[PYTHON] Organize machine learning and deep learning platforms

Introduction

Recently, the openness of the platform has become popular, so let's reorganize the machine learning platform and deep learning platform (framework).

• • • Please point out if it is different from the fact.

Main platforms

PaaS service

Learning framework (Python)

A brief description of the PaaS service

The services provided by each vendor include pre-learned model translation and image / video recognition.

Google Cloud Platform

It will be a machine learning platform provided by Google. A managed service that makes it easy to build machine learning models for any data, regardless of size. You can also use TensorFlow prepared on the cloud.

• Excerpt from the site

Contents of machine learning

・ Cloud Machine Learning Engine You can learn from various data such as supervised regression / classification and unsupervised clustering. The algorithm used is not disclosed.

Azure Machine Learning

Azure Machine Learning is a cloud predictive analytics service that enables you to quickly create and deploy predictive models as an analytics solution. With a library of ready-to-use algorithms, you can create models on an internet-connected PC and quickly deploy your forecasting solution.

• Excerpt from the site

Contents of machine learning

· Azure ML You can also learn from various data such as supervised regression / classification and unsupervised clustering. You can select the algorithm to use. (There are too many algorithms and it is over-engineered.)

Amazon Machine Learning

Amazon Machine Learning is a managed service for building machine learning models and generating predictions, allowing you to develop robust, scalable, and advanced applications. Amazon Machine Learning allows you to use powerful machine learning technologies without the need for extensive background knowledge of machine learning algorithms and techniques.

• Excerpt from the site

Contents of machine learning

・ Amazon Machine Learning You can also learn from various data such as supervised regression / classification and unsupervised clustering.

[Algorithm used] Regression: Linear Regression Classification Binary classification: Logistic Regression Multiclass classification: Multinomial logistic Regression

Bluemix IBM Watson

IBM defines AI as "Augmented Intelligence" rather than "Artificial Intelligence" to expand and enhance human knowledge, and provides customers with IBM Watson as a core cognitive solution. It offers. IBM Watson is a technology platform that uses natural language processing and machine learning to uncover insights from large amounts of unstructured data.

• Excerpt from the site

Contents of machine learning

You can also learn from various data such as supervised regression / classification and unsupervised clustering. Also, since there are multiple learned APIs, it is convenient when creating a new service. 　 [Algorithm used] private It seems that "consider multiple available algorithms and select the one that meets your needs."

A brief description of the learning framework

It is a framework distributed by OSS.

Theano

http://deeplearning.net/software/theano/index.html

Called the grandfather of the deep learning framework, many global researchers use Theano written in Python.

merit and demerit

TensorFlow

https://www.tensorflow.org/

TnesorFlow is the latest library of neural networks developed by Google under the project "Google Brain".

High-level functionality can be implemented and calculations can be represented in data flows and graphs. The inside of the library is made in C ++, but Python is also included and it has a structure that can be used flexibly.

merit and demerit

Caffe

http://caffe.berkeleyvision.org/

Caffe is a well-known and widely used machine vision library that is a C and C ++ port of the fast convolutional network implemented by Matlab. Caffe focuses on image processing and does not target other deep learning such as text, voice, and time series data.

merit and demerit

Chainer

http://chainer.org/

Chainer is an open source neural network framework with a Python API. The core development team is active in Preferred Networks, a Tokyo-based machine learning startup, with many engineers from the University of Tokyo. Until the advent of CMU's DyNet and Facebook's PyTorch, Chainer was the main neural network framework for dynamic computational graphs and was a network that allowed input of various lengths, making it popular for natural language processing work. It is a high function. Using its own benchmarks, Chainer is faster than other frameworks for Python and features TensorFlow, the slowest test group including MxNet and CNTK.

merit and demerit

Keras

https://keras.io/

Keras is a library used at the top of Theano and TensorFlow, which does not have a wrapper for deep learning of high-speed calculation libraries, and provides an intuitive API.

When you start deep learning, the programming barrier stands out. However, with Keras, even if you have no programming experience, you can quickly learn a simple network.

at the end

There is no difference between PaaS companies in what they can achieve, so I think the game will be ease of use.

Regarding the framework, SONY was released the other day! It seems that OSS will continue to increase in the future!

I think it's best to choose what you need to achieve what you want to do, but I feel that what is absolutely necessary is the fundamentals such as algorithm selection and neural design.