Deep Learning (488)

• Why Deep Learning is perfect for NLP (Natural Language Processing) - Apr 20, 2018.
  
  Deep learning brings multiple benefits in learning multiple levels of representation of natural language. Here we will cover the motivation of using deep learning and distributed representation for NLP, word embeddings and several methods to perform word embeddings, and applications.

  Deep Learning, Neural Networks, NLP, Packt Publishing, word2vec

• 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

• Deep Learning With Apache Spark: Part 1 - Apr 18, 2018.
  
  First part on a full discussion on how to do Distributed Deep Learning with Apache Spark. This part: What is Spark, basics on Spark+DL and a little more.

  Apache Spark, Databricks, Deep Learning, Pipeline

• Are High Level APIs Dumbing Down Machine Learning? - Apr 16, 2018.
  
  Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?

  API, Deep Learning, Francois Chollet, Keras, Machine Learning, Neural Networks, TensorFlow

• Top 10 Technology Trends of 2018 - Apr 13, 2018.
  
  In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.

  AI, Blockchain, Chief Data Officer, Deep Learning, Ethics, IoT, NLP, Privacy, Top 10, Trends

• Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works - Apr 11, 2018.
  
  PyTorch has emerged as a major contender in the race to be the king of deep learning frameworks. What makes it really luring is it’s dynamic computation graph paradigm.

  Pages: 1 2

  Deep Learning, Neural Networks, Python, PyTorch

• Top 8 Free Must-Read Books on Deep Learning - Apr 10, 2018.
  
  Deep Learning is the newest trend coming out of Machine Learning, but what exactly is it? And how do I learn more? With that in mind, here's a list of 8 free books on deep learning.

  Deep Learning, Deep Neural Network, Free ebook, Machine Learning, Neural Networks

• Implementing Deep Learning Methods and Feature Engineering for Text Data: The Skip-gram Model - Apr 10, 2018.
  
  Just like we discussed in the CBOW model, we need to model this Skip-gram architecture now as a deep learning classification model such that we take in the target word as our input and try to predict the context words.

  Deep Learning, Feature Engineering, NLP, Python, Text Mining, Word Embeddings

• Top 20 Deep Learning Papers, 2018 Edition - Apr 3, 2018.
  
  Deep Learning is constantly evolving at a fast pace. New techniques, tools and implementations are changing the field of Machine Learning and bringing excellent results.

  Algorithms, Deep Learning, Machine Learning, Neural Networks, TensorFlow, Text Analytics, Trends

• Implementing Deep Learning Methods and Feature Engineering for Text Data: The Continuous Bag of Words (CBOW) - Apr 3, 2018.
  
  The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words).

  Deep Learning, Neural Networks, NLP, word2vec

• A “Weird” Introduction to Deep Learning - Mar 30, 2018.
  
  There are amazing introductions, courses and blog posts on Deep Learning. But this is a different kind of introduction.

  Pages: 1 2

  Deep Learning, Dropout, Neural Networks, Representation, Tensor, TensorFlow

• Semantic Segmentation Models for Autonomous Vehicles - Mar 29, 2018.
  
  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.

  Deep Learning, Self-Driving Car, Semantic Analysis

• Understanding Feature Engineering: Deep Learning Methods for Text Data - Mar 28, 2018.
  
  Newer, advanced strategies for taming unstructured, textual data: In this article, we will be looking at more advanced feature engineering strategies which often leverage deep learning models.

  Deep Learning, Feature Engineering, NLP, Python, Text Mining

• Exploring DeepFakes - Mar 27, 2018.
  
  In this post, I explore the capabilities of this tech, describe how it works, and discuss potential applications.

  Pages: 1 2

  Deep Learning, Image Recognition, Video recognition

• Comparing Deep Learning Frameworks: A Rosetta Stone Approach - Mar 26, 2018.
  
  A Rosetta Stone of deep-learning frameworks has been created to allow data-scientists to easily leverage their expertise from one framework to another.

  Caffe, CNTK, Deep Learning, GPU, Keras, Microsoft, MXNet, PyTorch, TensorFlow

• Multiscale Methods and Machine Learning - Mar 19, 2018.
  
  We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare with similar approaches.

  Algorithms, Data Science, Deep Learning, Machine Learning, Statistics

• Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box - Mar 8, 2018.
  
  The best data scientists have strong imaginative skills for not just “thinking outside the box” – but actually redefining the box – in trying to find variables and metrics that might be better predictors of performance.

  Andrew Ng, Data Science, Data Scientist, Deep Learning, Machine Learning

• Time Series for Dummies – The 3 Step Process - Mar 5, 2018.
  
  Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model.

  Data Science, Deep Learning, Machine Learning, Predictive Modeling, Stationarity, Time Series

• The Current Hype Cycle in Artificial Intelligence - Feb 28, 2018.
  
  Over the past decade, the field of artificial intelligence (AI) has seen striking developments. As surveyed in, there now exist over twenty domains in which AI programs are performing at least as well as (if not better than) humans.

  AGI, AI, Deep Learning, History, Hype, Jobs, Machine Learning

• 5 Fantastic Practical Natural Language Processing Resources - Feb 22, 2018.
  
  This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.

  Deep Learning, Keras, LSTM, Neural Networks, NLP, NLTK, Python

• Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch - Feb 20, 2018.
  
  Now you can develop deep learning applications with Google Colaboratory - on the free Tesla K80 GPU - using Keras, Tensorflow and PyTorch.

  Pages: 1 2

  Deep Learning, Google, Google Colab, Keras, Python, PyTorch, TensorFlow

• Neural network AI is simple. So… Stop pretending you are a genius - Feb 15, 2018.
  
  This post may come off as a rant, but that’s not so much its intent, as it is to point out why we went from having very few AI experts, to having so many in so little time.

  AI, Deep Learning, Neural Networks

• A Basic Recipe for Machine Learning - Feb 13, 2018.
  
  One of the gems that I felt needed to be written down from Ng's deep learning courses is his general recipe to approaching a deep learning algorithm/model.

  Algorithms, Andrew Ng, Coursera, Deep Learning, deeplearning.ai

• Fast.ai Lesson 1 on Google Colab (Free GPU) - Feb 8, 2018.
  
  In this post, I will demonstrate how to use Google Colab for fastai. You can use GPU as a backend for free for 12 hours at a time. GPU compute for free? Are you kidding me?

  Deep Learning, fast.ai, Google, Google Colab, GPU, Jupyter

• 5 Fantastic Practical Machine Learning Resources - Feb 6, 2018.
  
  This post presents 5 fantastic practical machine learning resources, covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks.

  Deep Learning, fast.ai, Gluon, Machine Learning, MOOC, MXNet, Python

• Understanding Learning Rates and How It Improves Performance in Deep Learning - Feb 1, 2018.
  
  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.

  Deep Learning, Hyperparameter, Neural Networks

• The 8 Neural Network Architectures Machine Learning Researchers Need to Learn - Jan 31, 2018.
  
  In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.

  Pages: 1 2

  Architecture, Deep Learning, Machine Learning, Neural Networks

• Automated Text Classification Using Machine Learning - Jan 30, 2018.
  
  In this post, we talk about the technology, applications, customization, and segmentation related to our automated text classification API.

  API, Deep Learning, Machine Learning, ParallelDots, Text Classification

• My Journey into Deep Learning - Jan 30, 2018.
  
  In this post I’ll share how I’ve been studying Deep Learning and using it to solve data science problems. It’s an informal post but with interesting content (I hope).

  Deep Learning, MOOC, Neural Networks

• Deep Learning in H2O using R - Jan 22, 2018.
  
  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.

  Backpropagation, Deep Learning, Gradient Descent, H2O, Machine Learning, R

• Visual Aesthetics: Judging photo quality using AI techniques - Jan 18, 2018.
  
  We built a deep learning system that can automatically analyze and score an image for aesthetic quality with high accuracy. Check the demo and see your photo measures up!

  AI, Deep Learning, Image Recognition

• Democratizing Artificial Intelligence, Deep Learning, Machine Learning with Dell EMC Ready Solutions - Jan 11, 2018.
  
  Democratization is defined as the action/development of making something accessible to everyone, to the “common masses.” AI | ML | DL technology stacks are complicated systems to tune and maintain, expertise is limited, and one minimal change of the stack can lead to failure.

  AI, Deep Learning, Dell, EMC, Machine Learning

• Custom Optimizer in TensorFlow - Jan 8, 2018.
  
  How to customize the optimizers to speed-up and improve the process of finding a (local) minimum of the loss function using TensorFlow.

  Deep Learning, Optimization, TensorFlow

• Deep Learning Made Easy with Deep Cognition - Dec 21, 2017.
  
  So normally we do Deep Learning programming, and learning new APIs, some harder than others, some are really easy an expressive like Keras, but how about a visual API to create and deploy Deep Learning solutions with the click of a button? This is the promise of Deep Cognition.

  Pages: 1 2

  Cloud, Deep Learning, Keras, Neural Networks, TensorFlow

• Building an Audio Classifier using Deep Neural Networks - Dec 15, 2017.
  
  Using a deep convolutional neural network architecture to classify audio and how to effectively use transfer learning and data-augmentation to improve model accuracy using small datasets.

  Acoustics, Audio, Deep Learning, Python, Speech, Speech Recognition, Transfer Learning

• 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

• When reinforcement learning should not be used? - Dec 6, 2017.
  
  While reinforcement learning has achieved many successes, there are situations when it use is problematic. We describe the issues and how to work around them.

  Deep Learning, Online Games, Reinforcement Learning, Self-Driving Car

• Using Deep Learning to Solve Real World Problems - Dec 4, 2017.
  
  Do you assume that deep learning is only being used for toy problems and in self-learning scenarios? This post includes several firsthand accounts of organizations using deep neural networks to solve real world problems.

  Advice, Career, Deep Learning, Neural Networks

• 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

• Understanding Objective Functions in Neural Networks - Nov 23, 2017.
  
  This blog post is targeted towards people who have experience with machine learning, and want to get a better intuition on the different objective functions used to train neural networks.

  Cost Function, Deep Learning, Gradient Descent, Neural Networks, Optimization

• Estimating an Optimal Learning Rate For a Deep Neural Network - Nov 21, 2017.
  
  This post describes a simple and powerful way to find a reasonable learning rate for your neural network.

  Deep Learning, Hyperparameter, Neural Networks

• Top 10 Videos on Deep Learning in Python - Nov 17, 2017.
  
  Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!

  Deep Learning, Keras, Python, PyTorch, TensorFlow, Theano, Top 10, Tutorials, Videolectures, Youtube

• Overview of GANs (Generative Adversarial Networks) – Part I - Nov 10, 2017.
  
  A great introductory and high-level summary of Generative Adversarial Networks.

  Deep Learning, GANs, Generative Adversarial Network, Neural Networks

• Want to know how Deep Learning works? Here’s a quick guide for everyone - Nov 3, 2017.
  
  Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.

  Deep Learning, Neural Networks

• 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

• Neural Networks, Step 1: Where to Begin with Neural Nets & Deep Learning - Oct 28, 2017.
  
  This is a short post for beginners learning neural networks, covering several essential neural networks concepts.

  Beginners, Deep Learning, Neural Networks

• Ranking Popular Deep Learning Libraries for Data Science - Oct 23, 2017.
  
  We rank 23 open-source deep learning libraries that are useful for Data Science. The ranking is based on equally weighing its three components: Github and Stack Overflow activity, as well as Google search results.

  Caffe, Deep Learning, Keras, Python, PyTorch, TensorFlow, Theano

• TensorFlow: Building Feed-Forward Neural Networks Step-by-Step - Oct 23, 2017.
  
  This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details.

  Pages: 1 2 3

  Deep Learning, Neural Networks, TensorFlow

• 5 Free Resources for Furthering Your Understanding of Deep Learning - Oct 20, 2017.
  
  This post includes 5 specific video-based options for furthering your understanding of neural networks and deep learning, collectively consisting of many, many hours of insights.

  Andrew Ng, Deep Learning, Neural Networks, NIPS, Summer School

• 7 Types of Artificial Neural Networks for Natural Language Processing - Oct 19, 2017.
  
  What is an artificial neural network? How does it work? What types of artificial neural networks exist? How are different types of artificial neural networks used in natural language processing? We will discuss all these questions in the following article.

  Pages: 1 2

  AI, Deep Learning, Natural Language Processing, Neural Networks

• An Overview of 3 Popular Courses on Deep Learning - Oct 13, 2017.
  
  After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera (which is not completely released) and Udacity, I believe a post about what you can expect from these 3 courses will be useful for future Deep learning enthusiasts.

  Andrew Ng, Coursera, Deep Learning, deeplearning.ai, fast.ai, Jeremy Howard, Neural Networks

• Tensorflow Tutorial, Part 2 – Getting Started - Sep 28, 2017.
  
  This tutorial will lay a solid foundation to your understanding of Tensorflow, the leading Deep Learning platform. The second part shows how to get started, install, and build a small test case.

  Deep Learning, GPU, Python, TensorFlow

• 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets - Sep 22, 2017.
  
  This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.

  Pages: 1 2 3

  Cheat Sheet, Data Science, Deep Learning, Machine Learning, Neural Networks, Probability, Python, R, SQL, Statistics

• 5 Ways to Get Started with Reinforcement Learning - Sep 20, 2017.
  
  We give an accessible overview of reinforcement learning, including Deep Q Learning, and provide useful links for implementing RL.

  Deep Learning, Machine Learning, Neural Networks, Reinforcement Learning

• New-Age Machine Learning Algorithms in Retail Lending - Sep 13, 2017.
  
  We review the application of new age Machine Learning algorithms for better Customer Analytics in Lending and Credit Risk Assessment.

  Credit Risk, Customer Analytics, Deep Learning, Fintech, Machine Learning, 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

• PyTorch or TensorFlow? - Aug 29, 2017.
  
  PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects. TensorFlow is better for large-scale deployments, especially when cross-platform and embedded deployment is a consideration.

  Deep Learning, Neural Networks, PyTorch, TensorFlow

• An Intuitive Guide to Deep Network Architectures - Aug 28, 2017.
  
  How and why do different Deep Learning models work? We provide an intuitive explanation for 3 very popular DL models: Resnet, Inception, and Xception.

  Pages: 1 2

  Deep Learning, Keras, Neural Networks

• 42 Steps to Mastering Data Science - Aug 25, 2017.
  
  This post is a collection of 6 separate posts of 7 steps a piece, each for mastering and better understanding a particular data science topic, with topics ranging from data preparation, to machine learning, to SQL databases, to NoSQL and beyond.

  Data Preparation, Data Science, Deep Learning, Machine Learning, NoSQL, Python, SQL

• Deep Learning and Neural Networks Primer: Basic Concepts for Beginners - Aug 18, 2017.
  
  This is a collection of introductory posts which present a basic overview of neural networks and deep learning. Start by learning some key terminology and gaining an understanding through some curated resources. Then look at summarized important research in the field before looking at a pair of concise case studies.

  Deep Learning, Neural Networks

• First Steps of Learning Deep Learning: Image Classification in Keras - Aug 16, 2017.
  
  Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over, this post is for you!

  Pages: 1 2

  Deep Learning, Image Recognition, Keras, 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

• Going deeper with recurrent networks: Sequence to Bag of Words Model - Aug 8, 2017.
  
  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.

  Deep Learning, LSTM, Machine Learning, NLP, Recurrent Neural Networks

• How I Used Deep Learning To Train A Chatbot To Talk Like Me - Aug 8, 2017.
  
  In this post, we’ll be looking at how we can use a deep learning model to train a chatbot on my past social media conversations in hope of getting the chatbot to respond to messages the way that I would.

  Pages: 1 2

  Chatbot, Deep Learning

• Train your Deep Learning Faster: FreezeOut - Aug 3, 2017.
  
  We explain another novel method for much faster training of Deep Learning models by freezing the intermediate layers, and show that it has little or no effect on accuracy.

  Deep Learning, Machine Learning, Model Performance, Modeling, Neural Networks

• Summary of Unintuitive Properties of Neural Networks - Jul 24, 2017.
  
  Neural networks work really well on many problems, including language, image and speech recognition. However understanding how they work is not simple, and here is a summary of unusual and counter intuitive properties they have.

  AI, Deep Learning, Hugo Larochelle, Neural Networks

• AI and Deep Learning, Explained Simply - Jul 21, 2017.
  
  AI can now see, hear, and even bluff better than most people. We look into what is new and real about AI and Deep Learning, and what is hype or misinformation.
   

  Pages: 1 2 3

  AI, Deep Learning, Explained, Machine Learning

• 5 Free Resources for Getting Started with Deep Learning for Natural Language Processing - Jul 19, 2017.
  
  This is a collection of 5 deep learning for natural language processing resources for the uninitiated, intended to open eyes to what is possible and to the current state of the art at the intersection of NLP and deep learning. It should also provide some idea of where to go next.

  Deep Learning, Natural Language Processing, Neural Networks, NLP

• Cartoon: The First Ever Self-Driving, Deep Learning Grill - Jul 15, 2017.
  
  New KDnuggets Cartoon looks at what happens when self-driving craze collides with the traditional summer pastime of grilling.

  Adversarial, Cartoon, Deep Learning, Self-Driving Car

• The Strange Loop in Deep Learning - Jul 11, 2017.
  
  This ‘strange loop’ is in fact is the fundamental reason for what Yann LeCun describes as “the coolest idea in machine learning in the last twenty years.”

  Deep Learning, Neural Networks, Yann LeCun

• What Are Artificial Intelligence, Machine Learning, and Deep Learning? - Jul 10, 2017.
  
  AI and Machine Learning have become mainstream, and people know shockingly little about it. Here is an explainer and useful references.

  AI, Artificial Intelligence, Deep Learning, Machine Learning, RapidMiner

• 5 Free Resources for Getting Started with Self-driving Vehicles - Jul 10, 2017.
  
  This is a short list of 5 resources to help newcomers find their bearings when learning about self-driving vehicles, all of which are free. This should be sufficient to learn the basics, and to learn where to look next for further instruction.

  Deep Learning, Machine Learning, Self-Driving Car, Udacity

• Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners, part 2 - Jul 1, 2017.
  
  Here are deep learning examples and demos you can just download and run, including Spotify Artist Search using Speech APIs, Symbolic AI Speech Recognition, and Algorithmia API Photo Colorizer.

  AI, Algorithmia, Beginners, Clarifai, Deep Learning, GitHub, iOS, Speech Recognition, Spotify

• Optimization in Machine Learning: Robust or global minimum? - Jun 30, 2017.
  
  Here we discuss how convex problems are solved and optimised in machine learning/deep learning.

  Deep Learning, Gradient Descent, Machine Learning, Optimization, UAI

• Applying Deep Learning to Real-world Problems - Jun 30, 2017.
  
  In this blog post I shared three learnings that are important to us at Merantix when applying deep learning to real-world problems. I hope that these ideas are helpful for other people who plan to use deep learning in their business.

  Balancing Classes, Deep Learning, Neural Networks, Training, Unbalanced

• Deep Learning with R + Keras - Jun 27, 2017.
  
  Keras has grown in popularity and supported on a wide set of platforms including Tensorflow, CNTK, Apple’s CoreML, and Theano. It is becoming the de factor language for deep learning.

  Deep Learning, Keras, Neural Networks, R

• Understanding Deep Learning Requires Re-thinking Generalization - Jun 16, 2017.
  
  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.

  Deep Learning, Machine Learning, Neural Networks

• Top 15 Python Libraries for Data Science in 2017 - Jun 13, 2017.
  
  Since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity.

  Pages: 1 2

  Data Mining, Data Science, Deep Learning, Machine Learning, Natural Language Processing, Python, Visualization

• Deep Learning Papers Reading Roadmap - Jun 13, 2017.
  
  The roadmap is constructed in accordance with the following four guidelines: from outline to detail; from old to state-of-the-art; from generic to specific areas; focus on state-of-the-art.

  Deep Learning, Neural Networks

• Deep Learning: TensorFlow Programming via XML and PMML - Jun 9, 2017.
  
  In this approach, problem dataset and its Neural network are specified in a PMML like XML file. Then it is used to populate the TensorFlow graph, which, in turn run to get the results.

  Deep Learning, PMML, TensorFlow

• Deep Learning 101: Demystifying Tensors - Jun 2, 2017.
  
  Many deep-learning systems available today are based on tensor algebra, but tensor algebra isn’t tied to deep-learning. It isn’t hard to get started with tensor abuse but can be hard to stop.

  Deep Learning, Tensor, TensorFlow

• Why Does Deep Learning Not Have a Local Minimum? - Jun 2, 2017.
  
  "As I understand, the chance of having a derivative zero in each of the thousands of direction is low. Is there some other reason besides this?"

  Deep Learning, Neural Networks

• Data preprocessing for deep learning with nuts-ml - May 30, 2017.
  
  Nuts-ml is a new data pre-processing library in Python for GPU-based deep learning in vision. It provides common pre-processing functions as independent, reusable units. These so called ‘nuts’ can be freely arranged to build data flows that are efficient, easy to read and modify.

  Data Preparation, Deep Learning, IBM, Image Recognition, Python

• The Path To Learning Artificial Intelligence - May 19, 2017.
  
  Learn how to easily build real-world AI for booming tech, business, pioneering careers and game-level fun.

  AI, Artificial Intelligence, Deep Learning, Learning Path, Machine Learning, Online Education, Python

• The Two Phases of Gradient Descent in Deep Learning - May 12, 2017.
  
  In short, you reach different resting placing with different SGD algorithms. That is, different SGDs just give you differing convergence rates due to different strategies, but we do expect that they all end up at the same results!

  Deep Learning, ICLR, Neural Networks

• 5 Machine Learning Projects You Can No Longer Overlook, May - May 10, 2017.
  
  In this month's installment of Machine Learning Projects You Can No Longer Overlook, we find some data preparation and exploration tools, a (the?) reinforcement learning "framework," a new automated machine learning library, and yet another distributed deep learning library.

  Automated Machine Learning, Data Exploration, Deep Learning, Distributed Systems, Machine Learning, Overlook, Pandas, Reinforcement Learning

• 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

• Building, Training, and Improving on Existing Recurrent Neural Networks - May 8, 2017.
  
  In this post, we’ll provide a short tutorial for training a RNN for speech recognition, including code snippets throughout.

  Deep Learning, Neural Networks, Recurrent Neural Networks, SVDS

• New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? - May 5, 2017.
  
  Vote in KDnuggets 18th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? We will clean, analyze, visualize, and publish the results.

  Data Mining Software, Data Science Platform, Deep Learning, Poll

• Deep Learning in Minutes with this Pre-configured Python VM Image - May 5, 2017.
  
  Check out this Python deep learning virtual machine image, built on top of Ubuntu, which includes a number of machine learning tools and libraries, along with several projects to get up and running with right away.

  Deep Learning, Machine Learning, Python

• Top 10 Machine Learning Videos on YouTube, updated - May 3, 2017.
  
  The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.

  Andrew Ng, Computer Vision, Deep Learning, Geoff Hinton, Google, Machine Learning, Neural Networks, Robots, Video Games, Yaser Abu-Mostafa, Youtube

• Deep Learning – Past, Present, and Future - May 2, 2017.
  
  There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.

  Pages: 1 2

  Andrew Ng, Big Data, Deep Learning, Geoff Hinton, Google, GPU, History, Neural Networks, NVIDIA

• How Not To Program the TensorFlow Graph - May 1, 2017.
  
  Using TensorFlow from Python is like using Python to program another computer. Being thoughtful about the graphs you construct can help you avoid confusion and costly performance problems.

  Deep Learning, Programming, Python, TensorFlow

• The Guerrilla Guide to Machine Learning with Python - May 1, 2017.
  
  Here is a bare bones take on learning machine learning with Python, a complete course for the quick study hacker with no time (or patience) to spare.

  Deep Learning, Machine Learning, Pandas, Python, scikit-learn, Sebastian Raschka

• One Deep Learning Virtual Machine to Rule Them All - Apr 28, 2017.
  
  The frontend code of programming languages only needs to parse and translate source code to an intermediate representation (IR). Deep Learning frameworks will eventually need their own “IR.”

  Deep Learning, Neural Networks

• How to Build a Recurrent Neural Network in TensorFlow - Apr 26, 2017.
  
  This is a no-nonsense overview of implementing a recurrent neural network (RNN) in TensorFlow. Both theory and practice are covered concisely, and the end result is running TensorFlow RNN code.

  Deep Learning, Neural Networks, Recurrent Neural Networks, TensorFlow

• Awesome Deep Learning: Most Cited Deep Learning Papers - Apr 21, 2017.
  
  This post introduces a curated list of the most cited deep learning papers (since 2012), provides the inclusion criteria, shares a few entry examples, and points to the full listing for those interested in investigating further.

  Deep Learning, Neural Networks, Research

• More Deep Learning “Magic”: Paintings to photos, horses to zebras, and more amazing image-to-image translation - Apr 17, 2017.
  
  This is an introduction to recent research which presents an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples.

  Deep Learning, Generative Adversarial Network, Generative Models, Torch

• 5 Machine Learning Projects You Can No Longer Overlook, April - Apr 13, 2017.
  
  It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out. Find tools for data exploration, topic modeling, high-level APIs, and feature selection herein.

  Data Exploration, Deep Learning, Java, Machine Learning, Neural Networks, Overlook, Python, Scala, scikit-learn, Topic Modeling

• Top 20 Recent Research Papers on Machine Learning and Deep Learning - Apr 6, 2017.
  
  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".

  Deep Learning, Machine Learning, Research, Top list, Yoshua Bengio

• Getting Started with Deep Learning - Mar 24, 2017.
  
  This post approaches getting started with deep learning from a framework perspective. Gain a quick overview and comparison of available tools for implementing neural networks to help choose what's right for you.

  Caffe, CNTK, Deep Learning, Keras, SVDS, TensorFlow, Theano, Torch

• Homebrewed Deep Learning and Do-It-Yourself Robotics - Mar 14, 2017.
  
  Learn how to experiment with embodied robotic cognition with IBM Project Intu, a platform that extends Deep Learning and other cognitive services to new devices with minimum coding.

  Cognitive Computing, Deep Learning, IBM, Robots

• Greed, Fear, Game Theory and Deep Learning - Mar 3, 2017.
  
  The most advanced kind of Deep Learning system will involve multiple neural networks that either cooperate or compete to solve problems. The core problem of a multi-agent approach is how to control its behavior.

  AI, Deep Learning, Reinforcement Learning

• 7 More Steps to Mastering Machine Learning With Python - Mar 1, 2017.
  
  This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
   
   

  Pages: 1 2

  7 Steps, Classification, Clustering, Deep Learning, Ensemble Methods, Gradient Boosting, Machine Learning, Python, scikit-learn, Sebastian Raschka

• An Overview of Python Deep Learning Frameworks - Feb 27, 2017.
  
  Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.

  Deep Learning, Keras, Neural Networks, Python, TensorFlow, Theano, Torch

• The Anatomy of Deep Learning Frameworks - Feb 24, 2017.
  
  This post sketches out some common principles which would help you better understand deep learning frameworks, and provides a guide on how to implement your own deep learning framework as well.
   

  Deep Learning, Neural Networks

• Deep Learning, Artificial Intuition and the Quest for AGI - Feb 20, 2017.
  
  Deep Learning systems exhibit behavior that appears biological despite not being based on biological material. It so happens that humanity has luckily stumbled upon Artificial Intuition in the form of Deep Learning.

  AGI, AI, Deep Learning, Machine Intelligence

• Is Deep Learning the Silver Bullet? - Feb 1, 2017.
  
  With nearly every every smart young computer scientist planning to work on deep learning, are there really still artificial intelligence researchers working on other techniques? Is deep learning the AI silver bullet?

  AI, Deep Learning, Machine Learning

• 5 Free Courses for Getting Started in Artificial Intelligence - Feb 1, 2017.
  
  A carefully-curated list of 5 free collections of university course material to help you better understand the various aspects of what artificial intelligence and skills necessary for moving forward in the field.

  AI, Artificial Intelligence, Deep Learning, MIT, Reinforcement Learning, Self-Driving Car, UC Berkeley

• Deep Learning Research Review: Natural Language Processing - Jan 31, 2017.
  
  This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don't have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.

  Pages: 1 2 3

  Deep Learning, Natural Language Processing, Neural Networks, NLP

• Eat Melon: A Deep Q Reinforcement Learning Demo in your browser - Jan 20, 2017.
  
  Check "Eat Melon demo", a fun way to gain familiarity with the Deep Q Learning algorithm, which you can do in your browser.

  Atari, Deep Learning, OpenAI, Reinforcement Learning

• The Data Science Puzzle, Revisited - Jan 20, 2017.
  
  The data science puzzle is re-examined through the relationship between several key concepts in the realm, and incorporates important updates and observations from the past year. The result is a modified explanatory graphic and rationale.

  AI, Big Data, Data Mining, Data Science, Deep Learning, Machine Learning

• Deep Learning Can be Applied to Natural Language Processing - Jan 16, 2017.
  
  This post is a rebuttal to a recent article suggesting that neural networks cannot be applied to natural language given that language is not a produced as a result of continuous function. The post delves into some additional points on deep learning as well.

  Deep Learning, Natural Language Processing, Neural Networks, NLP

• Generative Adversarial Networks – Hot Topic in Machine Learning - Jan 3, 2017.
  
  What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.

  Deep Learning, Generative Adversarial Network, Machine Learning, Neural Networks, NIPS

• Game Theory Reveals the Future of Deep Learning - Dec 29, 2016.
  
  This post covers the emergence of Game Theoretic concepts in the design of newer deep learning architectures. Deep learning systems need to be adaptive to imperfect knowledge and coordinating systems, 2 areas with which game theory can help.

  Architecture, Deep Learning, Optimization

• The Five Capability Levels of Deep Learning Intelligence - Dec 22, 2016.
  
  Deep learning writer Carlos Perez gives his own classification for deep learning-based AI, which is aimed at practitioners. This classification gives us a sense of where we currently are and where we might be heading.

  AI, Deep Learning, Machine Intelligence

• 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

• arXiv Paper Spotlight: Why Does Deep and Cheap Learning Work So Well? - Dec 13, 2016.
  
  The recent paper at hand approaches explaining deep learning from a different perspective, that of physics, and discusses the role of "cheap learning" (parameter reduction) and how it relates back to this innovative perspective.

  Academics, arXiv, Deep Learning, Machine Learning

• Artificial Neural Networks (ANN) Introduction, Part 2 - Dec 9, 2016.
  
  Matching the performance of a human brain is a difficult feat, but techniques have been developed to improve the performance of neural network algorithms, 3 of which are discussed in this post: Distortion, mini-batch gradient descent, and dropout.

  Algobeans, Deep Learning, Neural Networks

• Why Deep Learning is Radically Different From Machine Learning - Dec 5, 2016.
  
  There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference?

  Deep Learning, Machine Learning

• The hard thing about deep learning - Dec 1, 2016.
  
  It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks.

  CA, Deep Learning, Neural Networks, NP-hard, Optimization, San Jose, Strata

• Deep Learning Research Review: Reinforcement Learning - Nov 25, 2016.
  
  This edition of Deep Learning Research Review explains recent research papers in Reinforcement Learning (RL). If you don't have the time to read the top papers yourself, or need an overview of RL in general, this post has you covered.

  Pages: 1 2

  Deep Learning, Machine Learning, Reinforcement Learning

• 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

• Questions To Ask When Moving Machine Learning From Practice to Production - Nov 18, 2016.
  
  An overview of applying machine learning techniques to solve problems in production. This articles covers some of the varied questions to ponder when incorporating machine learning into teams and processes.

  Data Science, Deep Learning, Deployment, Machine Learning, Production

• Deep Learning Reading Group: Skip-Thought Vectors - Nov 17, 2016.
  
  Skip-thought vectors take inspiration from Word2Vec skip-gram and attempt to extend it to sentences, and are created using an encoder-decoder model. Read on for an overview of the paper.

  Deep Learning, Lab41, Natural Language Processing, Neural Networks, word2vec

• 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

• A Quick Introduction to Neural Networks - Nov 9, 2016.
  
  This article provides a beginner level introduction to multilayer perceptron and backpropagation.

  Pages: 1 2 3

  Backpropagation, Deep Learning, 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

• 5 EBooks to Read Before Getting into A Machine Learning Career - Oct 21, 2016.
  
  A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.

  Bayesian, Data Science, Deep Learning, Free ebook, Machine Learning, Reinforcement Learning

• Artificial Intelligence, Deep Learning, and Neural Networks, Explained - Oct 14, 2016.
  
  This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

  AI, Artificial Intelligence, Deep Learning, Explained, Neural Networks