[unable to retrieve full-text content]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.
Original Post: Deep Learning Can be Applied to Natural Language Processing
By Pablo Soto, Research Engineer at Tryolabs. Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all. One of the main challenges researchers have historically struggled with has been unsupervised learning. We think 2016 has been a great year for this area, mainly because of the vast amount of work on Generative Models. Moreover, the ability to naturally communicate with machines has been also one of the dream goals and several approaches have been presented by giants like Google and…
Original Post: The Major Advancements in Deep Learning in 2016
By Al Gharakhanian. NIPS2016 (Neural Information Processing System) is an annual event that attracts the best and the brightest of the field of Machine Learning both from academia as well as industry. I attended this event last week for the very first time and was blown away by the volume and diversity of the presentations. One unusual observation was that a large chunk of exhibitors were hedge funds in search of ML talent. Some of the papers were highly abstract and theoretical while others quite pragmatic from the likes of Google, Facebook. The topics were wide-ranging but there were two topics stood out attracting a sizable attention. The first was “Generative Adversarial Networks” (GANs for short), while the second was “Reinforcement Learning” (RL for short). My plan is to cover GANs in this post and hope to do the same for RL in a future post.…
Original Post: Generative Adversarial Networks – Hot Topic in Machine Learning
Previous instalments of “5 Machine Learning Projects You Can No Longer Overlook” brought to light a number of lesser-known machine learning projects, and included both general purpose and specialized machine learning libraries and deep learning libraries, along with auxiliary support, data cleaning, and automation tools. After a hiatus, we thought the idea deserved another follow-up. This post will showcase 5 machine learning projects that you may not yet have heard of, including those from across a number of different ecosystems and programming languages. You may find that, even if you have no requirement for any of these particular tools, inspecting their broad implementation details or their specific code may help in generating some ideas of your own. Like the previous iteration, there is no formal criteria for inclusion beyond projects that have caught my eye over time spent online, and…
Original Post: 5 Machine Learning Projects You Can No Longer Overlook, January
By Arthur Juliani, University of Oregon. When it comes to neural network design, the trend in the past few years has pointed in one direction: deeper. Whereas the state of the art only a few years ago consisted of networks which were roughly twelve layers deep, it is now not surprising to come across networks which are hundreds of layers deep. This move hasn’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 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. I will…
Original Post: ResNets, HighwayNets, and DenseNets, Oh My!
By Brian Wang, Next Big Future. I am big. It’s the pictures that got small. – Quote from Sunset Boulevard movie How is deep learning solving problems that seem to have an incredibly huge space of possible solutions? The actual number of possible solutions is vastly smaller than earlier estimates. Deep Learning is useful and powerful but it is also that the problems were not as big or as hard as researchers feared when they were unsolved. Last year, Deep Learning AI accomplished a task many people thought impossible: DeepMind, Google’s deep learning AI system, defeated the world’s best Go player after trouncing the European Go champion. The feat stunned the world because the number of potential Go moves exceeds the number of atoms in the universe, and past Go-playing robots performed only as well as a mediocre human player. But…
Original Post: Deep Learning Works Great Because the Universe, Physics and the Game of Go are Vastly Simpler than Prior Models and Have Exploitable Patterns
Data Science, Predictive Analytics Main Developments in 2016, Key Trends in 2017; Where Analytics, Data Mining, Data Science were applied in 2016; Bayesian Basics, Explained; Data Science Trends To Look Out For In 2017; Artificial Neural Networks (ANN) Introduction Features | Software | Tutorials | Opinions | News | Courses | Meetings | Jobs | Academic | Tweets | Image of the Week Features Software Tutorials, Overviews, How-Tos Opinions News Courses Meetings Jobs Academic Top Tweets Image of the Week From 4 Cognitive Bias Key Points Data Scientists Need to Know Top Stories Past 30 Days
Original Post: KDnuggets™ News 16:n44, Dec 14: Key Data Science 2016 Events, 2017 Trends; Where Data Science was applied; Bayesian Basics
[unable to retrieve full-text content]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.
Original Post: Artificial Neural Networks (ANN) Introduction, Part 2
[unable to retrieve full-text content]This intro to ANNs will look at how we can train an algorithm to recognize images of handwritten digits. We will be using the images from the famous MNIST (Mixed National Institute of Standards and Technology) database.
Original Post: Artificial Neural Networks (ANN) Introduction, Part 1
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. By Reza Zadeh, Founder and CEO of Matroid. At the heart of deep learning lies a hard optimization problem. So hard that for several decades after the introduction…
Original Post: The hard thing about deep learning