Through the first 3 parts of our federated learning (FL) demo project, we’ve set up a system wherein machine learning (ML) models is trained using FL. Put simply, a generic model is created at the server. The model is then sent to the clients for training, and then sent back to the server.
Check out the previous 3 parts in the project to get caught up:
Continue reading Federated Learning Demo in Python (Part 4): Working with Mobile Devices
GANs are one of the most interesting topics in machine learning today. They have been used in a number of problems (and not just to generate MNIST digits!) and performed very well in each case. A GAN (General Adversarial Network) consists of a generator and a discriminator, which compete against each other to produce mind-blowing results. Here, we’ll take a mathematical approach towards understanding the GAN and its loss functions. As the idea behind training a GAN comes from game theory, we’ll have a quick look at the Minimax Optimization Strategy too.
Continue reading Colorizing B/W Images With GANs in TensorFlow
In my previous article that examined classification with TensorFlow, I covered the basics details of how to perform linear classification with TensorFlow’s estimator API. You can read that blog post here:
For part two, I’m going to cover how we can tackle classification with a dense neural network. I’ll be using the same dataset and the same amount of input columns to train the model, but instead of using TensorFlow’s LinearClassifier, I’ll instead be using DNNClassifier. We’ll also compare the two methods.
Continue reading Classification with TensorFlow and Dense Neural Networks
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
