Deep learning doesn’t need to be a black box - Feb 5, 2021.
The cultural perception of AI is often suspect because of the described challenges in knowing why a deep neural network makes its predictions. So, researchers try to crack open this "black box" after a network is trained to correlate results with inputs. But, what if the goal of explainability could be designed into the network's architecture -- before the model is trained and without reducing its predictive power? Maybe the box could stay open from the beginning.
Convolutional Neural Networks, Deep Learning, Explainability, Explainable AI, Image Recognition
- 2011: DanNet triggers deep CNN revolution - Feb 4, 2021.
In 2021, we are celebrating the 10-year anniversary of DanNet, which, in 2011, was the first pure deep convolutional neural network (CNN) to win computer vision contests. Read about its history here.
AI, Convolutional Neural Networks, History, Jurgen Schmidhuber, Neural Networks
- Popular Machine Learning Interview Questions, part 2 - Jan 27, 2021.
Get ready for your next job interview requiring domain knowledge in machine learning with answers to these thirteen common questions.
Convolutional Neural Networks, Interview Questions, Linear Regression, Logistic Regression, Machine Learning, Regularization, Transfer Learning, Unbalanced
- Roadmap to Computer Vision - Oct 26, 2020.
Read this introduction to the main steps which compose a computer vision system, starting from how images are pre-processed, features extracted and predictions are made.
Computer Vision, Convolutional Neural Networks, Data Preprocessing, Neural Networks, Roadmap
- AI Papers to Read in 2020 - Sep 10, 2020.
Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science.
AI, Attention, Convolutional Neural Networks, Data Science, GANs, Neural Networks, Reformer, Research
- Introduction to Convolutional Neural Networks - Jun 3, 2020.
The article focuses on explaining key components in CNN and its implementation using Keras python library.
Convolutional Neural Networks, Keras, Neural Networks, Python
- Interactive Machine Learning Experiments - May 26, 2020.
Dive into experimenting with machine learning techniques using this open-source collection of interactive demos built on multilayer perceptrons, convolutional neural networks, and recurrent neural networks. Each package consists of ready-to-try web browser interfaces and fully-developed notebooks for you to fine tune the training for better performance.
Convolutional Neural Networks, GitHub, Image Recognition, Jupyter, Machine Learning, Recurrent Neural Networks, Tutorials
- Google Unveils TAPAS, a BERT-Based Neural Network for Querying Tables Using Natural Language - May 19, 2020.
The new neural network extends BERT to interact with tabular datasets.
BERT, Convolutional Neural Networks, Google, NLP
5 Papers on CNNs Every Data Scientist Should Read - Apr 20, 2020.
In this article, we introduce 5 papers on CNNs that represent both novel approaches and baselines in the field.
Convolutional Neural Networks, Data Scientist, Research
- Brain Tumor Detection using Mask R-CNN - Mar 30, 2020.
Mask R-CNN has been the new state of the art in terms of instance segmentation. Here I want to share some simple understanding of it to give you a first look and then we can move ahead and build our model.
Brain, Cancer Detection, Convolutional Neural Networks, Healthcare, Medical
- Recreating Fingerprints using Convolutional Autoencoders - Mar 4, 2020.
The article gets you started working with fingerprints using Deep Learning.
Autoencoder, Convolutional Neural Networks, Neural Networks, Python
- Create Your Own Computer Vision Sandbox - Feb 5, 2020.
This post covers a wide array of computer vision tasks, from automated data collection to CNN model building.
Computer Vision, Convolutional Neural Networks, Python
Top 10 AI, Machine Learning Research Articles to know - Jan 30, 2020.
We’ve seen many predictions for what new advances are expected in the field of AI and machine learning. Here, we review a “data set” based on what researchers were apparently studying at the turn of the decade to take a fresh glimpse into what might come to pass in 2020.
2020 Predictions, Adversarial, Anomaly Detection, Autoencoder, Convolutional Neural Networks, Graph Theory, NLP, Transformer, Trends
Knowing Your Neighbours: Machine Learning on Graphs - Aug 8, 2019.
Graph Machine Learning uses the network structure of the underlying data to improve predictive outcomes. Learn how to use this modern machine learning method to solve challenges with connected data.
Convolutional Neural Networks, Graph Analytics, Graph Mining, Machine Learning
Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras - Jul 26, 2019.
Different neural network architectures excel in different tasks. This particular article focuses on crafting convolutional neural networks in Python using TensorFlow and Keras.
Convolutional Neural Networks, Keras, Neural Networks, Python, TensorFlow
- An Overview of Human Pose Estimation with Deep Learning - Jun 28, 2019.
Human Pose Estimation is one of the main research areas in computer vision. The reason for its importance is the abundance of applications that can benefit from such a technology. Here's an introduction to the different techniques used in Human Pose Estimation based on Deep Learning.
Computer Vision, Convolutional Neural Networks, Deep Learning, Image Recognition, Object Detection
Predict Age and Gender Using Convolutional Neural Network and OpenCV - Apr 4, 2019.
Age and gender estimation from a single face image are important tasks in intelligent applications. As such, let's build a simple age and gender detection model in this detailed article.
Computer Vision, Convolutional Neural Networks, OpenCV, Python
- Deep Compression: Optimization Techniques for Inference & Efficiency - Mar 20, 2019.
We explain deep compression for improved inference efficiency, mobile applications, and regularization as technology cozies up to the physical limits of Moore's law.
Compression, Convolutional Neural Networks, Deep Learning, ICLR, Inference, Optimization, Regularization
- How to do Everything in Computer Vision - Feb 27, 2019.
The many standard tasks in computer vision all require special consideration: classification, detection, segmentation, pose estimation, enhancement and restoration, and action recognition. Let me show you how to do everything in Computer Vision with Deep Learning!
Computer Vision, Convolutional Neural Networks, Image Classification, Image Recognition, Neural Networks, Object Detection
- Top KDnuggets tweets, Nov 21-27: Intro to #DataScience for Managers – a mindmap; An Introduction to #AI - Nov 28, 2018.
Also: An Introduction to #AI; Intuitively Understanding Convolutions for #DeepLearning; 10 Free Must-See Courses for Machine Learning and Data Science.
Convolutional Neural Networks, Data Science, Manager, Top tweets
- Don’t Use Dropout in Convolutional Networks - Sep 5, 2018.
If you are wondering how to implement dropout, here is your answer - including an explanation on when to use dropout, an implementation example with Keras, batch normalization, and more.
Convolutional Neural Networks, Dropout, Keras
- Text Classification & Embeddings Visualization Using LSTMs, CNNs, and Pre-trained Word Vectors - Jul 5, 2018.
In this tutorial, I classify Yelp round-10 review datasets. After processing the review comments, I trained three model in three different ways and obtained three word embeddings.
Convolutional Neural Networks, Keras, LSTM, NLP, Python, Text Classification, Word Embeddings
- Inside the Mind of a Neural Network with Interactive Code in Tensorflow - Jun 29, 2018.
Understand the inner workings of neural network models as this post covers three related topics: histogram of weights, visualizing the activation of neurons, and interior / integral gradients.
Pages: 1 2
Convolutional Neural Networks, Image Recognition, Neural Networks, Python, TensorFlow
- Using Topological Data Analysis to Understand the Behavior of Convolutional Neural Networks - Jun 28, 2018.
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.
Ayasdi, Convolutional Neural Networks, MNIST, Neural Networks, Topological Data Analysis
Detecting Sarcasm with Deep Convolutional Neural Networks - Jun 21, 2018.
Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence.
arXiv, Convolutional Neural Networks, NLP, Sentiment Analysis
Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API - May 15, 2018.
In this tutorial, a CNN is to be built, and trained and tested against the CIFAR10 dataset. To make the model remotely accessible, a Flask Web application is created using Python to receive an uploaded image and return its classification label using HTTP.
Pages: 1 2
API, Convolutional Neural Networks, Dropout, Flask, Neural Networks, Python, RESTful API, TensorFlow
- KDnuggets™ News 18:n18, May 2: Blockchain Explained in 7 Python Functions; Data Science Dirty Secret; Choosing the Right Evaluation Metric - May 2, 2018.
Also: Building Convolutional Neural Network using NumPy from Scratch; Data Science Interview Guide; Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model; Jupyter Notebook for Beginners: A Tutorial
Blockchain, Convolutional Neural Networks, Data Science, Machine Learning, Metrics, numpy, Python
Building Convolutional Neural Network using NumPy from Scratch - Apr 26, 2018.
In this article, CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), ReLU, and max pooling.
Convolutional Neural Networks, Image Recognition, Neural Networks, numpy, Python
- Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step - Apr 19, 2018.
What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.
Convolutional Neural Networks, Deep Learning, Neural Networks
- Ten Machine Learning Algorithms You Should Know to Become a Data Scientist - Apr 11, 2018.
It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.
Pages: 1 2
Algorithms, Clustering, Convolutional Neural Networks, Decision Trees, Machine Learning, Neural Networks, PCA, Regression, SVM
- The 10 Deep Learning Methods AI Practitioners Need to Apply - Dec 13, 2017.
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.
Pages: 1 2
Backpropagation, Convolutional Neural Networks, Deep Learning, Dropout, Gradient Descent, LSTM, Neural Networks, Transfer Learning
- TensorFlow for Short-Term Stocks Prediction - Dec 12, 2017.
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.
Convolutional Neural Networks, Finance, Python, Stocks, TensorFlow
Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras - Nov 29, 2017.
We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.
Pages: 1 2
Convolutional Neural Networks, Deep Learning, Keras, TensorFlow
7 Steps to Mastering Deep Learning with Keras - Oct 30, 2017.
Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.
7 Steps, Convolutional Neural Networks, Deep Learning, Keras, Logistic Regression, LSTM, Machine Learning, Neural Networks, Python, Recurrent Neural Networks
- 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.
Convolutional Neural Networks, Deep Learning, Image Recognition, Neural Networks
- 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.
AI, Compression, Convolutional Neural Networks, Image Recognition
- 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.
Clarifai, Convolutional Neural Networks, IBM Watson, Image Recognition, 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.
Brain, Convolutional Neural Networks, Deep Learning, Neural Networks, SVDS
- 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?
Convolutional Neural Networks, Neural Networks, Open Source, Visualization
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.
Convolutional Neural Networks, Deep Learning, Natural Language Processing, Neural Networks, NLP, Text Mining
- 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.
Convolutional Neural Networks, Deep Learning, Neural Networks
- Implementing a CNN for Human Activity Recognition in Tensorflow - Nov 21, 2016.
In this post, we will see how to employ Convolutional Neural Network (CNN) for HAR, that will learn complex features automatically from the raw accelerometer signal to differentiate between different activities of daily life.
Pages: 1 2
Convolutional Neural Networks, Deep Learning, TensorFlow, Time Series Classification
- 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.
Pages: 1 2 3
Convolutional Neural Networks, Deep Learning, Explanation, Machine Learning, Neural Networks
- 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.
Convolutional Neural Networks, Deep Learning, Deep Neural Network, Neural Networks, Podcast, Speech
- 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.
Academics, Convolutional Neural Networks, Deep Learning, Image Recognition, Lab41, Machine Learning, Neural Networks
- 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.
Pages: 1 2
Beginners, Convolutional Neural Networks, Deep Learning, Neural Networks
- 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?
Balancing Classes, Convolutional Neural Networks, Data Scientist, Deep Learning, Neural Networks
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.
Pages: 1 2
Beginners, Convolutional Neural Networks, Deep Learning, Neural Networks
- 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.
Pages: 1 2
Brandon Rohrer, Convolutional Neural Networks, Image Recognition, Neural Networks
- Top Machine Learning Libraries for Javascript - Jun 24, 2016.
Javascript may not be the conventional choice for machine learning, but there is no reason it cannot be used for such tasks. Here are the top libraries to facilitate machine learning in Javascript.
Andrej Karpathy, Convolutional Neural Networks, Deep Learning, Javascript, Machine Learning, Neural Networks
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.
Convolutional Neural Networks, Deep 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.
Art, Convolutional Neural Networks, Deep Learning, Machine Learning, Recurrent Neural Networks
- 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.
Pages: 1 2
Convolutional Neural Networks, Deep Learning
- 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!
Pages: 1 2
7 Steps, Caffe, Convolutional Neural Networks, Deep Learning, Matthew Mayo, Recurrent Neural Networks, TensorFlow, Theano
- 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.
Pages: 1 2 3
Convolutional Neural Networks, Deep Learning, Neural Networks, NLP
- 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.
Convolutional Neural Networks, Deep Learning, Image Recognition, MetaMind, Recurrent Neural Networks, Richard Socher, Zachary Lipton
- Popular Deep Learning Tools – a review - Jun 18, 2015.
Deep Learning is the hottest trend now in AI and Machine Learning. We review the popular software for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch.
Convolutional Neural Networks, CUDA, Deep Learning, GPU, Pylearn2, Python, Ran Bi, Theano, Torch
