual units in a layer detect.**What is the Difference Between Deep Learning and “Regular” Machine Learning?**- Jun 3, 2016.

Another concise explanation of a machine learning concept by Sebastian Raschka. This time, Sebastian explains the difference between Deep Learning and "regular" machine learning.**Machine Learning for Artists – Video lectures and notes**- Apr 28, 2016.

Art has always been deep for those who appreciate it... but now, more than ever, deep learning is making a real impact on the art world. Check out this graduate course, and its freely-available resources, focusing on this very topic.**Must Know Tips for Deep Learning Neural Networks**- Mar 22, 2016.

Deep learning is white hot research topic. Add some solid deep learning neural network tips and tricks from a PhD researcher.**Top /r/MachineLearning Posts, February: AlphaGo, Distributed TensorFlow, Neural Network Image Enhancement**- Mar 2, 2016.

In February on /r/MachineLearning, we get a run-down of the AlphaGo matches, Distributed TensorFlow is released, convolutional neural nets are cleaning Star Wars images, vintage science is on parade, military machine learning is criticized, and the overwhelmed researcher is given advice.**Around the World in 60 Days: Getting Deep Speech to Work in Mandarin**- Feb 24, 2016.

Baidu continues to make impressive gains with deep learning. Their latest achievement centers on Mandarin speech recognition, which you can read about here from the researchers involved in the project.**The Top A.I. Breakthroughs of 2015**- Feb 2, 2016.

Learn about the biggest developments of 2015 in the field of Artificial Intelligence.**Top /r/MachineLearning Posts, January: Google Masters Go, Deep Learning Laughs, OpenAI AMA**- Feb 1, 2016.

In January on /r/MachineLearning: Go gets mastered, deep learning laughs, an OpenAI team AMA, convolutional neural nets colorize black and white photos, and the AI community loses a leader.**20+ hottest research papers on Computer Vision, Machine Learning**- Jan 15, 2016.

December's ICCV 2015 conference in Santiago, Chile has come and gone, but that's no reason not to know about its top papers. Get an update on which computer vision papers and researchers won awards.**7 Steps to Understanding Deep Learning**- Jan 11, 2016.

There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps!**Top 5 Deep Learning Resources, January**- Jan 7, 2016.

There is an increasing volume of deep learning research, articles, blog posts, and news constantly emerging. Our Deep Learning Reading List aims to make this information easier to digest.**Top /r/MachineLearning Posts, November: TensorFlow, Deep Convolutional Generative Adversarial Networks, and lolz**- Dec 2, 2015.

In November on /r/MachineLearning, we've got a good laugh, a fantastic image-generating convolutional generative adversarial network, and a whole lot of Google TensorFlow.**KDnuggets™ News 15:n38, Nov 18: TensorFlow Disappoints; Spark with Python; Deep Learning; Top 20 Books**- Nov 18, 2015.

TensorFlow Disappoints - Google Deep Learning falls shallow; Introduction to Spark with Python; A Statistical View of Deep Learning; Amazon Top 20 Books in Databases & Big Data.**Top KDnuggets tweets, Nov 10-16: 5 Books Every #Data Professional Needs; TensorFlow Disappoints – Google Deep Learning falls shallow**- Nov 17, 2015.

Deep Learning for #Visual Question Answering; 5 Books Every #Data Professional Needs; Deep, excellent overview: A Statistical View of #DeepLearning; TensorFlow Disappoints - Google #DeepLearning falls shallow.**Understanding Convolutional Neural Networks for NLP**- Nov 11, 2015.

Dive into the world of Convolution Neural Networks (CNN), learn how they work, how to apply them for NLP, and how to tune CNN hyperparameters for best performance.**MetaMind Mastermind Richard Socher: Uncut Interview**- Oct 20, 2015.

In a wide-ranging interview, Richard Socher opens up about MetaMind, deep learning, the nature of corporate research, and the future of machine learning.**Deep Learning and Artistic Style – Can art be quantified?**- Sep 17, 2015.

We analyze the latest advance in Deep learning which teaches computers to paint in the style of different famous painters, from Van Gogh to Picasso. Is it really Art?**CuDNN – A new library for Deep Learning**- Sep 19, 2014.

Becoming more and more popular, deep learning is proved to be useful in artificial intelligence. Last week, NVIDIA’s new library for deep neural networks, cuDNN, has attracted much attention.