AI News, Machine Learning Tutorial: High Performance Text Processing

Machine Learning Tutorial: High Performance Text Processing

Daniel Krasner (Co-founder KFit Solutions and Research Scholar at Columbia University) covers rapid development of high performance scalable text processing solutions for tasks such as classification, semantic analysis, topic modeling and general machine learning.

How Python modules, and in particular the Rosetta Python library, can be used to process, clean, tokenize, extract features, and finally build statistical models with large volumes of text data.

(Easy) High Performance Text Processing in Machine Learning by Daniel Krasner

See the full post here: This talk covers rapid development of high ..

Sharing your Python machine learning model

Ever wondered how you can share your machine learning models with others. One way is to use the python pickle library. In this tutorial I create a random forest ...

Build a TensorFlow Image Classifier in 5 Min

In this episode we're going to train our own image classifier to detect Darth Vader images. The code for this repository is here: ...

SPACY'S ENTITY RECOGNITION MODEL: incremental parsing with Bloom embeddings & residual CNNs

spaCy v2.0's Named Entity Recognition system features a sophisticated word embedding strategy using subword features and "Bloom" embeddings, a deep ...

Cloud OnAir: Build Your First Serverless Application With Google Cloud Functions

Find out how your developer teams can work orders-of-magnitude faster and build applications whose functionality, security, and reliability are as good as that of ...

Automatic Speech Recognition - An Overview

An overview of how Automatic Speech Recognition systems work and some of the challenges. See more on this video at ...

Lecture 8 | Deep Learning Software

In Lecture 8 we discuss the use of different software packages for deep learning, focusing on TensorFlow and PyTorch. We also discuss some differences ...

Android Meets TensorFlow: How to Accelerate Your App with AI (Google I/O '17)

Portability is one of the main benefits of TensorFlow -- you can easily move a neural network model to Android and run predictions on mobile phones, for all ...

Keynote - Building the intelligent apps of the future | K102

XLA: TensorFlow, Compiled! (TensorFlow Dev Summit 2017)

Speed is everything for effective machine learning, and XLA was developed to reduce training and inference time. In this talk, Chris Leary and Todd Wang ...