Object Detection, Translation, and AutoML Vision Edge

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Photo by Andrew Neel on Unsplash

Last year, at I/O 2018, Google announced a brand new SDK available for developers: ML Kit. It’s no surprise that Google’s advances in machine learning are miles ahead of what any other company is aiming for. Through this SDK, Google was hoping to help mobile developers bring machine learning to their apps with simple, concise code. As part of the Firebase ecosystem, ML Kit allows developers to implement ML functionality with just a few lines of code; everything from vision to natural language to custom models.

This year, at I/O 2019, Google’s ML Kit team had 3 new features in store for us. These APIs add on to ML Kit’s already-impressive library of machine learning frameworks. In this tutorial, we’ll take a look at how to incorporate these new ML Kit features in our Swift apps. Let’s get started! …


A summary of Apple’s WWDC 2019 Session: Designing Great ML Experiences.

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Photo by José Alejandro Cuffia on Unsplash

The following tutorial is a summary of Apple’s WWDC 2019 Session: Designing Great ML Experiences. If you’d like to watch the hour long session, the link is attached at the end of the article.

Introduction

When developing mobile apps, as machine learning engineers, we often like to focus on the more complex tasks of ML, such as training a model and trying to get the highest possible accuracy. …


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Photo by Daniel Korpai on Unsplash

In October 2018, Apple announced the brand new iPad Pro and the all-new Apple Pencil 2.0. Unlike the previous generation of the Apple Pencil, this utensil offers developers some extra fun APIs to play around with in order to enhance their app’s functionality and UX. In this tutorial, I will show you how to make your app support the Apple Pencil 2.

Note: To test the demo app in this tutorial, you will need a real iPad Pro which is compatible with the second gen Apple Pencil. The simulator does not offer this functionality. …

Sentence Splitting, Tokenization, Stemming, Lemmatization, and Stop Word Removal

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What is Text Wrangling?

Although is has many forms, text wrangling is basically the pre-processing work that’s done to prepare raw text data ready for training. Simply put, it’s the process of cleaning your data to make it readable by your program, and then formatting it as such.

Many of you may be wrangling text without knowing it yourself. In this tutorial, I will teach you how to clean up your text in Python. I will show you to perform the most common forms of text wrangling: sentence splitting, tokenization, stemming, lemmatization, and stop word removal.

Prerequisites

Obviously, you’ll need a little bit of Python know-how in order to run the code I’ll show below. I’ll be using a Google Colab notebook to host all my code. I’ll share the link at the end so you can see how your code compares. …


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If you’ve been following Apple’s announcements from the past year, you know that they are heavily invested in machine learning. Ever since they introduced Core ML last year at WWDC 2017, there are tons of apps which have sprung up which harness the power of machine learning.

However, one challenge developers always faced was how to create the models? Luckily, Apple solved our question last winter when they announced the acquisition on Turi Create from GraphLab. Turi Create is Apple’s tool which can help developers simplify the creation of their own custom models. …


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Just as Apple does a lot for its developer community, another company which goes to great lengths to create amazing tools and services for its developers is Google. In recent years, Google has released and improved its services such as Google Cloud, Firebase, TensorFlow, etc. to give more power to both iOS and Android developers.

This year at Google I/O 2018, Google released a brand new toolkit called ML Kit for its developers. …


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At WWDC 2018, Apple introduced a brand new framework for iOS developers called Natural Language. Simply put, this framework gives apps the ability to analyze natural language text and understand parts of it. Natural Language can perform a variety of tasks on a block of text by assigning tag schemes to the text. What does this mean?

We can think of tag schemes as the tasks we ask a tagger to apply to the text. Here are some common tag schemes you’ll see.

  • Token Type: Classifies each character as either a word, punctuation mark, or whitespace.
  • Language: Determines the token’s dominant…

About

Sai Kambampati

19 y/o • Web/App Developer/Designer • WWDC ’17 ’18 Scholar • UCSC Comp Sci & Cog Sci • Author for AppCoda and Fritz AI

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