Here are 10 open-source tools/frameworks for today’s hot topic, AI.
An open-source software library for Machine Intelligence.
TensorFlow™ is an open-source software library, which was originally developed by researchers and engineers working on the Google Brain Team. TensorFlow is 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 provides multiple APIs. The lowest level API — TensorFlow Core — provides you with complete programming control. The higher level APIs are built on top of TensorFlow Core. These higher level APIs are typically easier to learn and use than TensorFlow Core. In addition, the higher level APIs make repetitive tasks easier and more consistent between different users. A high-level API like tf.estimator helps you manage data sets, estimators, training, and inference.
The central unit of data in TensorFlow is the tensor. A tensor consists of a set of primitive values shaped into an array of any number of dimensions. A tensor’s rank is its number of dimensions.
A few Google applications using tensor flow are:
RankBrain: A large-scale deployment of deep neural nets for search ranking on www.google.com
Inception Image Classification Model: Baseline model and follow-on research into highly accurate computer vision models, starting with the model that won the 2014 Imagenet image classification challenge
SmartReply: Deep LSTM model to automatically generate email responses
Massively Multitask Networks for Drug Discovery: A deep neural network model for identifying promising drug candidates by Google in association with Stanford University.
On-Device Computer Vision for OCR: On-device computer vision model to do optical character recognition to enable real-time translation
An optimal workplace for machine learning using big data.
SystemML, the machine-learning technology created at IBM, has reached one of the top-level project status at the Apache Software Foundation and it’s a flexible, scalable, machine learning system. Important characteristics are:
Algorithm customizability via R-like and Python-like languages.
Multiple execution modes, including Spark MLContext, Spark Batch, Hadoop Batch, Standalone, and JMLC (Java Machine Learning Connector).
Automatic optimization based on data and cluster characteristics to ensure both efficiency and scalability.
SystemML considered as SQL for Machine learning. Latest version (1.0.0) of SystemML supports: Java 8+, Scala 2.11+, Python 2.7/3.5+, Hadoop 2.6+, and Spark 2.1+.
It can be run on top of Apache Spark, where it automatically scales your data line by line, determining whether your code should be run on the driver or an Apache Spark cluster. Future SystemML developments include additional deep learning with GPU capabilities such as importing and running neural network architectures and pre-trained models for training.
Java Machine Learning Connector (JMLC) for SystemML
The Java Machine Learning Connector (JMLC) API is a programmatic interface for interacting with SystemML in an embedded fashion. The primary purpose of JMLC is as a scoring API, where your scoring function is expressed using SystemML’s DML (Declarative Machine Learning) language. In addition to scoring, embedded SystemML can be used for tasks such as unsupervised learning (for example, clustering) in the context of a larger application running on a single machine.
A deep learning framework made with expression, speed, and modularity in mind.
The Caffe project was initiated by Yangqing Jia during his Ph.D. at UC Berkeley and then later developed by Berkeley AI Research (BAIR) and by community contributors. It mostly focusses on convolutional networks for computer vision applications. Caffe is a solid and popular choice for computer vision-related tasks and you can download many successful models made by Caffe users from the Caffe Model Zoo (link below) for out-of-the-box use.
Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back.
Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU.
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