# Neural Networks Published articles for Neural Networks. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Quiz: Python AI: How to Build a Neural Network & Make Predictions DevFeed: [Quiz: Python AI: How to Build a Neural Network & Make Predictions]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-03T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Python](), [Neural Network](), [Artificial Intelligence](), [math](), [dataset]() Tags: [ai](), [math](), [neural-networks](), [predictions](), [python](), [train](), [training]() ### AI overview An interactive 13-question quiz testing understanding of how to build a neural network and make predictions with Python AI. It covers input vectors, layers, weights, bias, dot products, sigmoid activation, mean squared error, and backpropagation. ### Source excerpt Check your grasp of how neural networks make predictions in Python, from dot products and activation functions to gradient descent and backpropagation. ## Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino DevFeed: [Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-08-27T13:18:53Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Artificial Intelligence](), [Computer vision](), [Neural Network](), [object-detection](), [C](), [datasets]() Tags: [ai](), [ai-powered-livestock-monitoring](), [arduino](), [computer-vision](), [data](), [livestock-monitoring](), [model-training](), [monitoring](), [neural-networks](), [uno-q](), [yolo]() ### AI overview An Arduino Blog article profiles four 17-year-olds in Italy developing an AI-powered livestock monitoring system with the UNO Q. The project uses computer vision, custom-trained neural networks, and diverse image data to identify animals, count them, and detect possible health problems in real time. ### Source excerpt Pietro Maria Piazza, Alessandro Nesci, Davide Santucci, and Matteo Angiolillo are not waiting to finish school before starting to build something real. Based in Forlì, Italy, the four friends behind Blue Proton Initiative strive to develop an AI-powered livestock monitoring system designed to help farmers identify individual animals and detect early signs of health problems [...] The post Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino appeared first on Arduino Blog. ## How Keras 3 Helped Modernise Expedia Group's Lodging Ranking Stack DevFeed: [How Keras 3 Helped Modernise Expedia Group's Lodging Ranking Stack]() Original publisher: [Read original article]() Author: Conor Worthington Published: 2026-08-11T11:01:02Z Content type: article Language: en Sources: [Expedia]() Topics: [Keras](), [recommendation systems](), [Latency](), [Inference Performance](), [Machine learning]() Tags: [data-science](), [features](), [framework](), [improvements](), [inference](), [keras](), [latency](), [llms](), [machine-learning](), [mixture-of-experts](), [model-training](), [neural-networks](), [ranking](), [recommendation-system]() ### AI overview Expedia Group describes modernising its lodging-ranking stack around Keras 3. The changes included rewriting parts of its pipelines, making model training 30% faster and reducing P99 inference latency by two-thirds. The article discusses limitations of the previous Keras 2, eager-training and combined-serving setup, along with the use of newer APIs and accelerator-oriented optimisations. ### Source excerpt Expedia Group Technology -- DataWhat happened when we treated a framework migration as an architecture modernisation -- and cut P99 inference latency by two-thirdsSt Paul's and millennium bridge, London Expedia Group™ has always been a market leader in providing personalised search experiences for travellers. As our ranking models evolved, we saw an opportunity not just to migrate to Keras 3, but to modernise the broader stack around it so we can better serve travellers. This led us to rewrite key parts of our pipelines that made model training 30% faster and cut P99 inference latency by two-thirds. Our main focus in this blog is to discuss our improvements to lodging ranking -- our service which handles users' search requests and returns a personalised property ranking on top of a lightweight candidate generator. This service is frequently retrained to improve customer experience as we get new signals over time. Example of personalised lodging ranking on Expedia search result page for a London search As such, our velocity to make changes needs to be fast, but more importantly we need to be able to build models which are state-of-the-art, enabling customers to easily find and book the most relevant property for their trip. The problem with Keras 2, eager training and combined serving The challenge was not that the old stack was broken. It was that it had gradually become a limiting factor. On the training side, we were carrying dependencies on older components and missing out on modern Keras APIs, new optimisers, new layers and cleaner support for accelerator-oriented training. On the serving side, we had a path that was simple and operationally familiar, but not especially well suited to aggressively optimised inference for heavier ranking architectures. This blockage on utilising new layers and optimisers has become more pronounced as LLMs see blistering amounts of innovation. Naturally, these new features are only available in Keras 3 or as separate backends from ou ## 🗓 This Week In AI Research (17-24 July 26) DevFeed: [🗓 This Week In AI Research (17-24 July 26)]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-07-30T10:44:06Z Content type: article Language: en Sources: [Into AI]() Topics: [AI Research](), [Artificial Intelligence](), [releases](), [Large Language Model](), [Deep neural networks](), [parquet]() Tags: [ai](), [ai-research](), [anthropic](), [claude](), [compression](), [llms](), [neural-networks](), [releases]() ### AI overview A weekly roundup of AI research papers and releases, including Claude Opus 5, the HOPE framework for analyzing knowledge in deep neural networks through compression, and research on how large language models track evolving user intent across conversations. ### Source excerpt The top 10 AI research papers and releases this week (Claude Opus 5, Laguna S 2.1, Loopie, Nanbeige 4.2, Fugu-Cyber, and more) ## Embodied cognition and agentic AI DevFeed: [Embodied cognition and agentic AI]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-05-28T23:04:35Z Content type: opinion Language: en Sources: [Daniel Lemire]() Topics: [Machine Intelligence](), [Artificial Intelligence](), [ChatGPT](), [Large Language Model](), [Tool](), [Deep neural networks]() Tags: [agentic-ai](), [ai](), [chatgpt](), [language-models](), [neural-networks](), [tools]() ### AI overview This opinion argues that intelligence is embodied in a person's environment, language, hands, and tools rather than located only in the brain. It connects this view to ChatGPT's chat interface and the emergence of agentic AI, although the supplied text ends before explaining the next step. ### Source excerpt Where is your intelligence located? In your brain? It is a simplistic answer. A better model is that your intelligence is embodied. Consider a cook working at an expensive restaurant. He has all his favorite knives and cooking instructions, placed exactly where he wants them. His kitchen is part of his intelligence, of his skills. ... Continue reading Embodied cognition and agentic AI ## Neural Networks, As Simple As They Can Get DevFeed: [Neural Networks, As Simple As They Can Get]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-05-13T14:43:34Z Content type: tutorial Language: en Sources: [Dr. Ashish Bamania]() Topics: [Deep neural networks](), [Artificial Intelligence]() Tags: [artificial-intelligence](), [beginner](), [coding](), [data-science](), [machine-learning](), [neural](), [neural-networks](), [programming](), [technology]() ### AI overview A beginner-friendly lesson explaining what neural networks are, how neurons or perceptrons connect into networks, and how these networks are trained for tasks such as text generation and object recognition. ### Source excerpt A beginner's lesson on what Neural networks are and how they are trained to do amazing things. Continue reading on Level Up Coding " ## Coding Challenge #119 - AI Pong Player DevFeed: [Coding Challenge #119 - AI Pong Player]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-05-09T08:01:53Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [Code Challenge](), [Reinforcement learning](), [coding](), [Deep neural networks](), [NumPy]() Tags: [ai](), [coding](), [games](), [neural-networks](), [python](), [reinforcement-learning]() ### AI overview A coding challenge guides readers through building a reinforcement learning agent that learns to play Atari Pong from raw screen pixels. It introduces policy gradients, the REINFORCE algorithm, returns, stochastic policies, and variance reduction, with Python and NumPy as prerequisites. ### Source excerpt This challenge is to build your own AI pong player with reinforcement learning. ## Neural Networks Explained In Plain English DevFeed: [Neural Networks Explained In Plain English]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-04-21T04:01:25Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Neural Network](), [AI Engineering](), [Machine learning]() Tags: [ai-engineering](), [applications](), [concepts](), [function](), [mlp](), [neural-networks](), [neuron](), [understand]() ### AI overview A plain-English introduction to neural networks. It explains neurons or perceptrons, activation functions, weights and biases, and how stacking neurons into dense layers creates a multilayer perceptron (MLP) architecture used in AI systems. ### Source excerpt All popular AI systems that you see today are made up of basic computing units called Neurons or Perceptrons. These connect to form a network called a Neural network that performs various complex computations. ## A chat with Byron Cook on automated reasoning and trust in AI systems DevFeed: [A chat with Byron Cook on automated reasoning and trust in AI systems]() Original publisher: [Read original article]() Author: werner@allthingsdistributed.com (Dr. Werner Vogels) Published: 2026-02-17T14:00:00Z Content type: article Language: en Sources: [All Things Distributed]() Topics: [Automated reasoning](), [Artificial Intelligence](), [AI Agent](), [Lean](), [large-language-models](), [Authorization](), [Architecture & Design]() Tags: [agentic-ai](), [ai](), [ai-agent](), [authorization](), [automated-reasoning](), [cryptographic](), [deployment](), [financial](), [generate](), [language-models](), [large-language-models](), [neural-networks](), [posts](), [reasoning](), [regulatory](), [safety](), [security](), [security-vulnerabilities]() ### AI overview An interview with Byron Cook examines how automated reasoning can establish trust in AI systems. It discusses applying formal verification techniques to agentic systems, generated code, authorization, cryptographic implementations, and virtualization, with neurosymbolic AI presented as a way to combine neural networks with mathematically rigorous symbolic procedures. ### Source excerpt Over the past decade, Byron's team has proven the correctness of our authorization engine, our cryptographic implementations, and our virtualization layer. Now they're taking those same techniques and applying them to agentic systems. ## Titans + MIRAS: Helping AI have long-term memory DevFeed: [Titans + MIRAS: Helping AI have long-term memory]() Original publisher: [Read original article]() Published: 2025-12-04T19:26:09Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Generative AI](), [Transformer architecture](), [Google](), [Mamba](), [Neural Network](), [Compression]() Tags: [ai](), [architecture](), [compression](), [context](), [generative-ai](), [machine-intelligence](), [mamba](), [natural-language-processing](), [neural-networks](), [offline](), [real-time](), [recurrent]() ### AI overview Google Research introduces Titans and the MIRAS framework, which combine recurrent-model speed with transformer-like accuracy for handling very long contexts. The approach uses test-time memorization and an adaptive neural long-term memory that updates as data streams in, without offline retraining. ### Source excerpt Generative AI ## Understanding neural networks through sparse circuits DevFeed: [Understanding neural networks through sparse circuits]() Original publisher: [Read original article]() Published: 2025-11-13T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Deep neural networks](), [Artificial Intelligence](), [OpenAI](), [Chain-of-thought]() Tags: [ai](), [analysis](), [chain-of-thought](), [models](), [neural-networks](), [openai](), [reasoning](), [research](), [safety](), [training]() ### AI overview OpenAI explores mechanistic interpretability through sparse circuits: training neural networks to use simpler, more traceable computations. The approach aims to make model behavior easier to understand and support safer, more reliable AI systems. ### Source excerpt OpenAI is exploring mechanistic interpretability to understand how neural networks reason. Our new sparse model approach could make AI systems more transparent and support safer, more reliable behavior. ## Four ways to run ONNX models on a GPU with CUDA in Clojure DevFeed: [Four ways to run ONNX models on a GPU with CUDA in Clojure]() Original publisher: [Read original article]() Published: 2025-11-09T17:49:00Z Content type: tutorial Language: en Sources: [Dragan Djuric]() Topics: [CUDA](), [GPU](), [onnx](), [Clojure](), [Artificial Intelligence](), [Deep neural networks]() Tags: [ai](), [clojure](), [code](), [cuda](), [deep](), [diamond](), [examples](), [gpu](), [image-recognition](), [model](), [neural-networks](), [onnx](), [tensors]() ### AI overview A tutorial presents four ways to run ONNX models on a GPU with CUDA using Clojure libraries including Diamond ONNX RT, Deep Diamond, and ClojureCUDA. It covers GPU tensor backends and a configuration that keeps input and output tensors in main memory while executing the model on the GPU. ### Source excerpt Two weeks ago, I announced a new Clojure ML library, Diamond ONNX RT, which integrates ONNX Runtime into Deep Diamond. In that post, we explored the classic Hello World example of Neural Networks, MNIST handwritten image recognition, step-by-step. We run that example on the CPU, from main memory. The next logical step is to execute this stuff on the GPU. You'll see that with a little help of ClojureCUDA and Deep Diamond built-in CUDA machinery, this is both easy and simple, requiring almost no effort from a curious Clojure programmer. But don't just trust me; let's fire up your REPL, and we can continue together. Here's how you can evaluate this directly in your REPL (you can use the Hello World that is provided in the ./examples sub-folder of Diamond ONNX RT as a springboard). Require Diamond's namespaces First things first, we refer functions that we're going to use. (require '[uncomplicate.commons.core :refer [with-release]] '[uncomplicate.neanderthal.core :refer [transfer! iamax native]] '[uncomplicate.diamond [tensor :refer [tensor with-diamond]] [dnn :refer [network]] [onnxrt :refer [onnx]]] '[uncomplicate.diamond.internal.dnnl.factory :refer [dnnl-factory]] '[uncomplicate.diamond.internal.cudnn.factory :refer [cudnn-factory]] '[hello-world.native :refer [input-desc input-tz mnist-onnx]]) None of the following ways to run CUDA models has preference, you use the one that best suits your needs. Way one One of the ways to run ONNX models on your GPU is to simply use Deep Diamond's cuDNN factory as the backend for your tensors. Then, the machinery recognizes what you need and proceeds doing everything on the GPU, using the right stream for tensors, Deep Diamond operations, and ONNX Runtime operations. This looks exactly the same as any other Deep Diamond example from this blog or the DLFP book. (with-diamond cudnn-factory [] (with-release [cuda-input-tz (tensor input-desc) mnist (network cuda-input-tz [mnist-onnx]) classify! (mnist cuda-input-tz)] (transfer! input ## Using AI to identify genetic variants in tumors with DeepSomatic DevFeed: [Using AI to identify genetic variants in tumors with DeepSomatic]() Original publisher: [Read original article]() Published: 2025-10-16T16:33:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Google](), [Deep neural networks](), [datasets](), [Machine Learning & Artificial Intelligence]() Tags: [2025](), [ai](), [general-science](), [google](), [health-bioscience](), [machine-learning](), [neural-networks](), [open-source-models-datasets](), [research](), [training]() ### AI overview Google Research presents DeepSomatic, an AI-powered tool that uses convolutional neural networks to identify cancer-related genetic variants in tumor sequencing data. The tool supports major sequencing platforms and sample-processing methods, and its software and training dataset are openly available to researchers. ### Source excerpt General Science ## How AI Medical Imaging Is Powering Precision Healthcare DevFeed: [How AI Medical Imaging Is Powering Precision Healthcare]() Original publisher: [Read original article]() Author: MARTIN ELIAS COSTA, AI ENGINEER @ TOPTAL Published: 2025-07-18T05:00:00Z Content type: article Language: en Sources: [Toptal Blog]() Topics: [Artificial Intelligence](), [Medical imaging](), [Computer vision](), [Deep neural networks](), [data](), [Development]() Tags: [ai](), [ai-engineer](), [artificial-intelligence](), [computer-vision](), [data](), [development](), [diagnostics](), [healthcare](), [images](), [machine-learning](), [medical-imaging](), [neural-networks]() ### AI overview This article explains how artificial intelligence is transforming medical imaging from image acquisition through diagnosis. It focuses on computer vision, neural networks, medical imaging data, and GPUs, and describes an AI system that analyzes brain MRI scans, identifies demyelinating lesions, measures brain-region volumes, classifies atrophy patterns, and integrates results into electronic health records. ### Source excerpt Artificial intelligence is revolutionizing how medical images are acquired, analyzed, and interpreted. The transformation ushers in a new era of data-driven diagnostics and faster, more personalized patient care. ## Touchpad Digit Recognition Based on ESP-DL DevFeed: [Touchpad Digit Recognition Based on ESP-DL]() Original publisher: [Read original article]() Author: John Lee Published: 2025-06-18T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32-S3](), [ESP32-P4](), [Embedded Systems](), [Deep learning](), [Jetson](), [Artificial Intelligence](), [quantization](), [Inference](), [Code]() Tags: [ai](), [applications](), [blog](), [c-plus-plus](), [deep-learning](), [devices](), [edge-computing](), [embedded-systems](), [esp-dl](), [esp32](), [esp32-p4](), [esp32-s3](), [inference](), [neural-networks](), [quantization](), [recognition]() ### AI overview This tutorial demonstrates touchpad-based digit recognition on ESP32-S3 and ESP32-P4 using ESP-DL. It covers touch-data collection and preprocessing, lightweight CNN design, model training and evaluation, quantization, and C++ implementation for model loading and inference. ### Source excerpt This article demonstrates how to implement a touchpad-based digit recognition system using ESP-DL on ESP32 series chips. It covers the complete workflow from data collection and preprocessing to model training, quantization, and deployment, showcasing ESP-DL's capabilities in edge AI applications. ## Visualize and understand GPU memory in PyTorch DevFeed: [Visualize and understand GPU memory in PyTorch]() Original publisher: [Read original article]() Author: Quentin Gallouédec Published: 2024-12-24T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [PyTorch](), [GPU](), [Deep neural networks]() Tags: [code](), [gpu](), [graph](), [memory](), [neural-networks](), [pytorch](), [tensors](), [train](), [training](), [tutorial]() ### AI overview A tutorial on visualizing and understanding GPU memory usage in PyTorch during training. It explains how model creation, input and output tensors, activations, forward passes, backpropagation, and variable reassignment affect memory, and discusses estimating requirements and optimizing usage. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Visualizing piecewise linear neural networks DevFeed: [Visualizing piecewise linear neural networks]() Original publisher: [Read original article]() Author: Ricson Cheng Published: 2024-07-22T00:00:00Z Content type: article Language: en Sources: [Jane Street]() Topics: [Deep neural networks](), [Algorithm]() Tags: [algorithm](), [neural](), [neural-networks]() ### AI overview The article explains how piecewise-linearity in ReLU neural networks can be described and visualized. It examines activation patterns, polygonal regions, polyhedral complexes, and how adding layers changes these structures. ### Source excerpt Neural networks are often thought of as opaque, black-box function approximators, but theoretical tools let us describe and visualize their behavior. In particular, let's study piecewise-linearity, a property many neural networks share. This property has been studied before, but we'll try to visualize it in more detail than has been previously done. ## AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks DevFeed: [AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-28T20:53:00Z Content type: article Language: en Sources: [Google Research]() Topics: [Time Series](), [Google](), [Open Source]() Tags: [data](), [forecasting](), [google](), [kernels](), [machine-learning](), [model](), [neural-networks](), [open-source](), [patterns](), [research](), [statistics](), [time-series]() ### AI overview Google Research introduces AutoBNN, an open-source JAX package for automatically discovering interpretable probabilistic time series forecasting models. It combines learned compositional Gaussian process kernels with neural-network scalability and provides uncertainty estimates for large datasets. ### Source excerpt Posted by Urs Köster, Software Engineer, Google Research Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about the data's patterns (prior probability), collecting evidence (e.g., new time series data), and continuously updating that assumption to form a posterior probability distribution. Traditional Bayesian approaches like Gaussian processes (GPs) and Structural Time Series are extensively used for modeling time series data, e.g., the commonly used Mauna Loa CO2 dataset. However, they often rely on domain experts to painstakingly select appropriate model components and may be computationally expensive. Alternatives such as neural networks lack interpretability, making it difficult to understand how they generate forecasts, and don't produce reliable confidence intervals. To that end, we introduce AutoBNN, a new open-source package written in JAX. AutoBNN automates the discovery of interpretable time series forecasting models, provides high-quality uncertainty estimates, and scales effectively for use on large datasets. We describe how AutoBNN combines the interpretability of traditional probabilistic approaches with the scalability and flexibility of neural networks. AutoBNN AutoBNN is based on a line of research that over the past decade has yielded improved predictive accuracy by modeling time series using GPs with learned kernel structures. The kernel function of a GP encodes assumptions about the function being modeled, such as the presence of trends, periodicity or noise. With learned GP kernels, the kernel function is defined compositionally: it is either a base kernel (such as Linear, Quadratic, Periodic, Matérn or ExponentiatedQuadratic) or a composite that combines two or more kernel functions using operators such as Addition, Multiplication, or ChangePoint. This compositional kernel structure serves two related purposes. First, it is simple e ## Graph neural networks in TensorFlow DevFeed: [Graph neural networks in TensorFlow]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-02-06T19:17:00Z Content type: release Language: en Sources: [Google Research]() Topics: [Graphs](), [Tensorflow](), [Library](), [Google](), [Neural Network]() Tags: [google](), [graph](), [graph-mining](), [graphs](), [library](), [machine-learning](), [neural-networks](), [release](), [tensorflow]() ### AI overview Google Research announces TensorFlow GNN 1.0, a production-tested library for building and training graph neural networks at large scales. The library supports heterogeneous graphs, TensorFlow modeling and training, and extracting input graphs from large data stores. ### Source excerpt Posted by Dustin Zelle, Software Engineer, Google Research, and Arno Eigenwillig, Software Engineer, CoreML Objects and their relationships are ubiquitous in the world around us, and relationships can be as important to understanding an object as its own attributes viewed in isolation -- take for example transportation networks, production networks, knowledge graphs, or social networks. Discrete mathematics and computer science have a long history of formalizing such networks as graphs, consisting of nodes connected by edges in various irregular ways. Yet most machine learning (ML) algorithms allow only for regular and uniform relations between input objects, such as a grid of pixels, a sequence of words, or no relation at all. Graph neural networks, or GNNs for short, have emerged as a powerful technique to leverage both the graph's connectivity (as in the older algorithms DeepWalk and Node2Vec) and the input features on the various nodes and edges. GNNs can make predictions for graphs as a whole (Does this molecule react in a certain way?), for individual nodes (What's the topic of this document, given its citations?) or for potential edges (Is this product likely to be purchased together with that product?). Apart from making predictions about graphs, GNNs are a powerful tool used to bridge the chasm to more typical neural network use cases. They encode a graph's discrete, relational information in a continuous way so that it can be included naturally in another deep learning system. We are excited to announce the release of TensorFlow GNN 1.0 (TF-GNN), a production-tested library for building GNNs at large scales. It supports both modeling and training in TensorFlow as well as the extraction of input graphs from huge data stores. TF-GNN is built from the ground up for heterogeneous graphs, where types of objects and relations are represented by distinct sets of nodes and edges. Real-world objects and their relations occur in distinct types, and TF-GNN's heterogen ## How to use AI for software development and cybersecurity DevFeed: [How to use AI for software development and cybersecurity]() Original publisher: [Read original article]() Author: Simon Maple Published: 2023-08-30T05:00:00Z Content type: tutorial Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI Development](), [Artificial Intelligence](), [Cybersecurity](), [Code](), [Machine learning](), [Natural language processing](), [Large Language Model](), [Neural Network]() Tags: [acquisition](), [ai](), [artificial-intelligence](), [blog](), [code](), [cybersecurity](), [developer](), [developers](), [development](), [devrel](), [how-to](), [intellij](), [large-language-model](), [machine-learning](), [natural-language-processing](), [neural-networks](), [security](), [snyk-code](), [software](), [software-development]() ### AI overview This article introduces AI-assisted development for developers and security practitioners. It explains how generative AI can help write, review, test, and document code while maintaining security, and defines foundational concepts including artificial intelligence, natural language processing, machine learning, neural networks, and large language models. ### Source excerpt AI-assisted development helps developers build software more efficiently, learn how to leverage AI to write, test and review your code whilst maintaining security. ## ESP32-S3 Edge-AI｜Human Activity Recognition Using Accelerometer Data and ESP-DL DevFeed: [ESP32-S3 Edge-AI｜Human Activity Recognition Using Accelerometer Data and ESP-DL]() Original publisher: [Read original article]() Author: John Lee Published: 2023-06-06T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32-S3](), [On-device AI](), [Deep neural networks](), [ESP-IDF](), [Espressif]() Tags: [ai](), [blog](), [edge-ai](), [edge-computing](), [esp](), [esp-dl](), [esp-idf](), [esp32-s3](), [espressif](), [neural-networks](), [recognition]() ### AI overview This tutorial explains how to read accelerometer data and deploy a deep-learning model with ESP-DL on an ESP32-S3 for human activity recognition. It covers prerequisites, ESP-IDF project structure, model definition, layer initialization, and layer construction. ### Source excerpt Edge computing is a distributed computing paradigm that brings computation and data storage closer to the device's location. Edge Artificial Intelligence (edge-AI) is an exciting development within edge computing because it allows traditional technologies to run more efficiently, with higher performance and less power. Trained neural networks are used to make inferences on small devices. ## Hello, Perceptron: An introduction to artificial neural networks DevFeed: [Hello, Perceptron: An introduction to artificial neural networks]() Original publisher: [Read original article]() Published: 2023-05-05T14:05:31Z Content type: tutorial Language: en Sources: [Matt Might]() Topics: [Neural Network](), [Artificial Intelligence](), [Generative AI](), [Algorithm](), [circuit]() Tags: [ai](), [algorithm](), [circuit](), [generative-ai](), [introduction](), [neural-networks]() ### AI overview This tutorial introduces artificial neural networks through Frank Rosenblatt's perceptron. It explains how a single perceptron can be trained to mimic the AND, OR, and NOT logical functions, then examines its inability to learn XOR as motivation for multilayer neural networks. ### Source excerpt Generative AI tools like ChatGPT and Midjournery are able to replicate (and often exceed) human-like performance on tasks like taking exams, generating text and making art. Even to seasoned programmers, their abilities can seem magical. But, obviously, there is no magic. These things are "just" artificial neural networks - circuits inspired by the architecture of biological brains. An AI-imagined image of a neural network (Midjourney) In fact, much like real brains, when broken down to their building blocks, these systems can seem "impossibly simple" relative to what they achieve. (Modern computing is also magical in that sense, in that all of what computers are able to do reduces to simple logical building blocks - gates that calculate basic operations with truth values, such as AND, OR and NOT.) The purpose of this article is to give programmers without much exposure to machine learning an understanding of the key building block powering generative AI: the artificial neuron. Toward that end, this article has three goals: to implement a perceptron - the simplest artificial neuron; to train perceptrons how to mimic AND, OR and NOT; and to describe the leap to full-fledged neural networks. Click here to read the rest of the article ## Hello Deep Learning: Linear combinations DevFeed: [Hello Deep Learning: Linear combinations]() Original publisher: [Read original article]() Published: 2023-03-30T10:00:01Z Content type: tutorial Language: en Sources: [Bert Hubert's writings]() Topics: [Neural Network](), [dataset](), [math](), [Matrix](), [PyTorch]() Tags: [dataset](), [deep-learning](), [math](), [matrices](), [neural-networks](), [pytorch](), [tutorial]() ### AI overview This tutorial chapter introduces neural networks through a handwritten-digit classification example using the EMNIST dataset. It explains the theory of linear combinations, matrices or tensors, and explicitly configured weights before training is introduced. ### Source excerpt This page is part of the Hello Deep Learning series of blog posts. You are very welcome to improve this page via GitHub! In this chapter we're going to build our first neural network and take it for a spin. Weirdly, this demonstration will likely simultaneously make you wonder "is this all??" and also impress you by what even this trivial stuff can do. The first part of this chapter covers the theory, and shows no code. ## A GPU-Free, From-Scratch Introduction to Modern Machine Learning DevFeed: [A GPU-Free, From-Scratch Introduction to Modern Machine Learning]() Original publisher: [Read original article]() Published: 2023-03-30T09:59:00Z Content type: tutorial Language: en Sources: [Bert Hubert's writings]() Topics: [Machine learning](), [Deep learning](), [Deep neural networks](), [Code](), [data augmentation](), [Transformers]() Tags: [code](), [data-augmentation](), [deep-learning](), [gpu](), [machine-learning](), [neural-networks](), [transformers](), [tutorial]() ### AI overview A GPU-free tutorial that explains modern machine learning and deep learning from the ground up, developing neural networks and a practical demo from scratch. ### Source excerpt A from scratch GPU-free introduction to modern machine learning. Many tutorials exist already of course, but this one aims to really explain what is going on, from the ground up. Also, we'll develop the demo until it is actually useful on real life data which you can supply yourself. Other documents start out from the (very impressive) PyTorch environment, or they attempt to math it up from first principles. Trying to understand deep learning via PyTorch is like trying to learn aerodynamics from flying an Airbus A380. [Next page]()