内容简介：Watson Studio Recognition service uses cutting-edge deep learning algorithms to analyze and classify images and detect objects and other content. It allows users to build a collaborative environment where machine learning engineers can work together remote
Visual Recognition in Watson Studio
Watson Studio Recognition service uses cutting-edge deep learning algorithms to analyze and classify images and detect objects and other content. It allows users to build a collaborative environment where machine learning engineers can work together remotely. It connects the projects with Github to make it easier to push and pull the changes. It supports different image formats such as JPEG and PNG. It also supports notebooks.
Natural Language Classifier
Watson Studio natural language classifier allows users to build text classifiers based on user-defined classes. It supports CSV (.csv) data files that contain samples of each class. It supports different languages including English, Arabic, French, German, Italian, Japanese, Korean, Portuguese (Brazilian), and Spanish. Watson Studio provides multi-category classification and multi-phase classification (up to 30 separate inputs in a single API request). It also supports APIs in notebooks.
Here is a sample model built using Watson Studio with three classes (hi, problem, question.
AutoAI Examples
In the following, I list several blogs that I built to guide you throw the steps of training and deploying different models using AutoAI:
Image Classification
We will use Stanford Dogs Dataset that contains images of 120 dog breeds from around the world. The goal of our classifier is to classify a dog image based on its breed.
Use pre-trained Image Classifiers
We learn in this blog how to reuse a pre-trained model by exposing them as an API for inference tasks.
Object Detection
In this blog, we learn how to build a model to detect cancer and fluids in brain scans.
Text Classification
We build a model for classifying different StackOverflow posts into different tags.
If you are new to image classification/deep learning, check my post to learn the basics of deep learning and understand the whole training process end-to-end in the following blog:
Learn more about Embeddings and text models
If you a beginner with textual data and would like to start from scratch, take a look at the following post
Conclusion
Machine learning engineers often spend considerable amounts of time searching for a proper model (reference model). Using AutoAI drastically improves and facilitates this step. It helps in building deep learning models easily providing your data only — without a single line of code.
Please let me know if you have any questions!
Resources
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perl进阶
Randal L.Schwartz、brian d.foy、Tom Phoenix / 韩雷 / 人民邮电出版社 / 2015-10-1 / 69
本书是Learning Perl一书的进阶。学完本书之后，您可以使用Perl语言的特性编写从简单脚本到大型程序在内的所有程序，正是Perl语言的这些特性使其成为通用的编程语言。本书为读者深入介绍了模块、复杂的数据结构以及面向对象编程等知识。 本书每章的篇幅都短小精悍，读者可以在一到两个小时内读完，每章末尾的练习有助于您巩固在本章所学的知识。如果您已掌握了Learning Perl中的内容并渴......一起来看看 《perl进阶》 这本书的介绍吧!
