In this blog post, we’ll be learning how to automate the Chrome Dinosaur Game using neural networks with TensorFlow.js. If you haven’t played it before, it’s a side scrolling game available offline (for when Chrome or your Internet crashes) where you control a 2D dinosaur and have to jump and duck to avoid obstacles. Give it a shot here:
Continue reading Using TensorFlow.js to Automate the Chrome Dinosaur Game
When building machine learning models, we often tend to focus on the more glamorous aspects of machine learning, such as finding the best architecture and tuning hyperparameters, that we fail to recognize that our dataset is the lifeblood of our entire pipeline.
Feature engineering is one of the most important skills needed in data science and machine learning. It has a major influence on the performance of machine learning models and even the quality of insights derived during exploratory data analysis (EDA).
In this article, we’re going to learn some important techniques and tools that will help you properly extract, prepare, and engineer features from your dataset.
Continue reading A Practical Guide to Feature Engineering in Python
The Open Neural Network Exchange (ONNX) is an open standard for representing machine learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose combinations that are best for them. ONNX is developed and supported by a community of partners including AWS, Facebook OpenSource, Microsoft, AMD, IBM, Intel AI, etc.
Continue reading Building an image recognition React app using ONNX.js
Deep neural networks perform incredibly well on computer vision tasks such as classification, object detection, and segmentation, but what do they consider before performing these tasks, and what does it take to make these decisions?
Interpreting neural network decision-making is an ongoing area of research, and it’s quite an important concept to understand. Neural networks are used in the real-world, so we can’t treat them like black boxes—we need to learn what they interpret, how they interpret, and what information each layer/channel in a neural network has learned.
Continue reading Class activation maps: Visualizing neural network decision-making
At the turn of this new decade, if there’s one field that has the potential to completely change our way of interacting with smartphones, it’s augmented reality (AR Glasses: are you reading this?). Google has been pushing ahead with its own platform—ARCore—that enables developers to create AR experiences on Android, iOS, Unity, and more.
In this part of our tutorial series exploring Unity’s AR Foundation framework, we’ll learn how to detect planes and place objects on those planes. We will also learn how to use C# to create scripts and add programmable behavior to our app. If you have not read the introduction to this series, I suggest doing so, as this is a continuation from our overview.
Continue reading Augmented Reality Using Unity’s AR Foundation Framework
This tutorial is the seventh chapter of our implementation of an AirBnB clone in React Native. In the previous chapter, we successfully implemented animated checkmarks for our Login form. In case you need to get caught up, here are links to parts 1–6:
In part 7, we’re going to continue where we left off by implementing a “forgot password?” screen. The forgot password screen is available in most web and app systems in case a user forgets their login credentials. Here, the idea is to create a simple forgot password screen with an email input form. Then we’re going to use Firebase to handle the backend configuration.
Continue reading AirBnB Clone with React Native: “Forgot Password?” with Firebase
Most of the time, when we start working with machine learning, we tend to use models that are readily available online so that we don’t have to spend time and effort creating and training them on our own. But this comes at a cost, as these models don’t usually fit our requirements exactly as expected. Fortunately, Apple has provided Create ML to address those issues, and in this article we’ll learn how to use our own data and categories to train our models.
Continue reading Build iOS-ready machine learning models using Create ML
Phew, okay. After writing part 1 in this series looking deeper into the AI and machine learning landscape, I needed to take a deep breath.
I came across so many awesome companies, organizations, and tools for data labeling, generation, and preparation—it left me impressed but also with a key question in my journey: Now that we have all we need to prepare our datasets, what’s next?
Continue reading AI & Machine Learning Landscape (Part 2): Training platforms and tools
