Search results for Convolutional Neural Network

    Found 90 documents, 10397 searched:

  • An Intuitive Explanation of Convolutional Neural Networks

    By Ujjwal Karn. What are Convolutional Neural Networks and why are they important? Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying…

    https://www.kdnuggets.com/2016/11/intuitive-explanation-convolutional-neural-networks.html

  • Research Guide for Neural Architecture Search

    ...p-turned-reality forms the basis of this guide. We’ll explore a range of research papers that have sought to solve the challenging task of automating neural network design. In this guide, we assume that the reader has been involved in the process of designing neural networks from scratch using one...

    https://www.kdnuggets.com/2019/10/research-guide-neural-architecture-search.html

  • Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras">Silver BlogUnderstanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras

    ...: link Text classification at character level: link References Here’s a list of some references I used to learn about neural nets and convnets: neuralnetworksanddeeplearning.com : By far the best notes on neural networks and deep learning. I highly recommend this website to anyone who want to...

    https://www.kdnuggets.com/2017/11/understanding-deep-convolutional-neural-networks-tensorflow-keras.html

  • 7 Types of Artificial Neural Networks for Natural Language Processing">Silver Blog7 Types of Artificial Neural Networks for Natural Language Processing

    ...ble to learn to perform tasks like classification, prediction, decision-making, visualization, and others just by considering examples. An artificial neural network consists of artificial neurons or processing elements and is organized in three interconnected layers: input, hidden that may include...

    https://www.kdnuggets.com/2017/10/7-types-artificial-neural-networks-natural-language-processing.html

  • Understanding Convolutional Neural Networks for NLP

    When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook’s automated photo tagging to self-driving cars. More recently…

    https://www.kdnuggets.com/2015/11/understanding-convolutional-neural-networks-nlp.html

  • The 8 Neural Network Architectures Machine Learning Researchers Need to Learn">Gold BlogThe 8 Neural Network Architectures Machine Learning Researchers Need to Learn

    ...adings in a nuclear power plant Prediction: Future stock prices or currency exchange rates, Which movies will a person like What are Neural Networks? Neural networks are a class of models within the general machine learning literature. So for example, if you took a Coursera course on machine...

    https://www.kdnuggets.com/2018/02/8-neural-network-architectures-machine-learning-researchers-need-learn.html

  • Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras">Silver BlogConvolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras

    comments Convolutional Neural Networks are a part of what made Deep Learning reach the headlines so often in the last decade. Today we’ll train an image classifier to tell us whether an image contains a dog or a cat, using TensorFlow’s eager API. Artificial Neural Networks have disrupted several...

    https://www.kdnuggets.com/2019/07/convolutional-neural-networks-python-tutorial-tensorflow-keras.html

  • Research Guide for Video Frame Interpolation with Deep Learning

    ...  Video Frame Interpolation via Adaptive Separable Convolution (ICCV, 2017)   In this paper, the authors propose a deep fully convolutional neural network that’s fed with two input frames and estimates pairs of 1D kernels for all pixels. The method is capable of estimating kernels and...

    https://www.kdnuggets.com/2019/10/research-guide-video-frame-interpolation-deep-learning.html

  • The 10 Deep Learning Methods AI Practitioners Need to Apply

    ...e not yet modeled. Neural networks are one type of model for machine learning; they have been around for at least 50 years. The fundamental unit of a neural network is a node, which is loosely based on the biological neuron in the mammalian brain. The connections between neurons are also modeled on...

    https://www.kdnuggets.com/2017/12/10-deep-learning-methods-ai-practitioners-need-apply.html

  • First Steps of Learning Deep Learning: Image Classification in Keras

    ...al is not to teach neural networks by itself, but to provide an overview and to point to didactically useful resources. Don’t be afraid of artificial neural networks – it is easy to start! In fact, my biggest regret is delaying learning it, because of the perceived difficulty. To start, all...

    https://www.kdnuggets.com/2017/08/first-steps-learning-deep-learning-image-classification-keras.html

  • How Convolutional Neural Networks Accomplish Image Recognition?

    …lions of videos and photos uploaded by the users that appear daily on social media. One way to solve this problem would be through the utilization of neural networks. We can make use of conventional neural networks for analyzing images in theory, but in practice, it will be highly expensive from a…

    https://www.kdnuggets.com/2017/08/convolutional-neural-networks-image-recognition.html

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

    ...y of adversarial behavior. For these reasons, it is very desirable to develop methods for developing some understanding of the internal states of the neural networks. Because of the very large number of nodes (or neurons) in the networks, this becomes a problem in data analysis, specifically for...

    https://www.kdnuggets.com/2018/06/topological-data-analysis-convolutional-neural-networks.html

  • 7 Steps to Understanding Deep Learning

    ...earch resurgence, and has been shown to deliver state of the art results in numerous applications. In essence, deep learning is the implementation of neural networks with more than a single hidden layer of neurons. This is, however, a very simplistic view of deep learning, and not one that is...

    https://www.kdnuggets.com/2016/01/seven-steps-deep-learning.html

  • A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1">Gold BlogA Beginner’s Guide To Understanding Convolutional Neural Networks Part 1

    ...1.jpg http://i.stack.imgur.com/vYju7.png https://upload.wikimedia.org/wikipedia/commons/thumb/c/c9/Simple_mouse.svg/479px-Simple_mouse.svg.png http://neuralnetworksanddeeplearning.com/images/tikz44.png http://cs231n.github.io/assets/cnnvis/filt1.jpeg http://i.stack.imgur.com/Nskzd.png...

    https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-1.html

  • 9 Key Deep Learning Papers, Explained">Gold Blog9 Key Deep Learning Papers, Explained

    ...oduction   In this post, we’ll go into summarizing a lot of the new and important developments in the field of computer vision and convolutional neural networks. We’ll look at some of the most important papers that have been published over the last 5 years and discuss why they’re so important....

    https://www.kdnuggets.com/2016/09/9-key-deep-learning-papers-explained.html

  • Knowing Your Neighbours: Machine Learning on Graphs">Gold BlogKnowing Your Neighbours: Machine Learning on Graphs

    ...) model for two papers in Cora. Each paper also has an attribute representing the subject of the paper. Each paper has one of seven subjects, such as Neural Networks, Probabilistic, Theory, etc. The dataset has 2708 papers (nodes) and 5429 citations (edges). Figure 5: The Bag-of-Words (BoW)...

    https://www.kdnuggets.com/2019/08/neighbours-machine-learning-graphs.html

  • 7 Steps to Mastering Deep Learning with Keras">Silver Blog7 Steps to Mastering Deep Learning with Keras

    ...work frameworks, libraries, and APIs available to anyone interested in getting started with deep learning. So… Why Keras? Keras is a high-level neural network API, helping lead the way to the commoditization of deep learning and artificial intelligence. It runs on top of a number of...

    https://www.kdnuggets.com/2017/10/seven-steps-deep-learning-keras.html

  • Peeking Inside Convolutional Neural Networks

    By Audun M. Øygard, Schibsted Media Group. Convolutional neural networks are used extensively for a number of image related tasks these days. Despite being very successful, they’re mostly seen as “black box” models, since it’s hard to understand what happens inside the...

    https://www.kdnuggets.com/2016/06/peeking-inside-convolutional-neural-networks.html

  • Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step

    comments In image analysis, convolutional neural networks (CNNs or ConvNets for short) are time and memory efficient than fully connected (FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term...

    https://www.kdnuggets.com/2018/04/derivation-convolutional-neural-network-fully-connected-step-by-step.html

  • ResNets, HighwayNets, and DenseNets, Oh My!

    ...asn’t just consisted of greater depth for depths sake. For many applications, the most prominent of which being object classification, the deeper the neural network, the better the performance. That is, provided they can be properly trained! In this post I would like to walk through the logic...

    https://www.kdnuggets.com/2016/12/resnets-highwaynets-densenets-oh-my.html

  • Solve any Image Classification Problem Quickly and Easily

    ...le to solve any image classification problem quickly and easily. The article has been organised in the following way: Transfer learning Convolutional neural networks Repurposing a pre-trained model Transfer learning process Classifiers on top of deep convolutional neural networks Example Summary...

    https://www.kdnuggets.com/2018/12/solve-image-classification-problem-quickly-easily.html

  • A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2

    ...that gets 100% or 99% on the training set, but only 50% on the test data. Dropout Layers   Now, dropout layers have a very specific function in neural networks. In the last section, we discussed the problem of overfitting, where after training, the weights of the network are so tuned to the...

    https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-2.html

  • A 2019 Guide to Speech Synthesis with Deep Learning

    ...eNet on Mel Spectrogram Predictions   WaveNet: A Generative Model for Raw Audio   The authors of this paper are from Google. They present a neural network for generating raw audio waves. Their model is fully probabilistic and autoregressive, and it generates state-of-the-art...

    https://www.kdnuggets.com/2019/09/2019-guide-speech-synthesis-deep-learning.html

  • Inside Deep Learning: Computer Vision With Convolutional Neural Networks

    ...ning more complex features out of raw pixel values, we’re going to use a special kind of neural network called a convolutional network. Convolutional neural networks, popularized by Yann LeCun and others in 1998 with LeNet are behind many of the successes of Deep Learning that have been reported...

    https://www.kdnuggets.com/2015/04/inside-deep-learning-computer-vision-convolutional-neural-networks.html

  • 20+ hottest research papers on Computer Vision, Machine Learning

    ...(i.e., is this a dog or a table) directly from images. The world of computer vision is changing fast has changed. We now have powerful convolutional neural networks that are able to extract just about anything directly from images. So if your input is an image (or set of images), then...

    https://www.kdnuggets.com/2016/01/iccv-2015-21-hottest-papers.html

  • Research Guide for Depth Estimation with Deep Learning

    ...hoto editing tools.   Unsupervised Monocular Depth Estimation with Left-Right Consistency (CVPR 2017)   This paper proposes a convolutional neural network that’s trained to perform single image depth estimation without ground-truth depth data. The authors propose a network architecture...

    https://www.kdnuggets.com/2019/11/research-guide-depth-estimation-deep-learning.html

  • Inside the Mind of a Neural Network with Interactive Code in Tensorflow

    ...nbsp; Bio: Jae Duk Seo is a fourth year computer scientist at Ryerson University. Original. Reposted with permission. Related: Building Convolutional Neural Network using NumPy from Scratch How I Used CNNs and Tensorflow and Lost a Silver Medal in Kaggle Challenge Using Tensorflow Object Detection...

    https://www.kdnuggets.com/2018/06/inside-mind-neural-network-interactive-code-tensorflow.html

  • A 2019 Guide to Object Detection

    ...mentation Strategies for Object Detection   R-CNN Model   This technique combines two main approaches: applying high-capacity convolutional neural networks to bottom-up region proposals so as to localize and segment objects; and supervised pre-training for auxiliary tasks. Rich feature...

    https://www.kdnuggets.com/2019/08/2019-guide-object-detection.html

  • Visualizing Convolutional Neural Networks with Open-source Picasso

    ...intended classification task. To understand what we mean, consider this (possibly apocryphal) story [1]: Once upon a time, the US Army wanted to use neural networks to automatically detect camouflaged enemy tanks. The researchers trained a neural net on 50 photos of camouflaged tanks in trees, and...

    https://www.kdnuggets.com/2017/08/visualizing-convolutional-neural-networks-open-source-picasso.html

  • A 2019 Guide for Automatic Speech Recognition

    ...n, they use the clipped rectified linear (ReLU) function. At its core, this architecture is similar to Deep Speech 1. The architecture is a recurrent neural network trained to ingest speech spectrograms and output text transcriptions. The model is trained using the CTC loss function. source  ...

    https://www.kdnuggets.com/2019/09/2019-guide-automatic-speech-recognition.html

  • How Convolutional Neural Networks Work

    pdf [2MB] ppt [6MB] Nine times out of ten, when you hear about deep learning breaking a new technological barrier, Convolutional Neural Networks are involved. Also called CNNs or ConvNets, these are the workhorse of the deep neural network field. They have learned to sort images into categories...

    https://www.kdnuggets.com/2016/08/brohrer-convolutional-neural-networks-explanation.html

  • Top 20 Deep Learning Papers, 2018 Edition">Gold BlogTop 20 Deep Learning Papers, 2018 Edition

    ...numbers when this article was published. In this list of papers more than 75% refer to deep learning and neural networks, specifically Convolutional Neural Networks (CNN). Almost 50% of them refer to pattern recognition applications in the field of computer vision. I believe tools like TensorFlow,...

    https://www.kdnuggets.com/2018/03/top-20-deep-learning-papers-2018.html

  • A 2019 Guide to Human Pose Estimation

    ...son Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model   DeepPose: Human Pose Estimation via Deep Neural Networks (CVPR, 2014)   This paper proposes using deep neural networks(DNNs) to tackle this ML task. The authors of this paper are...

    https://www.kdnuggets.com/2019/08/2019-guide-human-pose-estimation.html

  • DeepMind Relational Reasoning Networks  Demystified">Silver Blog, Aug 2017DeepMind Relational Reasoning Networks  Demystified

    …ead phrases that are often misleading. For example, its new paper on relational reasoning networks has futurism reporting it like DeepMind Develops a Neural Network That Can Make Sense of Objects Around It. This is not only misleading, but it also makes the everyday non PhD person intimidated. In…

    https://www.kdnuggets.com/2017/08/deepmind-relational-reasoning-networksdemystified.html

  • Predict Age and Gender Using Convolutional Neural Network and OpenCV">Silver BlogPredict Age and Gender Using Convolutional Neural Network and OpenCV

    ...o the tremendous leaps in performance recently reported for the related task of face recognition. — Age and Gender Classification using Convolutional Neural Networks   Introduction   Age and gender, two of the key facial attributes, play a very foundational role in social interactions,...

    https://www.kdnuggets.com/2019/04/predict-age-gender-using-convolutional-neural-network-opencv.html

  • Top /r/MachineLearning Posts, June: Neural Network Generated Images, Free Data Science Books, Super Mario World

    ...Research’s neural nets, 16 great free books on data science, a machine learning system that can play Super Mario World, a tutorial to implement neural networks in Python, and a video stream that visualizes terms provided by the audience made it to the top. 1. Image generated by a...

    https://www.kdnuggets.com/2015/07/top-machine-learning-posts-june.html

  • Deep Learning for NLP: An Overview of Recent Trends">Silver BlogDeep Learning for NLP: An Overview of Recent Trends

    ...applying deep learning in NLP. Some topics include: The rise of distributed representations (e.g., word2vec) Convolutional, recurrent, and recursive neural networks Applications in reinforcement learning Recent development in unsupervised sentence representation learning Combining deep learning...

    https://www.kdnuggets.com/2018/09/deep-learning-nlp-overview-recent-trends.html

  • Don’t Use Dropout in Convolutional Networks

    ...lutional architectures. Dropout If you are reading this, I assume that you have some understanding of what dropout is, and its roll in regularizing a neural network. If you want a refresher, read this post by Amar Budhiraja. Srivastava, Nitish, et al. ”Dropout: a simple way to prevent neural...

    https://www.kdnuggets.com/2018/09/dropout-convolutional-networks.html

  • How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1">Gold BlogHow to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1

    ...thresholding and Non-maximum suppression Part 5 : Designing the input and the output pipelines Prerequisites You should understand how convolutional neural networks work. This also includes knowledge of Residual Blocks, skip connections, and Upsampling. What is object detection, bounding box...

    https://www.kdnuggets.com/2018/05/implement-yolo-v3-object-detector-pytorch-part-1.html

  • A 2019 Guide to Semantic Segmentation

    ...ntic Image Segmentation (ICCV, 2015)   This paper proposes a solution to the challenge of dealing with weakly-labeled data in deep convolutional neural networks (CNNs), as well as a combination of data that’s well-labeled and data that’s not properly labeled. In the paper, a combination of...

    https://www.kdnuggets.com/2019/08/2019-guide-semantic-segmentation.html

  • Deep Learning for Object Detection: A Comprehensive Review">Silver BlogDeep Learning for Object Detection: A Comprehensive Review

    ...nto its ancestry. R-CNN R-CNN is the grand-daddy of Faster R-CNN. In other words, R-CNN reallykicked things off. R-CNN, or Region-based Convolutional Neural Network, consisted of 3 simple steps: Scan the input image for possible objects using an algorithm called Selective Search, generating ~2000...

    https://www.kdnuggets.com/2017/10/deep-learning-object-detection-comprehensive-review.html

  • Overview and benchmark of traditional and deep learning models in text classification

    ...ut these: Logistic regression with word ngrams Logistic regression with character ngrams Logistic regression with word and character ngrams Recurrent neural network (bidirectional GRU) without pre-trained embeddings Recurrent neural network (bidirectional GRU) with GloVe pre-trained embeddings...

    https://www.kdnuggets.com/2018/07/overview-benchmark-deep-learning-models-text-classification.html

  • Building Convolutional Neural Network using NumPy from Scratch">Silver BlogBuilding Convolutional Neural Network using NumPy from Scratch

    ...ter control and understanding, you should try to implement them yourself. This article shows how a CNN is implemented just using NumPy. Convolutional neural network (CNN) is the state-of-art technique for analyzing multidimensional signals such as images. There are different libraries that already...

    https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html

  • Using AI to Super Compress Images

    networks have taken the world by storm. They recent surge is due to several factors, including cheap and powerful hardware, and vast amounts of data. Neural Networks are currently the state of the art when it comes to ‘cognitive’ tasks like image recognition, natural language understanding , etc….

    https://www.kdnuggets.com/2017/08/ai-compress-images.html

  • Medical Image Analysis with Deep Learning , Part 2

    ...image we have input with 3 dimensions — length, width and depth (which are the color channels) Source: http://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/ Filters or Kernels : As shown in the figure from RiverTrail below, a filter or kernel slides to every...

    https://www.kdnuggets.com/2017/04/medical-image-analysis-deep-learning-part-2.html

  • Top 5 Deep Learning Resources, January

    ...rom training examples, some results of which are shown in the above image. Says hardmaru: In this blog post, I will describe how to train a recurrent neural network to generate fake, but plausible Chinese characters, in vector .svg format. I created a tool called sketch-rnn that would attempt to...

    https://www.kdnuggets.com/2016/01/deep-learning-reading-list-january.html

  • Top arXiv Papers, January: ConvNets Advances, Wide Instead of Deep, Adversarial Networks Win, Learning to Reinforcement Learn

    ...or each paper, you will also find some modest commentary, links, perhaps an image, and an excerpt from the abstract. Recent Advances in Convolutional Neural Networks   Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang...

    https://www.kdnuggets.com/2017/02/top-arxiv-papers-january-convnets-wide-adversarial.html

  • What is the Difference Between Deep Learning and “Regular” Machine Learning?">2016 Silver BlogWhat is the Difference Between Deep Learning and “Regular” Machine Learning?

    ...eral way. The tl;dr version of this is: Deep learning is essentially a set of techniques that help we to parameterize deep neural network structures, neural networks with many, many layers and parameters. And if we are interested, a more concrete example: Let’s start with multi-layer...

    https://www.kdnuggets.com/2016/06/difference-between-deep-learning-regular-machine-learning.html

  • TensorFlow for Short-Term Stocks Prediction

    comments By Mattia Brusamento Summary In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of neural networks that has successfully been applied to image recognition and analysis. In this project I’ve approached this class of models trying to apply it to stock...

    https://www.kdnuggets.com/2017/12/tensorflow-short-term-stocks-prediction.html

  • Ten Machine Learning Algorithms You Should Know to Become a Data Scientist">Silver BlogTen Machine Learning Algorithms You Should Know to Become a Data Scientist

    ...n use as it lets me check both LR and SVM with a common interface. You can also train it on >RAM sized datasets using mini batches. 6. Feedforward Neural Networks These are basically multilayered Logistic Regression classifiers. Many layers of weights separated by non-linearities (sigmoid, tanh,...

    https://www.kdnuggets.com/2018/04/10-machine-learning-algorithms-data-scientist.html

  • [Webinar] Neural Network Fundamentals

    ...nces between popular Deep Learning frameworks like Keras or TensorFlow, and where should you start? In this webinar, we will apply your convolutional neural network using the ImageNet scenario. We will also review some of the ImageNet architectures and how convolutions work. After this session,...

    https://www.kdnuggets.com/2018/10/databricks-webinar-neural-network-fundamentals.html

  • A “Weird” Introduction to Deep Learning">Silver BlogA “Weird” Introduction to Deep Learning

    ...just created this timeline based on several papers and other timelines with the purpose of everyone seeing that Deep Learning is much more than just Neural Networks. There has been really theoretical advances, software and hardware improvements that were necessary for us to get to this day. If you...

    https://www.kdnuggets.com/2018/03/weird-introduction-deep-learning.html

  • Mind Reading: Using Artificial Neural Nets to Predict Viewed Image Categories From EEG Readings

    ...of images their test subjects were viewing using linear discriminant analysis. Winning Kaggle competition teams have successfully applied artificial neural networks on EEG data (see first place winner of the grasp-and-lift challenge and third place winner of seizure prediction competition). Could...

    https://www.kdnuggets.com/2017/08/mind-reading-using-artificial-neural-nets.html

  • An Intuitive Guide to Deep Network Architectures

    …a streamlined version of the Xception architecture optimized for mobile applications. The remaining three, however, truly redefine the way we look at neural networks. This rest of this post will focus on the intuition behind the ResNet, Inception, and Xception architectures, and why they have…

    https://www.kdnuggets.com/2017/08/intuitive-guide-deep-network-architectures.html

  • Deep Learning cleans podcast episodes from ‘ahem’ sounds

    …sounds on the podcast Data Science at Home? Because we remove them. Actually not us. A neural network does. The ahem detector is a deep convolutional neural network trained on transformed audio signals to recognize ahem sounds. The network has been trained to detect such signals on the episodes of…

    https://www.kdnuggets.com/2016/11/deep-learning-cleans-podcast-ahem-sounds.html

  • 50 Deep Learning Software Tools and Platforms, Updated

    ...els of natural images (from Marc’Aurelio Ranzato). Nengo, a graphical and scripting based software package for simulating large-scale neural systems. neuralnetworks, a Java based GPU library for deep learning algorithms. NVIDIA DIGITS is a new system for developing, training and visualizing deep...

    https://www.kdnuggets.com/2015/12/deep-learning-tools.html

  • In Deep Learning, Architecture Engineering is the New Feature Engineering

    ...time a human being forces an architectural decision that couldn’t be learned, we’re essentially hard coding a feature. The Convolutional Neural Network (CNN)   A major reason for the resurgence in popularity of neural networks were their impressive results from the ImageNet...

    https://www.kdnuggets.com/2016/07/deep-learning-architecture-engineering-feature-engineering.html

  • Must Know Tips for Deep Learning Neural Networks

    By Xiu-Shen Wei, Nanjing University. Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the...

    https://www.kdnuggets.com/2016/03/must-know-tips-deep-learning-part-1.html

  • Convolutional Neural Network for Breast Cancer Classification

    ...ems. In this blog, I have demonstrated how to classify benign and malignant breast cancer from a collection of microscopic images using convolutional neural networks and transfer learning.   References/Further Readings   Transfer Learning for Image Classification in Keras One stop guide...

    https://www.kdnuggets.com/2019/10/convolutional-neural-network-breast-cancer-classification.html

  • Real World Deep Learning: Neural Networks for Smart Crops

    ...d information, which comes at a high cost. The advances in image classification, object detection, and semantic segmentation using deep Convolutional Neural Networks, which spawned the availability of open source tools such as Caffe and TensorFlow (to name a couple) to easily manipulate neural...

    https://www.kdnuggets.com/2017/11/real-world-deep-learning-neural-networks-smart-crops.html

  • Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API">Silver BlogComplete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API

    comments This tutorial takes you along the steps required to create a convolutional neural network (CNN/ConvNet) using TensorFlow and get it into production by allowing remote access via a HTTP-based application using Flask RESTful API. In this tutorial, a CNN is to be built using TensorFlow NN...

    https://www.kdnuggets.com/2018/05/complete-guide-convnet-tensorflow-flask-restful-python-api.html

  • Predicting Popularity of Online Content

    ...by looking into the visual features such as the colors present in an image, edge and gradient distributions as well as outputs of deep convolutional neural networks – machine learning algorithms proved successful in image classification tasks such as ImageNet Challenge. The results show...

    https://www.kdnuggets.com/2016/05/predicting-popularity-online-content.html

  • Capsule Networks Are Shaking up AI  – Here’s How to Use Them

    ...orks. I will show you how you can start using them today. Geoffrey Hinton is known as the father of “deep learning.” Back in the 50s the idea of deep neural networks began to surface and, in theory, could solve a vast amount of problems. However, nobody was able to figure out how to train them and...

    https://www.kdnuggets.com/2017/11/capsule-networks-shaking-up-ai.html

  • Top /r/MachineLearning Posts, February: AlphaGo, Distributed TensorFlow, Neural Network Image Enhancement

    ...wakens fame), an image which shows a fuzzy, unclear original image side-by-side with a version of the image which has been enhanced via convolutional neural network. This paper from November, 2015, is responsible for the enhancement process used, with the paper’s abstract shown below: We...

    https://www.kdnuggets.com/2016/03/top-reddit-machine-learning-februrary.html

  • Deep Learning in Neural Networks: An Overview

    ...problem (Minsky, 1963)…. The present survey will focus on the narrower, but now commercially important, subfield of Deep Learning (DL) in Artificial Neural Networks (NNs)… Learning or credit assignment is about finding weights that make the NN exhibit desired behaviour – such as driving a car....

    https://www.kdnuggets.com/2016/04/deep-learning-neural-networks-overview.html

  • Semantic Segmentation Models for Autonomous Vehicles

    ...tails the memory requirements of various models. This has encouraged researchers to explore novel designs to achieve output rates of >10fps from a neural network and contain fewer parameters. Table 1: Semantic Segmentation models for autonomous vehicles Architecture Params Output mIoU (%)...

    https://www.kdnuggets.com/2018/03/semantic-segmentation-models-autonomous-vehicles.html

  • Deep Learning Finds What Makes a Good #selfie

    ...ore or less), and provides us with his personal recollection of the astonishment surrounding the ImageNet 2012 results. Under the hood, convolutional neural networks represent images with pixel-by-pixel arrays, and cover these image representations with convolutions, or filters. These filters then...

    https://www.kdnuggets.com/2015/10/karpathy-deep-learning-good-selfie.html

  • Text Classification & Embeddings Visualization Using LSTMs, CNNs, and Pre-trained Word Vectors

    ...ification Using LSTM and visualize Word Embeddings In this part, I build a neural network with LSTM and word embeddings were leaned while fitting the neural network on the classification problem. The network starts with an embedding layer. The layer lets the system expand each token to a more...

    https://www.kdnuggets.com/2018/07/text-classification-lstm-cnn-pre-trained-word-vectors.html

  • Deep Learning and Artistic Style – Can art be quantified?

    ...” discussed here presents yet another interesting application of deep learning and a variation on the theme of computer vision. A convolutional neural network is employed to separate the style and content of different images and recombine these disparate sources into one cohesive piece....

    https://www.kdnuggets.com/2015/09/deep-learning-art-style.html

  • Deep Residual Networks for Image Classification with Python + NumPy

    ...-only) deep learning framework along with the residual model, and trained it on CIFAR-10, MNIST and SFDDD. Results speak by themselves. Convolutional Neural Networks for Computer Vision   On Monday, June 13rd, I graduated with a master’s degree in computer engineering, presenting a thesis on...

    https://www.kdnuggets.com/2016/07/deep-residual-neworks-image-classification-python-numpy.html

  • Top /r/MachineLearning Posts, November: TensorFlow, Deep Convolutional Generative Adversarial Networks, and lolz

    ...rials for TensorFlow. The tutorials are based on these Theano tutorials. Topics covered include simple multiplication, Linear regression, feedforward neural networks, and convolutional neural networks, among others. 4. Jeff Dean Explains TensorFlow +179 Here we have a video of Googler Jeff Dean...

    https://www.kdnuggets.com/2015/12/top-reddit-machine-learning-november.html

  • Detecting Facial Features Using Deep Learning

    ...u can see the overall architecture here: Well, fairly complicated, right?   Deep Learning   Instead, we can use a very simple convolutional neural network (CNN) and perform detection of key-points on parts of images we expect to contain faces. For that we need to have a training dataset...

    https://www.kdnuggets.com/2017/09/detecting-facial-features-deep-learning.html

  • Object Detection: An Overview in the Age of Deep Learning

    ...deep learning for object detection was OverFeat from NYU published in 2013. They proposed a multi-scale sliding window algorithm using Convolutional Neural Networks (CNNs).   R-CNN   Quickly after OverFeat, Regions with CNN features or R-CNN from Ross Girshick, et al. at the UC Berkeley...

    https://www.kdnuggets.com/2017/09/object-detection-overview-deep-learning.html

  • Up to Speed on Deep Learning: July Update, Part 2

    ...se efforts. Neural Networks for Machine Learning course by Geoffrey Hintonwill be offered again in September 2016 on Coursera. Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language...

    https://www.kdnuggets.com/2016/09/deep-learning-july-update-part-2.html

  • Up to Speed on Deep Learning: August Update

    ...so let us know if there’s something we should add, or if you’re interested in discussing this area further. An Intuitive Explanation of Convolutional Neural Networks by Ujjwal Karn. An thorough overview of CNNs: what they do, why they’re important, how they work, some history, and their underlying...

    https://www.kdnuggets.com/2016/09/deep-learning-august-update.html

  • KDnuggets™ News 16:n32, Sep 7: Cartoon: Data Scientist was sexiest job until…; Up to Speed on Deep Learning

    ...nbsp;  Top Tweets Top KDnuggets tweets, Aug 24-30: #DataScientist – sexiest job of the 21st century until …; Activation Function in #NeuralNetworks. Top KDnuggets tweets, Aug 17-23: Approaching (Almost) Any #MachineLearning Problem; #Database Nirvana – can one query language...

    https://www.kdnuggets.com/2016/n32.html

  • Top Stories, Aug 29-Sep 4: How Convolutional Neural Networks Work; Activation Functions in Neural Networks

    ...rview Questions and Answers How to Become a (Type A) Data Scientist Up to Speed on Deep Learning: July Update Most Shared Last Week How Convolutional Neural Networks Work, by Brandon Rohrer – Aug 31, 2016. What is the Role of the Activation Function in a Neural Network?, By Sebastian Raschka...

    https://www.kdnuggets.com/2016/09/top-news-week-0829-0904.html

  • Fixing a Major Weakness in Machine Learning of Images with Hinton’s Capsule Networks

    ...iew of the other. This sort of cognitive transformation is effortlessly intuitive for a human, but turns out to be very difficult for a convolutional neural network without explicit training examples. Limitations of Convolutional and Max Pooling Layers Standard convolutional neural networks are...

    https://www.kdnuggets.com/2019/05/machine-learning-images-hinton-capsule-networks.html

  • KDnuggets Top Blogger: An Interview with Adit Deshpande, Deep Learning Aficionado

    ...s, all of which have been very informative and well received. Also, they have all been related to neural networks. What has attracted you to studying neural networks? Well, I think what most attracted me to this field was just the incredible progress and innovation that has been taking place. From...

    https://www.kdnuggets.com/2016/10/top-blogger-interview-adit-deshpande.html

  • Deep Compression: Optimization Techniques for Inference & Efficiency

    ...ration of top models runs the risk of rapid obsolescence. Deep Compression: Biologically Inspired Efficiency Improvements Deep learning convolutional neural networks, as their name suggests, are known for their depth and width, with some exceptional examples of resnets having about 1000 layers. At...

    https://www.kdnuggets.com/2019/03/deep-compression-optimization-techniques-inference-efficiency.html

  • Using Deep Learning To Extract Knowledge From Job Descriptions">Gold Blog, May 2017Using Deep Learning To Extract Knowledge From Job Descriptions

    ...escriptions in words and all words were transformed to lowercase. No feature engineering was done. Our model architecture consists of a convolutional neural network (CNN) that generates an embedding for a job description and a lookup table with job title embeddings: The cosine similarity between...

    https://www.kdnuggets.com/2017/05/deep-learning-extract-knowledge-job-descriptions.html

  • Top Stories, Apr 23-29: Blockchain Explained in 7 Python Functions; Building Convolutional Neural Network using NumPy from Scratch

    ...Libraries and Platforms Most Shared Last Week Blockchain Explained in 7 Python Functions, by Tom Cusack – Apr 27, 2018. Building Convolutional Neural Network using NumPy from Scratch, by Ahmed Gad – Apr 26, 2018. Choosing the Right Metric for Evaluating Machine Learning Models –...

    https://www.kdnuggets.com/2018/04/top-news-week-0423-0429.html

  • Generative Adversarial Networks — Part II

    ...control the output systematically and reliably without having to randomly walk the space. This problem and solution will be explored in Part III. Original. Reposted with permission. Related: Can (Creative Adversarial Network)-Explained Real World Deep Learning: Neural Networks for Smart Crops Top...

    https://www.kdnuggets.com/2017/11/generative-adversarial-networks-part2.html

  • KDnuggets Exclusive: Interview with Yann LeCun, Deep Learning Expert, Director of Facebook AI Lab

    ...rception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits and architectures for computer perception. The...

    https://www.kdnuggets.com/2014/02/exclusive-yann-lecun-deep-learning-facebook-ai-lab.html

  • Top 20 Recent Research Papers on Machine Learning and Deep Learning">Silver Blog, 2017Top 20 Recent Research Papers on Machine Learning and Deep Learning

    ...Note that the second paper is only published last year. Read (or re-read them) and learn about the latest advances. Dropout: a simple way to prevent neural networks from overfitting, by Hinton, G.E., Krizhevsky, A., Srivastava, N., Sutskever, I., & Salakhutdinov, R. (2014). Journal of Machine...

    https://www.kdnuggets.com/2017/04/top-20-papers-machine-learning.html

  • Top /r/MachineLearning Posts, January: Google Masters Go, Deep Learning Laughs, OpenAI AMA

    ...lorizing Black and White Photos with Deep Learning +346 This is a great post summarizing and showing examples from a project which used convolutional neural networks to colorize black and white photos. The description of the network architecture used is very good, as is the explanation of the...

    https://www.kdnuggets.com/2016/02/top-reddit-machine-learning-january.html

  • DeepSense: A unified deep learning framework for time-series mobile sensing data processing

    ...ut actually we’re going to process slice by slice in the T dimension (one window at a time). Each d x 2f window slice is passed through a convolution neural network component comprising three stages as illustrated below: First we use 2D convolutional filters to capture interactions among...

    https://www.kdnuggets.com/2017/08/deepsense-unified-deep-learning-framework-time-series-mobile.html

  • Free resources to learn Natural Language Processing

    ...og posts anyone who hasn’t read them: http://colah.github.io/posts/2015-08-Understanding-LSTMs https://distill.pub/2016/augmented-rnns/ Convolutional Neural Networks (Convnets) can be used to make sense of Natural Language. You can visualize how Convnets work in NLP by reading this paper here. How...

    https://www.kdnuggets.com/2018/09/free-resources-natural-language-processing.html

  • Deep Learning – important resources for learning and understanding

    ...5%) and the error rate fell to only 6.6% from 11.7%. Significantly, almost all of the entrants used a variant of an approach known as a convolutional neural network (ConvNet), an approach first refined in 1998 by Yann LeCun, NYU professor, recently hired to head Facebook AI Research Center. Here...

    https://www.kdnuggets.com/2014/08/deep-learning-important-resources-learning-understanding.html

  • The Value of Semi-Supervised Machine Learning">Gold BlogThe Value of Semi-Supervised Machine Learning

    ...t we can take advantage of. Thinking Deeper About Your Data We know that our data are images and since 2012, the hammer for images is a convolutional neural network (CNN). There are a couple of ways we could use a CNN, from a pre-trained network or as a simple model to pre-process the images. For...

    https://www.kdnuggets.com/2018/01/value-semi-supervised-machine-learning.html

Refine your search here:

Sign Up

By subscribing you accept KDnuggets Privacy Policy