This is the second of a multi-part series explaining the fundamentals of deep learning by long-time tech journalist Michael Copeland. That's how to think about deep neural networks going through the "training" phase. Neural networks get an education for the same reason most people do -- to learn to do a job. More specifically, the trained neural network is put to work out in the digital world using what it has learned -- to recognize images, spoken words, a blood disease, or suggest the shoes someone is likely to buy next, you name it -- in the streamlined form of an application. This speedier and more efficient version of a neural network infers things about new data it's presented with based on its training.

TensorFlow is the library that revolutionized the way we approach machine learning problems. It was designed to build deep neural networks, train them, and evaluate and serve the solutions. The result of its popularity is the genuine democratization of AI. Like any library, it provides classes and functions designed to tackle deep learning process. This introduces an interesting black box dilemma.

Graphics in this book are printed in black and white. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--scikit-learn and TensorFlow--author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems.

In this article, I will try to give a fairly simple and understandable explanation of one really fascinating type of neural network. Introduced by Cho, et al. in 2014, GRU (Gated Recurrent Unit) aims to solve the vanishing gradient problem which comes with a standard recurrent neural network. GRU can also be considered as a variation on the LSTM because both are designed similarly and, in some cases, produce equally excellent results. If you are not familiar with Recurrent Neural Networks, I recommend reading my brief introduction. For better understanding of LSTM, many people recommend Christopher Olah's article.

For the course "Deep Learning for Business," the first module is "Deep Learning Products & Services," which starts with the lecture "Future Industry Evolution & Artificial Intelligence" that explains past, current, and future industry evolutions and how DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of future industry in the near future. The following lectures look into the hottest DL and ML products and services that are exciting the business world. Then the Amazon Echo and Echo Dot products are introduced along with the Alexa cloud based DL personal assistant that uses ASR (Automated Speech Recognition) and NLU (Natural Language Understanding) technology. The next lecture focuses on LettuceBot, which is a DL system that plants lettuce seeds with automatic fertilizer and herbicide nozzles control. Then the computer vision based DL blood cells analysis diagnostic system Athelas is introduced followed by the introduction of a classical and symphonic music composing DL system named AIVA (Artificial Intelligence Virtual Artist).

The average respondent used 7.7 tools/methods, similar to 2016 poll. Next, we compared the top 16 methods in this year's poll with their share last year - see Figure 1. We note a significant increase in Random Forests, Visualization, and Deep Learning share of usage, and decline in K-nn, PCA, and Boosting. Gradient Boosting Machines was a new entry in 2017. Deep Learning, despite its amazing successes, is reported used by only about 20% of KDnuggets readers.

Exploding gradients are a problem where large error gradients accumulate and result in very large updates to neural network model weights during training. This has the effect of your model being unstable and unable to learn from your training data. In this post, you will discover the problem of exploding gradients with deep artificial neural networks. A Gentle Introduction to Exploding Gradients in Recurrent Neural Networks Photo by Taro Taylor, some rights reserved. An error gradient is the direction and magnitude calculated during the training of a neural network that is used to update the network weights in the right direction and by the right amount.

Before scientists can effectively capture and deploy fusion energy, they must learn to predict major disruptions that can halt fusion reactions and damage the walls of doughnut-shaped fusion devices called tokamaks. Timely prediction of disruptions, the sudden loss of control of the hot, charged plasma that fuels the reactions, will be vital to triggering steps to avoid or mitigate such large-scale events. Today, researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University are employing artificial intelligence to improve predictive capability. Researchers led by William Tang, a PPPL physicist and a lecturer with the rank of professor in astrophysical sciences at Princeton, are developing the code for predictions for ITER, the international experiment under construction in France to demonstrate the practicality of fusion energy. The new predictive software, called the Fusion Recurrent Neural Network (FRNN) code, is a form of "deep learning" -- a newer and more powerful version of modern machine learning software, an application of artificial intelligence.

The functions of proteins and RNAs are determined by a myriad of interactions between their constituent residues, but most quantitative models of how molecular phenotype depends on genotype must approximate this by simple additive effects. While recent models have relaxed this constraint to also account for pairwise interactions, these approaches do not provide a tractable path towards modeling higher-order dependencies. Here, we show how latent variable models with nonlinear dependencies can be applied to capture beyond-pairwise constraints in biomolecules. We present a new probabilistic model for sequence families, DeepSequence, that can predict the effects of mutations across a variety of deep mutational scanning experiments significantly better than site independent or pairwise models that are based on the same evolutionary data. The model, learned in an unsupervised manner solely from sequence information, is grounded with biologically motivated priors, reveals latent organization of sequence families, and can be used to extrapolate to new parts of sequence space.

Learning-based representations have become the defacto means to address computer vision tasks. Despite their massive adoption, the amount of work aiming at understanding the internal representations learned by these models is rather limited. Existing methods aimed at model interpretation either require exhaustive manual inspection of visualizations, or link internal network activations with external "possibly useful" annotated concepts. We propose an intermediate scheme in which, given a pretrained model, we automatically identify internal features relevant for the set of classes considered by the model, without requiring additional annotations. We interpret the model through average visualizations of these features. Then, at test time, we explain the network prediction by accompanying the predicted class label with supporting heatmap visualizations derived from the identified relevant features. In addition, we propose a method to address the artifacts introduced by strided operations in deconvnet-based visualizations. Our evaluation on the MNIST, ILSVRC 12 and Fashion 144k datasets quantitatively shows that the proposed method is able to identify relevant internal features for the classes of interest while improving the quality of the produced visualizations.