Search results for activation function
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How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1">How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1
The best way to go about learning object detection is to implement the algorithms by yourself, from scratch. This is exactly what we'll do in this tutorial.https://www.kdnuggets.com/2018/05/implement-yolo-v3-object-detector-pytorch-part-1.html
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Semantic Segmentation Models for Autonomous Vehicles
State-of-the-art Semantic Segmentation models need to be tuned for efficient memory consumption and fps output to be used in time-sensitive domains like autonomous vehicles.https://www.kdnuggets.com/2018/03/semantic-segmentation-models-autonomous-vehicles.html
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Understanding Learning Rates and How It Improves Performance in Deep Learning
Furthermore, the learning rate affects how quickly our model can converge to a local minima (aka arrive at the best accuracy). Thus getting it right from the get go would mean lesser time for us to train the model.https://www.kdnuggets.com/2018/02/understanding-learning-rates-improves-performance-deep-learning.html
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Deep Learning in H2O using R
This article is about implementing Deep Learning (DL) using the H2O package in R. We start with a background on DL, followed by some features of H2O's DL framework, followed by an implementation using R.https://www.kdnuggets.com/2018/01/deep-learning-h2o-using-r.html
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Using Genetic Algorithm for Optimizing Recurrent Neural Networks
In this tutorial, we will see how to apply a Genetic Algorithm (GA) for finding an optimal window size and a number of units in Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN).https://www.kdnuggets.com/2018/01/genetic-algorithm-optimizing-recurrent-neural-network.html
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The 10 Deep Learning Methods AI Practitioners Need to Apply
Deep learning emerged from that decade’s explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.https://www.kdnuggets.com/2017/12/10-deep-learning-methods-ai-practitioners-need-apply.html
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TensorFlow for Short-Term Stocks Prediction
In this post you will see an application of Convolutional Neural Networks to stock market prediction, using a combination of stock prices with sentiment analysis.https://www.kdnuggets.com/2017/12/tensorflow-short-term-stocks-prediction.html
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Today I Built a Neural Network During My Lunch Break with Keras
So yesterday someone told me you can build a (deep) neural network in 15 minutes in Keras. Of course, I didn’t believe that at all. So the next day I set out to play with Keras on my own data.https://www.kdnuggets.com/2017/12/today-built-neural-network-during-lunch-break-keras.html
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Interpreting Machine Learning Models: An Overview">Interpreting Machine Learning Models: An Overview
This post summarizes the contents of a recent O'Reilly article outlining a number of methods for interpreting machine learning models, beyond the usual go-to measures.https://www.kdnuggets.com/2017/11/interpreting-machine-learning-models-overview.html
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How I started with learning AI in the last 2 months">How I started with learning AI in the last 2 months
The relevance of a full stack developer will not be enough in the changing scenario of things. In the next two years, full stack will not be full stack without AI skills.https://www.kdnuggets.com/2017/10/how-started-learning-ai.html
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Detecting Facial Features Using Deep Learning
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.https://www.kdnuggets.com/2017/09/detecting-facial-features-deep-learning.html
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An Intuitive Guide to Deep Network Architectures
How and why do different Deep Learning models work? We provide an intuitive explanation for 3 very popular DL models: Resnet, Inception, and Xception.https://www.kdnuggets.com/2017/08/intuitive-guide-deep-network-architectures.html
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37 Reasons why your Neural Network is not working">37 Reasons why your Neural Network is not working
Over the course of many debugging sessions, I’ve compiled my experience along with the best ideas around in this handy list. I hope they would be useful to you.https://www.kdnuggets.com/2017/08/37-reasons-neural-network-not-working.html
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How Convolutional Neural Networks Accomplish Image Recognition?
Image recognition is very interesting and challenging field of study. Here we explain concepts, applications and techniques of image recognition using Convolutional Neural Networks.https://www.kdnuggets.com/2017/08/convolutional-neural-networks-image-recognition.html
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Going deeper with recurrent networks: Sequence to Bag of Words Model
Deep learning makes it possible to convert unstructured text to computable formats, incorporating semantic knowledge to train machine learning models. These digital data troves help us understand people on a new level.https://www.kdnuggets.com/2017/08/deeper-recurrent-networks-sequence-bag-words-model.html
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Understanding Deep Learning Requires Re-thinking Generalization">Understanding Deep Learning Requires Re-thinking Generalization
What is it that distinguishes neural networks that generalize well from those that don’t? A satisfying answer to this question would not only help to make neural networks more interpretable, but it might also lead to more principled and reliable model architecture design.https://www.kdnuggets.com/2017/06/understanding-deep-learning-rethinking-generalization.html
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Using Deep Learning To Extract Knowledge From Job Descriptions">Using Deep Learning To Extract Knowledge From Job Descriptions
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.https://www.kdnuggets.com/2017/05/deep-learning-extract-knowledge-job-descriptions.html
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Top 20 Recent Research Papers on Machine Learning and Deep Learning">Top 20 Recent Research Papers on Machine Learning and Deep Learning
Machine learning and Deep Learning research advances are transforming our technology. Here are the 20 most important (most-cited) scientific papers that have been published since 2014, starting with "Dropout: a simple way to prevent neural networks from overfitting".https://www.kdnuggets.com/2017/04/top-20-papers-machine-learning.html
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Neuroscience for Data Scientists: Understanding Human Behaviour
Neuroscience is very complex and advanced study of brain and people often misuse this term. Here we try to explain neuroscience terminologies and use of data science for such studies.https://www.kdnuggets.com/2017/03/neuroscience-data-science-human-behaviour.html
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ResNets, HighwayNets, and DenseNets, Oh My!
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.https://www.kdnuggets.com/2016/12/resnets-highwaynets-densenets-oh-my.html
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A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2
This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-2.html
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A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1">A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1
Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-1.html
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Multi-Task Learning in Tensorflow: Part 1
A discussion and step-by-step tutorial on how to use Tensorflow graphs for multi-task learning.https://www.kdnuggets.com/2016/07/multi-task-learning-tensorflow-part-1.html
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MNIST Generative Adversarial Model in Keras
This post discusses and demonstrates the implementation of a generative adversarial network in Keras, using the MNIST dataset.https://www.kdnuggets.com/2016/07/mnist-generative-adversarial-model-keras.html
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What is Softmax Regression and How is it Related to Logistic Regression?
An informative exploration of softmax regression and its relationship with logistic regression, and situations in which each would be applicable.https://www.kdnuggets.com/2016/07/softmax-regression-related-logistic-regression.html
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Are Deep Neural Networks Creative?
Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?https://www.kdnuggets.com/2016/05/deep-neural-networks-creative-deep-learning-art.html
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Deep Learning for Visual Question Answering
Here we discuss about the Visual Question Answering problem, and I’ll also present neural network based approaches for same.https://www.kdnuggets.com/2015/11/deep-learning-visual-question-answering.html
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Does Deep Learning Have Deep Flaws?
A recent study of neural networks found that for every correctly classified image, one can generate an "adversarial", visually indistinguishable image that will be misclassified. This suggests potential deep flaws in all neural networks, including possibly a human brain.https://www.kdnuggets.com/2014/06/deep-learning-deep-flaws.html