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?
Continue reading Natural Language in iOS 12: Customizing tag schemes and named entity recognition
With recent advances in both Android device cameras and AI-accelerated chip technology, it’s no wonder that some of the most innovative, transformative use cases for machine learning on Android come in the form of computer vision.
From understanding scenes and creating artistic masterpieces to tracking human movement and even changing hair color, the promise of real-time computer vision on Android is real.
Continue reading Machine Learning on Android: Computer Vision
For this article, I want to discuss a couple topics in the iOS world I’ve always found interesting: memory management, heaps, and stacks. I’ve never really understood the difference between them or how heaps and stacks relate to storing memory in Swift.
So, I decided to do some of my own research and dig deep into memory management in Swift. I hope you’re looking forward to this topic as much as I am! Let’s dive right in.
So you’ve trained your Core ML model in Create ML or Turi Create. And now, you want to test it out on some real-world data. To make quick and dirty tests, you can leverage Swift Playgrounds and run Core ML models there. If you’re satisfied with the results, you can move over to Xcode project and run on-device or on a simulator.
Continue reading How to Run and Test Core ML Models in a Swift Playground
This post is a part of a series about feature engineering techniques for machine learning with python.
You can check out the rest of the articles:
Welcome back! In this post, we’re going to cover the different imputation techniques used when dealing with missing data. Additionally, we’ll also explore a few code snippets you can use directly in your machine learning and data science projects.
Continue reading Hands-on with Feature Engineering Techniques: Imputing Missing Values
In a few of my previous posts, you’ve built a React Native app using Firebase as the backend service provider for email authentication, storing a user’s data upon successful signup.
Let’s add another common yet useful and necessary feature in our current app structure: Forgot Password. This feature will require another screen in the current React Native app. To follow this tutorial, you can work through any of the previous posts if you’re a beginner in the React Native world:
Continue reading Implementing a ‘Forgot Password’ feature in React Native with Firebase
ARIMA stands for Auto Regressive Integrated Moving Average. It’s a statistical library used for analyzing and forecasting time series data. Models created using ARIMA library are more flexible than other statistical models like simple linear regression. Here’s a closer look at the terms that make up this acronym:
Continue reading Implementing ARIMA using Statsmodels and Python
Data visualization is the art of transforming information into a visual context, such as a map or graph. Through your journey in analyzing and preparing your data, you’ll often need to visually present your findings using some type of chart to make sense of the facts, numbers, and measurements.
After all, a picture is worth a thousand words (or in our case, a chart). However, to get the best use of a chart representation, you need to make sure you chose the perfect chart type to communicate your purpose and intentions. Choosing the perfect chart type for your goal can save you a lot of time and effort in order to better explain your data.
Continue reading How to choose the best chart type to present your dataset
Our experience of the world is multimodal — we see objects, hear sounds, feel the texture, smell odors and taste flavors and then come up to a decision. Multimodal learning suggests that when a number of our senses — visual, auditory, kinesthetic — are being engaged in the processing of information, we understand and remember more. By combining these modes, learners can combine information from different sources.
In machine learning, a hyperparameter is a configuration variable that’s external to the model and whose value is not estimated from the data given.
Hyperparameters are an essential part of the process of estimating model parameters and are often defined by the practitioner.
When a machine learning algorithm is used for a specific problem, such as using a grid search or a random search algorithm, then you’re actually tuning the hyperparameters of the model to discover the values that result in the most accurate predictions.
Continue reading Introduction to Learning Rates in Machine Learning
