8 Deep Learning Software Libraries : 8+ Deep Learning Software Libraries including Torch, Deeplearning4j, Gensim, Caffe, Theano, ND4J, DeepLearnToolbox, convnetjs.Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text.
TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
The Greenplum Database (GPDB) is an advanced, fully featured, open source data warehouse. It provides powerful and rapid analytics on petabyte scale data volumes. Uniquely geared toward big data analytics, Greenplum Database is powered by the world’s most advanced cost-based query optimizer delivering high analytical query performance on large data volumes.
An important step towards next-generation ultra-compact photonic and optoelectronic devices has been taken with the realization of a two-dimensional excitonic laser. Berkeley Lab researchers have embedded a monolayer of tungsten disulfide into a microdisk resonator to achieve bright excitonic lasing at visible light wavelengths.
We tackle image question answering (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose weights are determined adaptively based on a question. For the adaptive parameter prediction, we employ a separate parameter prediction network, which consists of gated recurrent units (GRU) taking a question as its input and a fully-connected layer generating a set of candidate weights as its output. Since the dynamic parameter layer is a fully connected layer, it is challenging to predict a large number of parameters in the layer to construct the CNN for ImageQA. We reduce the complexity of this problem by incorporating a hashing technique, where the candidate weights given by the parameter prediction network are selected using a predefined hash function to determine individual weights in the dynamic parameter layer. The proposed network---joint network with the CNN for ImageQA and the parameter prediction network---is trained end-to-end through back-propagation, where its weights are initialized using a pre-trained CNN and GRU. The proposed algorithm illustrates the state-of-the-art performance on all available public ImageQA benchmarks.
This list is an attempt to collect and categorize a growing critical literature on algorithms as social concerns. The work included spans sociology, anthropology, science and technology studies, geography, communication, media studies, and legal studies, among others. Our interest in assembling this list was to catalog the emergence of “algorithms” as objects of interest for disciplines beyond mathematics, computer science, and software engineering.
The new integration between Flume and Kafka offers sub-second-latency event processing without the need for dedicated infrastructure. In this previous post you learned some Apache Kafka basics and explored a scenario for using Kafka in an online application. This post takes you a step further and highlights the integration of Kafka with Apache Hadoop, demonstrating both a basic ingestion capability as well as how different open-source components can be easily combined to create a near-real time
In this paper, a framework for testing Deep Neural Network (DNN) design in Python is presented. First, big data, machine learning (ML), and Artificial Neural Networks (ANNs) are discussed to familiarize the reader with the importance of such a system. Next, the benefits and detriments of implementing such a system in Python are presented. Lastly, the specifics of the system are explained, and some experimental results are presented to prove the effectiveness of the system.
By Steph Locke We polled our consultants for their tips on how to be more effective at writing R code; and here are the top 10! 10. Don’t pre-emptively tidy Spending time on formatting as you go along gives you … Continue reading →
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