# Neural Published articles for Neural. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Amlogic A123X and C305X2 Arm Cortex-A320 SoCs target industrial and low-power AIoT applications DevFeed: [Amlogic A123X and C305X2 Arm Cortex-A320 SoCs target industrial and low-power AIoT applications]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-11T03:13:41Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Arm](), [cpu](), [Embedded Systems](), [Artificial Intelligence](), [Internet of things](), [Hardware](), [object-detection](), [Neural Network](), [Transformer]() Tags: [aiot](), [amlogic](), [arm](), [armv9](), [camera](), [computer-vision](), [cortex-a320](), [edge-ai](), [embedded](), [ethernet](), [h-264](), [hardware](), [iot](), [linux](), [llm](), [low-power](), [neural](), [npu](), [object-detection](), [processor](), [security](), [soc]() ### AI overview Amlogic has announced the A123X quad-core and C305X2 dual-core Arm Cortex-A320 SoCs for industrial and battery-powered edge AI and IoT devices. The preliminary specifications include video encoding and decoding, NPUs, image signal processing, camera interfaces, networking, USB, and low-power features. The article notes that full specifications, block diagrams, and software details are not yet available. ### Source excerpt Amlogic has unveiled the A123X quad-core and C305X2 dual-core Arm Cortex-A320 SoCs for industrial and battery-powered Edge AI and IoT applications such as robots, dashcams, IP cameras, video conferencing equipment, and so on. The Arm Cortex-A320 low-power Armv9 CPU core was introduced in February 2025, and Amlogic is the first silicon vendor to announce Cortex-A320 SoCs. Details are sparse, with no full specifications or block diagrams and limited software information, but let's see what we know so far. Amlogic A123X Amlogic A123X specifications: CPU - Quad-core Arm Cortex-A320 processor (Armv9.2-A, SVE2) GPU - None or not disclosed VPU H.264/H.265 encoding at 4K @ 60fps H.264/H.265 decoding at 4K @ 30fps AI 4 TOPS ADLA2 NPU for object detection and tracking CNN models 8 TOPS ADLA3 NPU supporting hardware-accelerated Transformer operations for ViT, LLM, etc. Neural network-based hardware SED engine for low-power audio event identification ISP - Low-light HDR ISP Supports [...] The post Amlogic A123X and C305X2 Arm Cortex-A320 SoCs target industrial and low-power AIoT applications appeared first on CNX Software - Embedded Systems News. ## MiniDXNN v0.4.0: Interactive neural texture compression on DirectX 12 DevFeed: [MiniDXNN v0.4.0: Interactive neural texture compression on DirectX 12]() Original publisher: [Read original article]() Author: Takahiro Harada; Sho Ikeda Published: 2026-08-13T14:30:00Z Content type: release Language: en Sources: [AMD GPUOpen]() Topics: [Compression](), [GPU](), [Inference](), [mlp](), [Machine learning](), [GUI](), [shaders](), [Open Source]() Tags: [agility-sdk](), [announcement](), [compression](), [directx](), [driver](), [getting-started](), [gpu](), [gpu-open-sdks](), [gpu-open-tools](), [gpuopen-sdks](), [gpuopen-tools](), [graphics-apis](), [gui](), [inference](), [maths](), [memory](), [microsoft](), [microsoft-agility-sdk](), [microsoft-directx](), [ml](), [mlp](), [neural](), [open-source](), [product-release](), [quick-start](), [real-time](), [shaders](), [technical-article](), [technical-articles](), [tools]() ### AI overview MiniDXNN v0.4.0 is an open-source library for GPU-accelerated MLP inference and training on DirectX 12. The release adds D3D12 Linear Algebra support, input encoding for neural texture compression, and a real-time GUI application for training and visualizing texture representations. ### Source excerpt MiniDXNN v0.4.0 introduces D3D12 Linear Algebra (SM 6.10) support, input encodings and neural texture compression, plus a real-time GUI app that trains and visualizes GPU-accelerated MLPs on DirectX® 12. ## Arm's Cortex A55 DevFeed: [Arm's Cortex A55]() Original publisher: [Read original article]() Author: Chester Lam Published: 2026-08-02T22:02:44Z Content type: article Language: en Sources: [Chips and Cheese]() Topics: [Arm](), [Front end](), [Cache](), [Neural Network]() Tags: [arm](), [cache](), [core](), [cortex-a55](), [devices](), [efficiency](), [floating-point](), [frontend](), [linux](), [memory](), [neural](), [performance]() ### AI overview A technical analysis of Arm's Cortex A55 examines its microarchitecture, including its in-order pipelines, frontend, memory subsystem, branch prediction, and implementations in the MediaTek Genio 1200 and other devices. ### Source excerpt Arm's 5-series cores are meant for tasks where performance barely matters, but power and area efficiency are top priorities. ## The Perceptron DevFeed: [The Perceptron]() Original publisher: [Read original article]() Author: Franco Fernando Published: 2026-07-18T05:44:10Z Content type: tutorial Language: en Sources: [The Polymathic Engineer]() Topics: [Neural Network]() Tags: [building](), [model](), [neural]() ### AI overview An explanation of how the perceptron works as a simple classification model and its role as a building block of neural networks. ### Source excerpt How the simplest classification model works, and why it is the building block of every neural network. ## 3 Questions: Neural transparency and the future of AI design DevFeed: [3 Questions: Neural transparency and the future of AI design]() Original publisher: [Read original article]() Author: Media Lab Published: 2026-07-15T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Neural Network](), [User Interfaces](), [interface](), [Language models]() Tags: [3-questions](), [ai](), [ai-companion](), [ai-empathy](), [ai-hallucinations](), [ai-personalization](), [ai-safety](), [ai-sycophancy](), [ai-toxicity](), [algorithms](), [anthony-baez](), [apps](), [artificial-intelligence](), [behavioral-prediction](), [character-ai](), [chatbot-behavior](), [computer-science-and-technology](), [data](), [ethics](), [faculty](), [human-ai-interaction](), [human-computer-interaction](), [interface](), [interview](), [language-models](), [large-language-models](), [machine-learning](), [mechanistic-interpretability](), [media-lab](), [mental-health](), [mit-faculty-interview](), [mit-media-lab](), [neural](), [neural-transparency](), [pat-pataranutaporn](), [persona-scores](), [persona-vectors](), [personalized-ai](), [research](), [safety](), [school-of-architecture-and-planning](), [sheer-karny](), [sunburst-visualization](), [technology-and-society](), [transparency](), [users]() ### AI overview An MIT Media Lab team introduces "neural transparency," an interface that visualizes internal patterns in AI models to help users anticipate how personalized chatbots may behave. The approach compares model activations associated with contrasting traits such as empathy, honesty, toxicity, hallucination, and sycophancy, then maps custom system prompts onto those behavior directions. ### Source excerpt Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word. ## 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 " ## Worth Reading: Microsoft Cloud Security, Neuro-Symbolic AI, Linux 7.0, and AI-Driven Cybercrime DevFeed: [Worth Reading: Microsoft Cloud Security, Neuro-Symbolic AI, Linux 7.0, and AI-Driven Cybercrime]() Original publisher: [Read original article]() Author: Russ Published: 2026-04-22T12:11:04Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [Artificial Intelligence](), [Cybercrime](), [Cybersecurity](), [Linux](), [Cloud](), [Kernel](), [Microsoft](), [networking](), [Neural Network]() Tags: [artificial-intelligence](), [cloud](), [cybercrime](), [cybersecurity](), [kernel](), [linux](), [machine](), [microsoft](), [networking](), [neural](), [worth-reading]() ### AI overview This roundup covers a federal cybersecurity assessment of a major Microsoft cloud offering, the evolution of neuro-symbolic artificial intelligence, the Linux 7.0 kernel release, and the threat of AI-driven cybercrime to small businesses. ### Source excerpt In late 2024, the federal government's cybersecurity evaluators rendered a troubling verdict on one of Microsoft's biggest cloud computing offerings. This report explores the evolution and current state of neuro- symbolic artificial intelligence, an approach that integrates neural network capabilities with symbolic reasoning. The Linux 7.0 kernel is now out, and it's one of the most impactful releases in years for networking professionals. The human-speed defense of small business is being obliterated by the machine-speed offense of AI-driven cybercrime. Today, what large companies treat as a manageable risk is a terminal expense for small enterprises, with 60% of small enterprises shutting down within six months of a major attack. The original frustration was familiar. You build on one provider, they change pricing, deprecate an API, or just aren't the right tool anymore, and migrating is brutal. ## Reverse-engineering a neural network puzzle with mechanistic interpretability DevFeed: [Reverse-engineering a neural network puzzle with mechanistic interpretability]() Original publisher: [Read original article]() Author: Ricson Cheng Published: 2026-02-24T00:00:00Z Content type: tutorial Language: en Sources: [Jane Street]() Topics: [Neural Network](), [Machine learning](), [PyTorch]() Tags: [capture](), [ml](), [models](), [neural](), [puzzle](), [pytorch](), [research]() ### AI overview This article explains a Jane Street machine-learning puzzle in which solvers receive a neural network specification, including its weights, and must determine what the network does. It describes why ordinary brute-force approaches fail and presents a solver's reverse-engineering process using mechanistic interpretability. ### Source excerpt A lot of "capture-the-flag" style ML puzzles give you a black box neural net, and your job is to figure out what it does. When we were thinking of creating our own ML puzzle early last year, we wanted to do something a little different. We thought it'd be neat to give users a complete specification of the neural net, weights and all. They would then be forced to use the tools of mechanistic interpretability to reverse engineer the network--which is a situation we sometimes find ourselves facing in our own research, when trying to interpret features of complex models. ## Arc Virtual Cell Challenge: A Primer DevFeed: [Arc Virtual Cell Challenge: A Primer]() Original publisher: [Read original article]() Author: Christopher Fleetwood; Abhinav Adduri Published: 2025-07-18T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [datasets](), [AI Research](), [Neural Network](), [data](), [Open Source]() Tags: [artificial-intelligence](), [collaboration](), [dataset](), [guide](), [model](), [neural](), [open-source](), [train](), [training]() ### AI overview This primer introduces the Arc Virtual Cell Challenge, which aims to train a neural network to predict how silencing a gene with CRISPR affects a cell. It explains the challenge dataset of roughly 300,000 single-cell RNA sequencing profiles, the transcriptome representation, control and perturbed cells, and the difficulty of separating perturbation signals from biological variation. ### 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. ## Category Prediction for Search Query Understanding DevFeed: [Category Prediction for Search Query Understanding]() Original publisher: [Read original article]() Author: music and waves Published: 2024-04-21T12:40:48Z Content type: article Language: en Sources: [Myntra]() Topics: [Query (disambiguation)](), [Training AI Models](), [datasets](), [data]() Tags: [ai](), [classification](), [data](), [deep-learning](), [ecommerce](), [information-retrieval](), [model](), [neural](), [nlp](), [search](), [text-classification](), [train](), [training]() ### AI overview This article describes Myntra's multi-label product-category classification model for understanding ambiguous search queries. It covers preparing query-and-category training data and training a neural text classifier to predict relevant categories for live searches. ### Source excerpt Navigating through online shopping platforms can sometimes feel like finding your way through a maze. Take the search bar, for example. You type in "winter upper wear," hoping to find the perfect jacket or cozy sweatshirt. But here's the tricky part: the search engine has to decipher what you mean. Is it jackets you're after? Or maybe sweatshirts? Or both? It gets even more confusing when you consider the overlapping categories. Kurtas can be standalone articles or part of kurta sets. And loafers? They could belong to formal shoes or casual shoes and certainly not sports shoes. See the challenge? To tackle this, Myntra uses a multi-label search to product category classification model. It's like having an assistant that can understand possible intents from your search query. So when you type in something like "whey," the model knows you might be looking for protein or health supplements. But here's the catch: search queries can be short and vague, and they often use words that don't directly match category names. People might search using different terms or even regional variations. So, the model needs to be clever enough to map those words to the right categories internally. The goal is to capture all possible intents without cluttering your search results with irrelevant stuff. After all, nobody likes sifting through pages of irrelevant products. It's a delicate balance between covering all bases and keeping things tidy. Solution The solution has 2 major components. I. Data preparation We prepare ( search query : categories ) data points to be consumed in training by the neural classifier. Ex. ( ethnic wear : kurta, sarees ) II. Training a neural model We train a neural multi-label text classifier that consumes the prepared training data which is used to predicts categories for search queries live. I. Data Preparation We generate the supervised text classification training data in form of a search query and its product category(s) as its labels. This set is enrich ## MELON: Reconstructing 3D objects from images with unknown poses DevFeed: [MELON: Reconstructing 3D objects from images with unknown poses]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-18T18:41:00Z Content type: article Language: en Sources: [Google Research]() Topics: [3D](), [Computer vision](), [Inference](), [Google]() Tags: [3d](), [computer-vision](), [gans](), [gaussian-splatting](), [generative](), [google](), [images](), [inference](), [machine-learning](), [models](), [neural](), [research](), [rgb](), [rotation](), [techniques](), [vision]() ### AI overview This Google Research article explains the challenge of reconstructing 3D objects from a small number of images when the camera poses are unknown. It covers pose inference, pseudo-symmetries, local-minimum failures, and prior approaches including NeRF, 3D Gaussian Splatting, GAN-based methods, BARF, SAMURAI, GNeRF, VMRF, SparsePose, and RUST. ### Source excerpt Posted by Mark Matthews, Senior Software Engineer, and Dmitry Lagun, Research Scientist, Google Research A person's prior experience and understanding of the world generally enables them to easily infer what an object looks like in whole, even if only looking at a few 2D pictures of it. Yet the capacity for a computer to reconstruct the shape of an object in 3D given only a few images has remained a difficult algorithmic problem for years. This fundamental computer vision task has applications ranging from the creation of e-commerce 3D models to autonomous vehicle navigation. A key part of the problem is how to determine the exact positions from which images were taken, known as pose inference. If camera poses are known, a range of successful techniques -- such as neural radiance fields (NeRF) or 3D Gaussian Splatting -- can reconstruct an object in 3D. But if these poses are not available, then we face a difficult "chicken and egg" problem where we could determine the poses if we knew the 3D object, but we can't reconstruct the 3D object until we know the camera poses. The problem is made harder by pseudo-symmetries -- i.e., many objects look similar when viewed from different angles. For example, square objects like a chair tend to look similar every 90° rotation. Pseudo-symmetries of an object can be revealed by rendering it on a turntable from various angles and plotting its photometric self-similarity map. Self-Similarity map of a toy truck model. Left: The model is rendered on a turntable from various azimuthal angles, θ. Right: The average L2 RGB similarity of a rendering from θ with that of θ*. The pseudo-similarities are indicated by the dashed red lines. The diagram above only visualizes one dimension of rotation. It becomes even more complex (and difficult to visualize) when introducing more degrees of freedom. Pseudo-symmetries make the problem ill-posed, with naïve approaches often converging to local minima. In practice, such an approach might mistake the ## 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 ## Recurrent Networks Hello World in Clojure with new Deep Diamond RNN support on CPU and GPU DevFeed: [Recurrent Networks Hello World in Clojure with new Deep Diamond RNN support on CPU and GPU]() Original publisher: [Read original article]() Published: 2022-08-26T14:25:00Z Content type: tutorial Language: en Sources: [Dragan Djuric]() Topics: [Clojure](), [Deep learning](), [Time Series](), [Programming]() Tags: [clojure](), [cpu](), [deep](), [deep-learning](), [diamond](), [gpu](), [learning](), [networks](), [neural](), [programming](), [recurrent](), [rnn](), [time-series]() ### AI overview A beginner-friendly Clojure tutorial demonstrates recurrent neural networks in Deep Diamond by training a model to predict the next value in a simple numerical sequence. It introduces time-series prediction and notes that the example can run on CPU and GPU. ### Source excerpt I've been busy in the last period working on new major features in Deep Diamond, one of which is the support for Recurrent Neural Networks (RNN). It's not been an easy ride, but I can finally show you some results! Big thanks for everyone that's helping me with this by buying my books (or subscribing to the upcoming editions), and Clojurists Together, who generously funded me in the past year to work on this. I know that most of you probably don't have much more than passing familiarity with deep learning, let alone recurrent neural networks, and that's why I'll try to show a very simple example on the level of Hello World that anyone interested in machine learning and programming can understand and try. So, enough talk, let's get down to business. What are we doing We are demonstrating a hammer: recurrent neural networks. Just kidding; we would like to create a (software) device that can learn to predict the next data point in a series. Depending on the data, this can be done in a number of ways (even by convolutional neural networks (CNN) that Deep Diamond already supported), one of which is RNN. So, we are creating a recurrent network, and training it with a set of data for this task. An example of data that fits this task would be temperature at some place, stock prices, and any other (possibly infinite) sequence of numbers in one of more dimensions that have an ordinal relation, that is, has an abstract notion of time attached to it. Then, we are trying to forecast one or more values that are happening in the future (temperature the next day, or the closing price of a stock, etc.). Since Hello World has to be dead simple, a real example would have too many opaque numbers, so we'll solve an artificially trivial task of teaching our network to predict the next number in the series for obvious series such as 1, 2, 3, 4, 5. Of course, in real life we rarely need to solve that exact task with such a bazooka as RNN, but if this is your first contact with time series ## How to Improve User Experience (and Behavior): Three Papers from Stanford's Alexa Prize Team DevFeed: [How to Improve User Experience (and Behavior): Three Papers from Stanford's Alexa Prize Team]() Original publisher: [Read original article]() Author: A Href; Amelia Hardy; Haojun Li; Abigail See Published: 2022-02-01T08:00:00Z Content type: article Language: en Sources: [The Stanford AI Lab Blog]() Topics: [User Experience](), [Bot](), [Chat Bot](), [Language models](), [Open Source]() Tags: [alexa](), [chat](), [code](), [developers](), [experience](), [language-models](), [modular](), [neural](), [scripted]() ### AI overview This article presents research from Stanford's Alexa Prize team on improving user experience and behavior in socialbot conversations. It discusses user dissatisfaction, responses to offensive behavior, and how conversational control can be balanced between users and bots. The Chirpy Cardinal socialbot uses a modular combination of neural generation and scripted dialogue to support broad, open-domain conversations. ### Source excerpt Introduction In 2019, Stanford entered the Alexa Prize Socialbot Grand Challenge 3 for the first time, with its bot Chirpy Cardinal, which went on to win 2nd place in the competition. In our previous post, we discussed the technical structure of our socialbot and how developers can use our open-source code to develop their own. In this post we share further research conducted while developing Chirpy Cardinal to discover common pain points that users encounter when interacting with socialbots, and strategies for addressing them. The Alexa Prize is a unique research setting, as it allows researchers to study how users interact with a bot when doing so solely for their own motivations. During the competition, US-based Alexa users can say the phrase "let's chat" to speak in English to an anonymous and randomly-selected competing bot. They are free to end the conversation at any time. Since Alexa Prize socialbots are intended to create as natural an experience as possible, they should be capable of long, open-domain social conversations with high coverage of topics. We observed that Chirpy users were interested in many different subjects, from current events (e.g., the coronavirus) to pop culture (e.g., the movie Frozen 2) to personal interests (e.g,. their pets). Chirpy achieves its coverage of these diverse topics by using a modular design that combines both neural generation and scripted dialogue, as described in our previous post. We used this setting to study three questions about socialbot conversations: What do users complain about, and how can we learn from the complaints to improve neurally generated dialogue? What strategies are effective and ineffective in handling and deterring offensive user behavior? How can we shift the balance of power, such that both users and the bot are meaningfully controlling the conversation? We've published papers on each of these topics at SIGDIAL 2021 and in this post, we'll share key findings which provide practical insights for ## 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... ## A video about running the author's life through a deep learning algorithm DevFeed: [A video about running the author's life through a deep learning algorithm]() Original publisher: [Read original article]() Author: Techno Tim Published: 2021-02-01T14:00:00Z Content type: opinion Language: en Sources: [Techno Tim]() Topics: [Machine learning](), [Neural Network](), [Algorithm]() Tags: [algorithm](), [deep-learning](), [homelab](), [ml](), [neural]() ### AI overview The article presents a video about running the author's life through a neural network using deep learning and machine learning techniques. ### Source excerpt My life, ran against a neural network and detected by Deep Learning.If you'd like to see how this video was generated using ML and Deep Learning, check out the video here: How this video was generated 📺 Watch Video Links 🛍 Check out the new Merch Shop at https://l.technotim.com/shop ⚙ See all the hardware I recommend at https://l.technotim.com/gear 🚀 Don't forget to check out the 🚀Lau... ## Accelerating Self-Play Learning in Go DevFeed: [Accelerating Self-Play Learning in Go]() Original publisher: [Read original article]() Author: David Wu Published: 2019-02-28T00:00:00Z Content type: article Language: en Sources: [Jane Street]() Topics: [Go Language](), [Reinforcement learning](), [Deep learning](), [Deep neural networks](), [AI Research]() Tags: [deep-learning](), [go](), [machine-learning](), [neural](), [reinforcement-learning](), [research]() ### AI overview Jane Street describes a personal research project applying neural-network training and self-play learning to Go. Short and medium-length runs produced strong professional or possibly superhuman play, and the team released a paper, source code, trained networks, and an online bot. The article reports preliminary estimates that the techniques may accelerate learning compared with Leela Zero, while noting that further testing is needed. ### Source excerpt At Jane Street, over the last few years, we've been increasingly exploring machine learning to improve our models. Many of us are fascinated by the rapid improvement we see in a wide variety of applications due to developments in deep learning and reinforcement learning, both for its exciting potential for our own problems, and also on a personal level of pure interest and curiosity outside of work. ## Choosing a Deep Learning library for developing and deploying your App/Service DevFeed: [Choosing a Deep Learning library for developing and deploying your App/Service]() Original publisher: [Read original article]() Published: 2018-03-23T10:11:36Z Content type: article Language: en Sources: [Curalate]() Topics: [Deep learning](), [Library](), [App](), [Requirements](), [Code](), [Open Source](), [GPU](), [Linux](), [browser]() Tags: [app](), [caffe](), [cntk](), [code](), [comparison](), [deep](), [deep-learning](), [gpu](), [learning](), [library](), [linux](), [mxnet](), [network](), [neural](), [open-source](), [pytorch](), [requirements](), [tensorflow](), [vs]() ### AI overview This article discusses how to choose a deep learning library for developing and deploying applications or services. Drawing on Curalate's experience using several libraries in production, it identifies factors such as application needs, deployment platforms, deep network architecture, API language requirements, and codebase quality. ### Source excerpt Interest in deep learning is growing and growing and, with it at peak hype right now, a lot of people are looking to find the best deep learning library to build their new app or bring their company into the modern age. There are many deep learning toolkits to choose from ranging from the long used, supported, and robust academic libraries to the new state-of-the-art, industry backed platforms. At Curalate, we've been working on deep learning problems since 2014, meaning we've had the chance to watch the deep learning community and its open source libraries grow. We have also had the fortunate (unfortunate?) experience of using a few of the deep learning libraries in our production services and applications, and along the way, we have learned a lot about what to look for in a deep learning library to build reliable, production-ready applications and services. In this post, I'll share our lessons learned knowledge in hopes it will help you in your search for the perfect deep learning library match. You might even find that your best fit is using more than one! Important factors The specifics needs of your application/service The platform you are developing on and deploying to. Develop in OSX? Linux? Windows? Plan on having your application run in a web browser? A smart phone? A massive multi-node GPU cluster? It's not surprising that each of the libraries have prioritized different environments and some will work much better for your specific situation. The specific deep net architecture you are trying to implement If you are just trying to implement a typical, pre-trained classification net, this factor may not be as important for you. Some libraries are more performant and appropriate for certain types of deep nets (LSTMs, RNNs), but more on this later. API language requirements If you already have a code base written in language A, you probably would like to keep it that way without having to figure out some convoluted way to fit a deep net interface in language B ## Higher level ops for building neural network layers with deeplearn.js DevFeed: [Higher level ops for building neural network layers with deeplearn.js]() Original publisher: [Read original article]() Author: Suresh Alse (noreply@blogger.com) Published: 2018-01-23T22:36:00Z Content type: tutorial Language: en Sources: [Life Plus Linux]() Topics: [Neural Network](), [Tensorflow](), [Code](), [Graphs]() Tags: [batching](), [code](), [datasets](), [deeplearning](), [graph](), [javascript](), [ml](), [neural](), [tensorflow](), [visualization]() ### AI overview This tutorial describes higher-level neural-network operations built for deeplearn.js, specifically implementations of tf.layers.conv2d and tf.layers.flatten. The operations are designed to closely follow corresponding TensorFlow function definitions, with documented arguments and return behavior. ### Source excerpt I have been meddling with google's deeplearn.js lately for fun. It is surprisingly good given how new the project is and it seems to have a sold roadmap. However it still lacks something like tf.layers and tf.contrib.layers which have many higher level functions that has made using tensorflow so easy. It looks like they will be added to Graphlayers in future but their priorities as of now is to fix the lower level APIs first - which totally makes sense. So, I quickly built one for tf.layers.conv2d and tf.layers.flatten which I will share in this post. I have made them as close to function definitions in tensorflow as possible. 1. conv2d - Functional interface for the 2D convolution layer. Arguments: inputs Tensor input. filters Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size Number to specify the height and width of the 2D convolution window. graph Graph opbject. strides Number to specify the strides of convolution. padding One of "valid" or "same" (case-insensitive). data_format "channels_last" or "channel_first" activation Optional. Activation function which is applied on the final layer of the function. Function should accept Tensor and graph as parameters kernel_initializer An initializer object for the convolution kernel. bias_initializer An initializer object for bias. name string which represents name of the layer. Returns: Tensor output. Usage: Add this to your code: 2. flatten - Flattens an input tensor. I wrote these snippets while building a tool using deeplearnjs where I do things like loading datasets, batching, saving checkpoints along with visualization. I will share more on that in my future posts. ## Hacking FaceNet using Adversarial examples DevFeed: [Hacking FaceNet using Adversarial examples]() Original publisher: [Read original article]() Author: Suresh Alse (noreply@blogger.com) Published: 2018-01-11T23:29:00Z Content type: tutorial Language: en Sources: [Life Plus Linux]() Topics: [face recognition](), [Machine Learning, Security Attacks](), [Deep learning](), [Neural Network](), [Authentication]() Tags: [authentication](), [deep-learning](), [deeplearning](), [embedding](), [face-recognition](), [hacking](), [ml](), [neural](), [security]() ### AI overview This tutorial explains how FaceNet performs face recognition using embeddings and triplet loss, then demonstrates generating small adversarial noise intended to make an attacker's photo be identified as a target. ### Source excerpt With the rise in popularity of face recognition systems with deep learning and it's application in security/ authentication, it is important to make sure that it is not that easy to fool them. I recently finished the 4th course on deeplearning.ai where there is an assignment which asks us to build a face recognition system - FaceNet. While I was working on the assignment, I couldn't stop thinking about how easy it is to fool it with adversarial examples. In this post I will tell you how I managed to do it. First off, some basics about FaceNet. Unlike image recognition systems which map every image with a class, it is not possible to assign a class label to every face in face recognition. This is because one, there are way too many faces that a system should handle in the real world to assign class to each of them and two, if there are new people the system should handle, it can't do it. So, what we do is, we build a system that learns similarities and dissimilarities. Basically, there is a neural network similar to what we have in image recognition and instead of applying softmax in the end, we just take the logits as embedding for the given image input and then minimize something called the triplet loss. Consider face A, we have a positive match P and negative match N. If f is the embedding function and L is the triplet loss, we have this: Triplet loss Basically, it is incentivizing small distance between A - P and large distance between A - N. Also, I really recommend watching Ian Goodfellow's lecture from Stanford's CS231n course if you want to know about adversarial examples. Like I said earlier, this thought came to me while doing an assignment from 4th course from deeplearning.ai which can be found here and I have built on top of it. The main idea here is to find small noise that when added to someone's photo although causing virtually no visual changes, can make faceNet identify them as the target. Benoit (attacker) Add noise Kian Kian Actual (Target) First l