Tag: TensorFlow

Feed: Recent Announcements. Data preparation is a foundational step of any data science and ML workflow. Therefore, the new TensorFlow 2.6 and PyTorch 1.8 environments come built-in with the recently introduced capability to visually browse and connect to Amazon EMR clusters right from the SageMaker Studio Notebook. Thus, you can interactively explore, visualize and prepare petabyte-scale data using Spark, Hive and Presto on Amazon EMR and build ML models using the latest deep learning frameworks without leaving the notebook ... Read More

Feed: R-bloggers. Author: finnstats. [This article was first published on Methods – finnstats, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Introduction to machine learning with TensorFlow! What is TensorFlow? The Google Brain team created TensorFlow, an open-source library. It was designed for activities that need a lot of numerical computations. TensorFlow was designed specifically for machine learning and deep learning networks. TensorFlow ran faster than python code thanks to the use of ... Read More

Feed: Recent Announcements. AWS Neuron, the SDK for running machine learning inference on AWS Inferentia-based Amazon EC2 Inf1 instances now supports TensorFlow 2. Starting with Neuron 1.15.0 you can execute your TensorFlow 2 BERT based models on Inf1 instances with support for additional models coming soon. To learn more about Neuron TensorFlow 2 support, visit our TensorFlow 2 FAQ page. AWS Neuron is natively integrated with popular ML frameworks such as TensorFlow, PyTorch and Apache MXNet. It includes a deep learning compiler, runtime and tools that assist you with extracting the best performance for your applications. To learn more visit the ... Read More

Feed: Planet PostgreSQL. In part 1 of this blog mini-series we looked at how to setup PostgreSQL so that we can perform regression analysis on our data using TensorFlow from within the database server using the pl/python3 procedural language. In part 2 we looked at how to understand and pre-process the data prior to training a neural network. In this third and find part, we'll use the data that we've prepared and optimised to train a network and then use it to analyse data - all from within a PostgreSQL database!  Splitting the Data When we finished the last part of ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Apoorva Gupta. Tensor Flow permits the subsequent: Tensor Flow helps you to deploy computation to as a minimum one or extra CPUs or GPUs in a computing tool, server, or mobile device in a completely easy manner. This way the matters may be completed very speedy. Tensor Flow lets you specific your computation as a statistics glide graph. Tensor Flow helps you to visualize the graph using the in-constructed tensor board. You can test and debug the graph very without difficulty. Tensor Flow offers the amazing regular overall performance with an ... Read More

Feed: Planet PostgreSQL. In part 1 of this blog mini-series on machine learning, we looked at how to setup PostgreSQL so that we can perform regression analysis on our data using TensorFlow from within the database server using the pl/python3 procedural language. We also looked at some of the basics of pl/python3 as a primer for what is to come in this and part 3 of the series.In this part, we'll look into data pre-processing. In order to ensure that our regression analysis is as accurate as possible, we first need to analyse and understand the raw data. We can use ... Read More

Feed: R-bloggers. Author: finnstats. [This article was first published on Methods – finnstats, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. LSTM network in R, In this tutorial, we are going to discuss Recurrent Neural Networks. Recurrent Neural Networks are very useful for solving sequence of numbers-related issues. The major applications involved in the sequence of numbers are text classification, time series prediction, frames in videos, DNA sequences Speech recognition problems, etc.. A ... Read More

Feed: R-bloggers. Author: dtholmes@mail.ubc.ca. [This article was first published on The Lab-R-torian, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Introduction This is the first of what I am hoping are a number of posts on different machine learning classifiers. The subject matter is not lab medicine but the methodology applies to any similar project. For example, maybe you want to classify the text of a general internal medicine consult into its subspecialty ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: ajit jaokar. Last week, I saw a nice presentation on Probabilistic Programming from a student in Iran (link below).   I am interested in this subject for my teaching at the #universityofoxford. In this post, I provide a brief introduction to Probabilistic programming. Probabilistic programming is a programming paradigm designed to implement and solve probabilistic models. They unite probabilistic modeling and traditional general-purpose programming. Probabilistic programming techniques aim to create systems that help make decisions in the face of uncertainty. There are already a number of existing statistical techniques that handle ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Sergey Lukyanchikov. What is Distributed Artificial Intelligence (DAI)? Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that ... Read More