# neural network Published articles for neural network. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Everything a Senior Engineer Needs to Know About What's Inside an LLM DevFeed: [Everything a Senior Engineer Needs to Know About What's Inside an LLM]() Original publisher: [Read original article]() Author: Sidwyn Koh Published: 2026-06-20T17:00:09Z Content type: tutorial Language: en Sources: [Path to Staff]() Topics: [Large Language Model](), [model architecture](), [Neural Network](), [Transformers]() Tags: [architecture](), [learning](), [llms](), [models](), [neural-network]() ### AI overview This tutorial explains the architecture and internal components of large language models, including the role of neural networks, recurrent neural networks, transformers, and diffusion models. It is part of a series covering AI systems in depth. ### Source excerpt Learn what AI models are made of ## CKKS -- Polynomials, the Canonical Embedding, and Encoding DevFeed: [CKKS -- Polynomials, the Canonical Embedding, and Encoding]() Original publisher: [Read original article]() Published: 2026-04-29T12:25:44Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [homomorphic encryption](), [Cryptography](), [Mathematics](), [Math and Logic]() Tags: [ckks](), [ckks-tutorial](), [cryptography](), [encoding](), [encryption](), [homomorphic-encryption](), [mathematics](), [neural-network](), [polynomial-ring](), [polynomials](), [programming](), [python](), [technical](), [tutorial]() ### AI overview This tutorial introduces the CKKS homomorphic encryption scheme and develops mathematical background on the polynomial ring used in its basic formulation and the canonical embedding used to encode cleartext messages as plaintexts. It also outlines CKKS's history, including its support for approximate arithmetic and later bootstrapping improvements. ### Source excerpt Table of Contents In this tutorial series, I will introduce the CKKS homomorphic encryption scheme from the ground up, in rather intricate detail. Each article in this series corresponds to a pull request on a GitHub repository. The code for this article is in this pull request. Follow along by cloning the repository and checking out the code at the relevant commit. This first article will cover some of the mathematical background necessary in the formulation of the CKKS encryption scheme, specifically the polynomial ring used in the most basic version of CKKS, and the canonical embedding used to encode cleartext messages as plaintexts. ## AI agent reliability requires recovery infrastructure for partial failures DevFeed: [AI agent reliability requires recovery infrastructure for partial failures]() Original publisher: [Read original article]() Author: Melanie Warrick Published: 2026-04-01T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [reliability](), [Resilience](), [agent observability](), [Machine learning](), [coding assistant](), [Neural Network](), [image recognition](), [Google](), [recommendation systems]() Tags: [agent](), [agents](), [ai](), [ai-agents](), [coding-assistant](), [google](), [image-recognition](), [neural-network](), [recommendation-systems](), [reliability](), [resilience](), [temporal-voices]() ### AI overview AI agents can reason through complex tasks and run long workflows, but failures during execution can compound when systems cannot checkpoint progress, recover from partial failures, or resume work. The article argues that dependable AI requires durable infrastructure and resilience in addition to better models, placing current agent reliability challenges in the context of earlier AI failures. ### Source excerpt Smart AI agents still fail mid-workflow. Learn why solving the AI reliability gap requires durable infrastructure, not just better models. ## Improving Embedding-Based Candidate Generation for Recommender Systems with a Two-Tower Model DevFeed: [Improving Embedding-Based Candidate Generation for Recommender Systems with a Two-Tower Model]() Original publisher: [Read original article]() Author: Ahmad Khan Published: 2026-03-18T21:04:58Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [recommendation systems](), [Machine learning](), [Embeddings](), [Neural Network](), [data](), [ordering]() Tags: [ai](), [data](), [embedding](), [machine-learning](), [model-training](), [neural-network](), [ordering](), [ranking](), [recommender-systems](), [self-supervised-learning](), [two-tower-model]() ### AI overview Glassdoor describes a two-tower neural network for candidate generation in its Community feed recommendation system. The model learns user and post embeddings from interaction data to retrieve potentially relevant posts before filtering, ranking, and ordering. ### Source excerpt Overview At Glassdoor (part of Indeed), millions of professionals come to learn about workplaces, ask career questions, and share experiences through our Community platform. When users open the Community Home Feed, they expect to see posts that are relevant to their interests, industry, and career stage. However, generating that feed is not a simple task. At any moment, thousands of new posts across hundreds of community feeds compete for attention. From discussions about interview experiences to workplace culture questions and industry advice, the platform contains a diverse and constantly evolving stream of content. The challenge is to quickly identify which of these posts are most relevant for each user. To address this problem, we built a multi-stage machine learning recommendation system that efficiently sources, filters, and ranks posts before they appear in the user's feed. In a previous post, Inside Glassdoor's Multi-Stage Recommendation System, we described the architecture of this system and how the different stages work together to deliver personalized content. Snapshot of the Glassdoor Community Home Feed At a high level, the system consists of four stages: Candidate Generation -- retrieves a large set of potentially relevant posts Filtering -- removes posts that are undesirable, duplicated, or already seen Ranking -- predicts which posts a user is most likely to engage with Ordering -- applies heuristics to maintain diversity and freshness in the feed Each stage progressively narrows the set of posts until only the most relevant posts remain. 4 Stage ML RecSys Design In this blog post, we focus on the Candidate Generation stage, which is responsible for retrieving an initial pool of potentially relevant posts from a very large pool of posts across many different feeds. For Candidate Generation, we developed a Two-Tower Neural Network Model that learns embeddings for users and posts based on real interaction data. In the rest of this blog post, we will walk ## 20 AI Concepts Explained in 20 Minutes DevFeed: [20 AI Concepts Explained in 20 Minutes]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-02-26T13:22:58Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Artificial Intelligence](), [Learning](), [Large Language Model](), [Neural Network]() Tags: [ai](), [concepts](), [language-models](), [learning](), [neural-network]() ### AI overview A beginner-friendly tutorial introducing foundational AI concepts, including neural networks, large language models, and transfer learning, with simplified explanations and examples. ### Source excerpt Learning AI can feel overwhelming. ## Generation configurations: temperature, top-k, top-p, and test time compute DevFeed: [Generation configurations: temperature, top-k, top-p, and test time compute]() Original publisher: [Read original article]() Author: Chip Huyen Published: 2024-01-16T00:00:00Z Content type: tutorial Language: en Sources: [Chip Huyen]() Topics: [Artificial Intelligence](), [Machine learning](), [Language models](), [Neural Network](), [Inference]() Tags: [ai](), [decoding](), [hallucinations](), [inference](), [ml](), [neural](), [neural-network](), [responses](), [token](), [tokens]() ### AI overview This tutorial explains why machine-learning models produce probabilistic responses and how sampling, or decoding, generates them. It covers sampling strategies such as temperature, top-k, and top-p, test-time compute through multiple outputs, and structured outputs. ### Source excerpt ML models are probabilistic. Imagine that you want to know what's the best cuisine in the world. If you ask someone this question twice, a minute apart, their answers both times should be the same. If you ask a model the same question twice, its answer can change. If the model thinks that Vietnamese cuisine has a 70% chance of being the best cuisine and Italian cuisine has a 30% chance, it'll answer "Vietnamese" 70% of the time, and "Italian" 30%. This probabilistic nature makes AI great for creative tasks. What is creativity but the ability to explore beyond the common possibilities, to think outside the box? However, this probabilistic nature also causes inconsistency and hallucinations. It's fatal for tasks that depend on factuality. Recently, I went over 3 months' worth of customer support requests of an AI startup I advise and found that ⅕ of the questions are because users don't understand or don't know how to work with this probabilistic nature. To understand why AI's responses are probabilistic, we need to understand how models generate responses, a process known as sampling (or decoding). This post consists of 3 parts. Sampling: sampling strategies and sampling variables including temperature, top-k, and top-p. Test time compute: increasing the compute allocated to inference, e.g. sampling multiple outputs, to help improve a model's performance. Structured outputs: how to get models to generate outputs in a certain format. Sampling Given an input, a neural network produces an output by first computing the probabilities of all possible values. For a classifier, possible values are the available classes. For example, if a model is trained to classify whether an email is spam, there are only two possible values: spam and not spam. The model computes the probability of each of these two values, say being spam is 90% and not spam is 10%. To generate the next token, a language model first computes the probability distribution over all tokens in the vocabulary. Fo ## Using Deep Learning to Recognize Handwritten Characters with OCR DevFeed: [Using Deep Learning to Recognize Handwritten Characters with OCR]() Original publisher: [Read original article]() Published: 2023-03-30T10:00:08Z Content type: tutorial Language: en Sources: [Bert Hubert's writings]() Topics: [Deep learning](), [Image processing](), [Neural Network](), [Training AI Models](), [datasets](), [Code]() Tags: [code](), [deep-learning](), [image-processing](), [neural-network](), [ocr](), [testing](), [training](), [training-data]() ### AI overview This tutorial builds and tests an OCR program that recognizes handwritten letters from a photo using a convolutional deep learning model. It shows how differences between real-world input and training data can cause recognition failures, including difficulty recognizing a handwritten "h". ### 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! The previous chapters have often mentioned the chasm between "deep learning models that work on my data" and "it actually works in the real world". It is perhaps for this reason that almost all demos and YouTube tutorials you find online never do any real world testing. ## Hello Deep Learning: Hyperparameters, inspection, parallelism, ADAM DevFeed: [Hello Deep Learning: Hyperparameters, inspection, parallelism, ADAM]() Original publisher: [Read original article]() Published: 2023-03-30T10:00:06Z Content type: tutorial Language: en Sources: [Bert Hubert's writings]() Topics: [Deep learning](), [Neural Network](), [parallel](), [GPU](), [cpu](), [data]() Tags: [cpu](), [deep-learning](), [faster](), [gpu](), [matrices](), [neural-network](), [parallelism](), [tensors]() ### AI overview A Hello Deep Learning tutorial explains how neural networks can train faster through parallel computation, including batched tensor operations, GPU capacity, CPU SIMD instructions, multiple CPU cores, and distributed processing. It also begins discussing network parameter inspection and matrix multiplication. ### 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 the previous chapter we successfully trained a network to recognize handwritten letters, but it took an awfully long time. This is not just inconvenient: networks that take too long to train mean we can experiment less. Some things really are out of reach if each iteration takes 24 hours, instead of 15 minutes. ## Hello Deep Learning: Convolutional networks DevFeed: [Hello Deep Learning: Convolutional networks]() Original publisher: [Read original article]() Published: 2023-03-30T10:00:05Z Content type: tutorial Language: en Sources: [Bert Hubert's writings]() Topics: [Neural Network](), [Deep learning](), [machine learning overfitting](), [generalization in machine learning]() Tags: [deep-learning](), [machine-learning](), [neural-network](), [tutorial](), [validation]() ### AI overview This tutorial chapter introduces convolutional neural networks as a way to recognize shapes and use the relative positions of pixels. It explains how convolutional operations, max-pooling, and GELU can improve robustness beyond a basic image-processing network, with examples involving handwritten digits and letters. ### 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 the previous chapter we taught a network of linear combinations and 'rectified linear units' to recognize handwritten digits reasonably successfully. But we already noted that the network would be sensitive to the exact location of pixels, and that it does not in any meaningful way "know" what a 7 looks like. ## Hello Deep Learning: Reading handwritten digits DevFeed: [Hello Deep Learning: Reading handwritten digits]() Original publisher: [Read original article]() Published: 2023-03-30T10:00:04Z Content type: tutorial Language: en Sources: [Bert Hubert's writings]() Topics: [Deep learning](), [Neural Network](), [Deep neural networks](), [Image]() Tags: [blog-posts](), [deep-learning](), [images](), [layer](), [matrices](), [neural-network](), [pixels](), [spatial]() ### AI overview A tutorial in the Hello Deep Learning series explains how to build a neural network that recognizes all ten handwritten digits. It describes flattening 28x28 images, applying matrix multiplications and ReLU layers, and selecting the highest-scoring output. ### 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 the previous chapter we described how automatic differentiation of the result of neural networks works. In the first and second chapters we designed and trained a one-layer neural network that could distinguish images of the digit 3 and the digit 7, and the network did so very well. ## Hello Deep Learning: Automatic differentiation, autograd DevFeed: [Hello Deep Learning: Automatic differentiation, autograd]() Original publisher: [Read original article]() Published: 2023-03-30T10:00:03Z Content type: tutorial Language: en Sources: [Bert Hubert's writings]() Topics: [Deep learning](), [Neural Network](), [Algorithm](), [Code]() Tags: [algorithm](), [deep-learning](), [matrices](), [model](), [neural-network](), [pixels](), [train]() ### AI overview This tutorial chapter explains how automatic differentiation and gradient descent train a five-layer neural network to recognize all ten handwritten digits. It introduces the network's layers, parameters, weights, and biases, then connects parameter updates to derivatives of the error. ### 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 the previous chapter we configured a neural network and made it learn to distinguish between the digits 3 and 7. The learning turned out to consist of "twisting the knobs in the right direction". Although simplistic, the results were pretty impressive. But, you might still be a bit underwhelmed - the network only distinguished between two digits. ## How a Neural Network Learns to Classify Images of 3s and 7s DevFeed: [How a Neural Network Learns to Classify Images of 3s and 7s]() Original publisher: [Read original article]() Published: 2023-03-30T10:00:02Z Content type: tutorial Language: en Sources: [Bert Hubert's writings]() Topics: [Learning](), [Neural Network](), [Deep learning](), [Machine learning]() Tags: [code](), [data](), [deep-learning](), [layer](), [machine-learning](), [neural-network](), [pixels]() ### AI overview This tutorial explains how a simple neural network can learn to classify images of 3s and 7s. It introduces random parameter initialization, the role of the weights matrix, and adjusting weights based on image pixels to improve the classification score. ### 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 take the neural network we made earlier, but actually make it do some learning itself. And, oddly enough, this demonstration will again likely simultaneously make you wonder "is this all??" and also impress you by what even this trivial stuff can do. ## Using a Neural Network for sending memes to my girlfriend DevFeed: [Using a Neural Network for sending memes to my girlfriend]() Original publisher: [Read original article]() Published: 2021-05-20T00:00:00Z Content type: tutorial Language: en Sources: [Mutt0-ds Notes]() Topics: [Neural Network](), [image recognition](), [ImageNet](), [Reddit](), [API](), [Library](), [email]() Tags: [api](), [cats](), [email](), [image-recognition](), [images](), [library](), [model](), [neural](), [neural-network](), [password](), [reddit]() ### AI overview This tutorial describes a project that uses Reddit's API and the praw library to download wholesome memes, applies a ResNet50 image-recognition model to identify animal content, and emails selected memes to a recipient. ### Source excerpt Well, my GF's tastes in memes are simple: she loves wholesome memes and, most importantly, she loves animals. That's why I created a fun... ## What is Machine Learning? DevFeed: [What is Machine Learning?]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-01-14T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Machine learning](), [Algorithms](), [data](), [Training AI Models]() Tags: [algorithm](), [data](), [explainability](), [machine-learning](), [neural-network](), [training-data]() ### AI overview This tutorial defines machine learning as a technology and as the study of algorithms and statistical models that learn from labeled training data to perform tasks or make predictions without explicit instructions. It explains the roles of corpora, labeling, training data, data-driven pattern recognition, and neural networks. ### Source excerpt I have written a number of posts about different aspects of Machine Learning but, it occurs to me, not a single one that actually says "Machine Learning is xyz and here are the definitions" so here goes. Machine Learning is a technology that allows a computer ("the Machine") to take a "corpus" of "labeled" "training data" and then "learn" how to do something that the training data teaches the computer. So the above is my definition but let's look at Wikipedia: Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.Wikipedia And, while it is a simplification, the underlying thing idea driving machine learning is that of data driven pattern recognition. Basically the Machine Learning process amounts to: Take a bunch of data and construct a set of examples that illustrate what you want the machine to learn. This is called Training Data and the overall collection amounts to a Corpus. Add metadata to these examples in a way that represent what you want the machine to learn. Examples could be blocks of text, images, video files or whatever. I personally work mostly in text but I have machine learning buddies who do things learning on images of cancerous masses. This is called Labeling. Labeling might consist of different things like adding Yes / No / Abstain values to a tweet or even extracting information from a recipe like the ingredie ## Adding Explainability to Machine Learning Routines via Runtime Execution of Labeling Functions DevFeed: [Adding Explainability to Machine Learning Routines via Runtime Execution of Labeling Functions]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-01-10T00:00:00Z Content type: article Language: en Sources: [Scott Johnson]() Topics: [Machine Learning & Artificial Intelligence](), [Neural Network](), [data](), [Kafka]() Tags: [data](), [explainability](), [kafka](), [machine-learning](), [neural-network](), [python](), [snorkel](), [supervised-learning](), [training]() ### AI overview The article discusses the black-box nature of machine-learning outputs and introduces runtime execution of labeling functions as an approach to adding explainability. It also explains basic concepts including supervised learning, labeled datasets, neural networks, training programs, and models. ### Source excerpt I am spending much of my time these days in the world of machine learning and I'm finding that it is profoundly interesting. One of the most interesting aspects of machine learning, for me, is the black box nature of machine learning's output. What we typically get from a machine learning model what the model thinks but not how it arrived at that result. And this lack of explainability is true for machine learning whether the underlying technology is Tensor Flow for image segmentation or Bert / Roberta / Albert for text processing. Note: Bert / Roberta / Albert are machine learning tools that generate linguistically savvy models that understand English language content. As a side note, given how hard it is for most people to understand why they think what they think, is the black box nature of machine learning actually all that surprising? Machine Learning Basics for the Newbie The world of machine learning, which appears intimidating as hell to the newbie, actually isn't that hard but the terms can be terrifying. Someone recently asked me what I was doing and this was my answer: "I am working on a weekly supervised learning model for an anti Semitic classifier that will be used in a Kafka data pipeline. " Here are the very basics of machine learning: Machine Learning is just that - a way for a machine to learn from data. The underlying idea of machine learning is pretty much just pattern recognition based on data fed through a neural network. To make machine learning work you "label" a data set which means you tell the machine that for Example X, the answer you want is Foo and for Example Y, the answer is Bar. And you do this over and over and over (sometimes you have to label 50,000 examples; that's what my current project uses). You give a program, generally called a training program, this labeled data and then it thinks on this for a while and creates what is called a model. The model is a trained neural network which understands how to process data like what yo ## How to Create a Malware Detection System With Machine Learning DevFeed: [How to Create a Malware Detection System With Machine Learning]() Original publisher: [Read original article]() Author: Simone Margaritelli Published: 2019-05-22T21:59:13Z Content type: tutorial Language: en Sources: [evilsocket]() Topics: [Malware](), [Machine Learning & Artificial Intelligence](), [Neural Network](), [Windows](), [Code]() Tags: [ai](), [antivirus](), [binary-analysis](), [classification](), [computer-virus](), [cuda](), [cybersecurity](), [deep-learning](), [deep-neural-networks](), [deployment](), [dnn](), [ergo](), [feature-engineering](), [features](), [gpu](), [how-to](), [keras](), [machine-learning](), [malware](), [malware-detection](), [neural-network](), [neural-networks](), [nvidia](), [portable-executable](), [security-research](), [tensorflow](), [tf](), [windows-pe]() ### AI overview A practical tutorial on using machine learning and artificial neural networks to detect Windows malware without relying on an explicit signatures database. It uses malware detection as an example for the ergo project, which automates parts of model creation, data encoding, GPU training, benchmarking, and deployment. ### Source excerpt In this post we'll talk about two topics I love and that have been central elements of my (private) research for the last ~7 years: machi ## Deep Probabilistic Modelling with Gaussian Processes #NIPS2017 DevFeed: [Deep Probabilistic Modelling with Gaussian Processes #NIPS2017]() Original publisher: [Read original article]() Published: 2017-12-04T12:00:00Z Content type: tutorial Language: en Sources: [Alex Korbonits]() Topics: [Tutorial](), [VAE](), [Machine learning](), [Deep learning](), [Neural Network](), [NeurIPS]() Tags: [analysis](), [bias](), [conference](), [data](), [deep-learning](), [gaussian](), [inference](), [modelling](), [neural-network](), [neurips](), [probabilistic](), [research](), [supervised-learning](), [theory](), [tutorial](), [unsupervised-learning](), [videos]() ### AI overview Lecture notes from a NeurIPS 2017 tutorial introduce deep probabilistic modelling with Gaussian processes, covering probabilistic neural networks, uncertainty, graphical models, and the computational challenge of inference. ### Source excerpt Lecture notes from Neil Lawrence's NIPS 2017 tutorial on deep probabilistic modelling with Gaussian processes -- from GPs to deep GPs and variational inference. ## Introducing Similarity Search at Flickr DevFeed: [Introducing Similarity Search at Flickr]() Original publisher: [Read original article]() Author: Clayton Mellina Published: 2017-03-07T18:04:36Z Content type: release Language: en Sources: [Flickr]() Topics: [Image]() Tags: [discovery](), [images](), [machine-learning](), [machine-tags](), [neural-network](), [photos](), [search](), [similarity-search](), [text-to-image](), [uncategorized](), [visual-similarity]() ### AI overview Flickr introduces similarity search, allowing users to search for photos similar to a selected photo from a search result. The article explains how photo discovery differs from traditional text-based web search and describes the feature as a new way to explore visual concepts and Flickr's image collection. ### Source excerpt At Flickr, we understand that the value in our image corpus is only unlocked when our members can find photos and photographers that inspire them, so we strive to enable the discovery and appreciation of new photos. To further that ... Continue reading ->