A Perceptron is a fundamental building block of a neural network. The flexibility and scalability of perceptron make it ubiquitous in building intelligent systems. Studies have shown the efficacy of a single neuron in making intelligent decisions. Here, we examined and compared two perceptrons with distinct mechanisms, and developed a quantum version of one of those perceptrons. As a part of this modeling, we implemented the quantum circuit for an artificial perception, generated a dataset, and simulated the training. Through these experiments, we show that there is an exponential growth advantage and test different qubit versions. Our findings show that this quantum model of an individual perceptron can be used as a pattern classifier. For the second type of model, we provide an understanding to design and simulate a spike-dependent quantum perceptron. Our code is available at \url{https://github.com/ashutosh1919/quantum-perceptron}

The Duke Robotics Club is proud to present our robot for the 2023 RoboSub Competition: Oogway. Oogway marks one of the largest design overhauls in club history. Beyond a revamped formfactor, some of Oogway's notable features include all-new computer vision software, advanced sonar integration, novel acoustics hardware processing, and upgraded stereoscopic cameras. Oogway was built on the principle of independent, well-integrated, and reliable subsystems. Individual components and subsystems were tested and designed separately. Oogway's most advanced capabilities are a result of the tight integration between these subsystems. Such examples include sonar-assisted computer vision algorithms and robot-agnostic controls configured in part through the robot's 3D model. The success of constructing and testing Oogway in under 2 year's time can be attributed to 20+ contributing club members, supporters within Duke's Pratt School of Engineering, and outside sponsors.

To illustrate everything, let us use a toy dataset with two real features x₁, x₂, and three classes c₁, c₂, c₃ in the following. Let us start with the class probability p(c), the probability that some class c is observed in the labeled dataset. The simplest way to estimate this is to just compute the relative frequencies of the classes and use them as the probabilities. We can use our dataset to see what this means exactly. There are 7 out of 20 points labeled class c₁ (blue) in the dataset, therefore we say p(c₁) 7/20.

Optimization is the process of finding the set of variables x that minimize or maximize an objective function f(x). Let us now introduce the steepest descent algorithm and implement it from scratch. Our goal is to solve the optimization problem and find the minimum [4.5, 2.3]. To solve the optimization problem minₓ f(x), we start by positioning ourselves in some point in the coordinate space. This approach requires a proper selection of the step size α and the search direction p.

There are many similarities between the Transformer encoder and decoder, such as in their implementation of multi-head attention, layer normalization and a fully connected feed-forward network as their final sub-layer. Having implemented the Transformer encoder, we will now proceed to apply our knowledge in implementing the Transformer decoder, as a further step towards implementing the complete Transformer model. Our end goal remains the application of the complete model to Natural Language Processing (NLP). In this tutorial, you will discover how to implement the Transformer decoder from scratch in TensorFlow and Keras. Implementing the Transformer Decoder From Scratch in TensorFlow and Keras Photo by François Kaiser, some rights reserved.

Warehouse management has changed significantly in the past decade. The COVID-19 pandemic, greater consumer access to technology, and wider demand for delivery mean that firms like Amazon have seen a $12 billion jump in profits compared to pre-pandemic estimates. But meeting the increased demand placed on warehouses isn't easy. The sheer volume and speed of orders require digitally-integrated solutions at every level. A generous helping of artificial intelligence is also needed to ensure that everyone's orders arrive on time and in their intended state.

Machine learning metrics for distributed, scalable PyTorch applications. TorchMetrics is a collection of 80 PyTorch metrics implementations and an easy-to-use API to create custom metrics. The module-based metrics contain internal metric states (similar to the parameters of the PyTorch module) that automate accumulation and synchronization across devices! This can be run on CPU, single GPU or multi-GPUs! Module metric usage remains the same when using multiple GPUs or multiple nodes.

There are lots of definitions of AI. According to the Merrian-Webster dictionary, Artificial Intelligence is a large area of computer science that simulates intelligent behavior in computers. Based on this, an algorithm implementation based on metaheuristic called Particle Swarm Optimization (originaly proposed to simulate birds searching for food, the movement of fishes' shoal, etc.) is able to simulate behaviors of swarms in order to optimize a numeric problem iteratively. It can be classified as a swarm intelligence algorithm like Ant Colony Algorithm, Artificial Bee Colony Algorithm and Bacterial Foraging, for example. Proposed in 1995 by J. Kennedy an R.Eberhart, the article "Particle Swarm Optimization" became very popular due his continue optimization process allowing variations to multi targets and more.

To get intimately familiar with the nuts and bolts of transformers I decided to implement the original architecture from Vaswani et al.'s "Attention is all you need" paper from scratch. I thought I knew everything there was to know, but to my own surprise, I encountered several unexpected implementation details that made me better understand how everything works under the hood. The goal of this post is not discuss the entire implementation -- there are plenty of great resources for that -- but to highlight seven things that I found particularly surprising or insightful, and that you might not know about. I will make this concrete by pointing to specific lines in my code using this hyperlink robot (try it!). The code should be easily understandable: it's well documented and automatically unit tested and type checked using Github Actions.

Cloud computing services have been dominated by the most popular and big tech companies in the world such as AWS, Microsoft Azure, Google GCP, and IBM. But every cloud service provider has some strengths and drawbacks that make it difficult for one cloud solution to meet all of an organization's needs. Implementing a multi-cloud strategy allows companies to have more flexibility to optimize costs, speed, and performance. In this article, you will learn what Multi-cloud strategy is, its pros and cons, and how it will reduce the cost to run your infrastructure and applications. Multi-cloud strategy refers to the use of more than one cloud service (multiple cloud services) from two or more vendors.