Tag: Deep Learning

Feed: All - O'Reilly Media. Author: Siddha Ganju. Cross entropy loss per epoch curve at Learning Rate 1 (source: Siddha Ganju, used with permission) Check out Siddha Ganju’s talk on embedded deep learning at the Artificial Intelligence Conference in San Francisco, Sept. 17-20, 2017. Registration is now open. Learning rate is the rate at which the accumulation of information in a neural network progresses over time. The learning rate determines how quickly (and whether at all) the network reaches the optimum, most conducive location in the network for the specific output desired. In plain Stochastic Gradient Descent (SGD), the learning ... Read More

Feed: All - O'Reilly Media. Author: Yishay Carmiel. Soundwave. (source: Pixabay) To learn more about conversational AI, check out Yishay Carmiel’s session Applications of neural-based models for conversational speech at the Artificial Intelligence Conference in San Francisco, Sept. 17-20, 2017. Registration is now open. The dream of speech recognition is a system that truly understands humans speaking—in different environments, with a variety of accents and languages. For decades, people tackled this problem with no success. Pinpointing effective strategies for creating such a system seemed impossible. In the past years, however, breakthroughs in AI and deep learning have changed everything in ... Read More

Feed: All - O'Reilly Media. Author: Ben Lorica. Stockton Infinity Bridge (source: darren price on Flickr) There will back-to-back half-day tutorials on reinforcement learning at the upcoming Artificial Intelligence Conference in San Francisco: Building reinforcement learning applications with Ray and Introduction to reinforcement learning. As more companies begin to experiment with and deploy machine learning in different settings, it’s good to look ahead at what future systems might look like. Today, the typical sequence is to gather data, learn some underlying structure, and deploy an algorithm that systematically captures what you’ve learned. Gathering, preparing, and enriching the right data—particularly training ... Read More

Feed: All - O'Reilly Media. Author: Ben Lorica. Anatomy. (source: Pixabay) Learn how companies are using deep learning and AI by attending the Artificial Intelligence Conference in San Francisco, September 17-20, 2017. Save 20% with the code BIGDATA20. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Soumith Chintala, AI research engineer at Facebook. Among his many research projects, Chintala was part of the team behind DCGAN (Deep Convolutional Generative Adversarial ... Read More

Feed: All - O'Reilly Media. Author: Adam Michael Wood. Pyramid pattern. (source: Pixabay) In "Learning to Love Data Science," Mike Barlow looks at how organizations are using data science to turn information into wisdom and business value. Download a collection of free chapters from "Learning to Love Data Science." We are swimming in data. Or possibly drowning in it. Organizations and individuals are generating and storing data at a phenomenal and ever-increasing rate. The volume and speed of data collection has given rise to a host of new technologies and new roles focused on dealing with this data, managing it, ... Read More

Feed: All - O'Reilly Media. Author: Jon Bruner. Framework (source: Pixabay) After several decades as an interest of academic computer scientists and specialized research labs, deep learning is appearing widely in real products. That transition has led to several exciting new deep-learning frameworks that tend to emphasize either rapid prototyping or efficient deployment at scale. For product developers looking to experiment with an idea and then reinforce and deploy it, a single framework that supports both ends of the process is helpful. Apache MXNet, an open-source deep learning framework first published in 2015, aims to achieve exactly that. I recently ... Read More

Feed: All - O'Reilly Media. Author: Manu Jeevan, Suresh Chelladurai. Road signs (source: Pixabay) The full Jupyter Notebook for this post can be accessed here. Although there are many deep learning frameworks, including TensorFlow, Keras, Torch, and Caffe, Apache MXNet in particular is gaining popularity due to its scalability across multiple GPUs. In this blog post, we'll tackle a computer vision problem: classifying German traffic signs using a convolutional neural network. The network takes a color photo containing a traffic sign image as input, and tries to identify the type of sign. The full notebook can be found here In ... Read More

Feed: R-bloggers. Author: Florian Teschner. The last months, I have worked on brand logo detection in R with Keras. Starting with a model from scratch adding more data and using a pretrained model. The goal is to build a (deep) neural net that is able to identify brand logos in images. Just to recall, the dataset is a combination of the Flickr27-dataset, with 270 images of 27 classes and self-scraped images from google image search. In case you want to reproduce the analysis, you can download the set here. In the last post, I used the VGG-16 pretrained model and ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. Monday newsletter published by Data Science Central. Previous editions can be found here.  The contribution flagged with a + is our selection for the picture of the week. Featured Resources and Technical Contributions Generative Adversarial Networks (GANs) - Engine and Applications +  Building Convolutional Neural Networks with Tensorflow  Dogs vs. Cats: Image Classification with Deep Learning using TensorFlow in Python  Neural Networks: Crash Course On Multi-Layer Perceptron  Data Science Simplified Part 7: Log-Log Regression Models  Combining Multiple Methods To Improve Time Series Prediction  Nice Generalization of the K-NN Clustering Algorithm - Also ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Luba Belokon. Generative adversarial networks (GANs) are a class of neural networks that are used in unsupervised machine learning. They help to solve such tasks as image generation from descriptions, getting high resolution images from low resolution ones, predicting which drug could treat a certain disease, retrieving images that contain a given pattern, etc. Our team asked a data scientist, Anton Karazeev, to make the introduction to GANs engine and their applications in everyday life. GANs were  introduced by Ian Goodfellow in 2014.They aren’t the only approach of neural networks in ... Read More