324
COBuild a real-time fraud detection system that identifies suspicious transactions using machine learning and behavioral analytics just like payment companies.
#DataScience #MachineLearning #FraudDetection #Fintech #CodeVisium
@codevisium
#Tinyml Microcontroller Board
世界中の人々によるTinyml Microcontroller Boardに関する件のリール動画を視聴。
ログインせずに匿名で視聴。
トレンドリール
(12)
423
TEUpskilling: learning TinyML. 😬
Today was about deploying an image classification model on a microcontroller.
Not exactly the flashiest demos, but challenging enough to keep my weekends busy. So… yay. 🥳
And yes, I did some vibe coding. Otherwise, this would’ve easily taken a week. 🙂
Tiny models. Tiny devices. Very non-tiny effort.
@techembers
149
FUA fascinating glimpse into where next-gen data architecture meets real-world hardware! This segment highlights a surprisingly practical, cutting-edge concept: connecting the tangible world via **IoT** sensors (think **Raspberry Pi** and **Arduino** setups) with sophisticated data modeling, perhaps even in something as ubiquitous as earphones. When you see the potential use cases, you realize how crucial powerful graph structures are for making sense of complex, connected data streams flowing from the edge. Experts are clearly looking beyond the basics to build truly intelligent systems.
#GraphDatabase #IoT #DataModeling #TechInnovation #Neo4j
@futureautomate
384
FNI just deployed an interactive MNIST Digit Classifier
You can draw any number directly on the canvas, and the model performs real-time inference in the browser.
Here’s the wild part:
- The model is only ~26KB.
That’s it.
Today, we talk about models that are hundreds of megabytes… gigabytes… even terabytes in scale.
But with just 26KB, this neural network can already classify handwritten digits reliably.
No matter how the digit varies — thicker strokes, messy handwriting, slightly off-center drawings — it consistently predicts correctly.
That’s the power of neural networks:
They don’t memorize pixels — they learn patterns.
This principle was powerfully demonstrated by Yann LeCun in 1998 with “Gradient-Based Learning Applied to Document Recognition”, introducing LeNet-5 and helping spark the deep learning revolution.
From kilobytes to trillion-parameter models — the core idea remains the same.
You can try it here:
https://fnilvuwu.github.io/mnist-digit-classifier/
Would love your thoughts and feedback
#AI #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks
#Tech #Innovation #DataScience #ComputerVision
#BuildInPublic #IndieHacker #WebAI #Developers #Coding
#AIProjects #MLProjects #EdgeAI #TinyML #FutureOfAI
@fnilvuwu
315
DSZero trust architecture enhancing data security
@dswithdennis
207
DR🔍 Binary Thresholding Made Simple
ㅤ
Binary thresholding is all about turning pixels into decisions-above the threshold, it’s max value; below, it’s zero. 🎯
From bright whites (255) to dim grays (127), you control how your image transforms. A powerful yet simple tool in computer vision!
ㅤ
#ComputerVision #ImageProcessing #BinaryThresholding #AI #MachineLearning #OpenCV #DataScience #TechExplained
@dr_satya_mallick
336
OP🔍 Binary Thresholding Made Simple
ㅤ
Binary thresholding is all about turning pixels into decisions-above the threshold, it’s max value; below, it’s zero. 🎯
From bright whites (255) to dim grays (127), you control how your image transforms. A powerful yet simple tool in computer vision!
ㅤ
#ComputerVision #ImageProcessing #BinaryThresholding #AI #MachineLearning #OpenCV #DataScience #TechExplained
@opencvuniversity
724
CONMIs can’t be masked. They can’t be postponed. They interrupt everything.
That’s why tracing inside an NMI context requires special NMI-safe tracepoints and per-CPU buffers — no sleeping, no unsafe locks, no deadlocks.
When the highest-priority interrupt fires, your observability has to be just as disciplined.
#software #linux #computerscience #system
@commandncode
2.6K
WAA multilayer perceptron (MLP) is the term used for a "basic" neural network. It can be used to recognize handwritten digits when trained on the MNIST dataset. The network starts by taking each handwritten digit image and flattening it into a vector of pixel values.
This vector is passed through one or more fully connected layers, where linear transformations followed by nonlinear activation functions (ReLU, sigmoid) allow the network to learn increasingly complex features.
During training, the model adjusts its weights to minimize classification error across the ten digit classes.
Even with this simple structure, an MLP can achieve strong performance on MNIST, correctly recognizing handwritten digits.
Want to Learn Deep Learning? Join 7000+ Others in our Visually Explained Deep Learning Newsletter—learn industry knowledge with easy-to-read issues complete with math and visuals. It's completely FREE (link in bio 🔗).
#machinelearning #deeplearning #datascience
@waterforge_nyc
236
DAEncrypted Network Traffic Classification Using Machine Learning
This project focuses on classifying encrypted network traffic without decrypting it. By leveraging flow-level features such as packet size, inter-arrival time, byte count, direction patterns, and TLS handshake information, machine learning models can identify the type of application or traffic class. Models like Random Forest, XGBoost, and 1D CNN are trained on a Kaggle encrypted traffic dataset to detect patterns hidden in encrypted packets, enabling effective traffic analysis while preserving privacy.
@dataprofinalyearprojecthub
108
SO𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗶𝘃𝗲 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗜𝘀 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗲𝗯𝘁.
Universities don’t suffer from a lack of telemetry.
They suffer from fragmented context.
Multi-vendor fabrics.
Decentralized domains.
Parallel tools.
Expanding AI workloads.
Signals are everywhere.
Correlation is not.
This infographic breaks down:
– Where MTTR is lost
– How investigative stitching compounds under scale
– Why interpretive speed now defines reliability
And how Aviz Networks reframes network operations from dashboard accumulation to explainable intelligence at decision speed.
Because modern infrastructure isn’t judged by uptime.
It’s judged by how fast it can explain itself.
@soveraignagents
151
AMTool #17 — NumPy 🔢
Behind data science, AI, and scientific computing
there’s NumPy.
Fast calculations, efficient arrays,
and the backbone of Python’s data ecosystem.
30 Posts • 30 Tools
Building AI & data foundations 💙
#NumPy #Python #DataScience #MachineLearning 30Posts30Tools LearnInPublic
@am_i_engineer_007
✨ #Tinyml Microcontroller Board発見ガイド
Instagramには#Tinyml Microcontroller Boardの下にthousands of件の投稿があり、プラットフォームで最も活気のあるビジュアルエコシステムの1つを作り出しています。
ログインせずに最新の#Tinyml Microcontroller Boardコンテンツを発見しましょう。このタグの下で最も印象的なリール、特に@waterforge_nyc, @commandncode and @techembersからのものは、大きな注目を集めています。
#Tinyml Microcontroller Boardで何がトレンドですか？最も視聴されたReels動画とバイラルコンテンツが上部に掲載されています。
人気カテゴリー
📹 ビデオトレンド: 最新のReelsとバイラル動画を発見
📈 ハッシュタグ戦略: コンテンツのトレンドハッシュタグオプションを探索
🌟 注目のクリエイター: @waterforge_nyc, @commandncode, @techembersなどがコミュニティをリード
#Tinyml Microcontroller Boardについてのよくある質問
Pictameを使用すれば、Instagramにログインせずに#Tinyml Microcontroller Boardのすべてのリールと動画を閲覧できます。あなたの視聴活動は完全にプライベートです。ハッシュタグを検索して、トレンドコンテンツをすぐに探索開始できます。
パフォーマンス分析
12リールの分析
💡 トップ投稿は平均1.0K回の再生（平均の2.1倍）
週3-5回、活動時間に定期的に投稿
コンテンツ作成のヒントと戦略
💡 トップコンテンツは1K+再生回数を獲得 - 最初の3秒に集中
✨ 一部の認証済みクリエイターが活動中（17%） - コンテンツスタイルを研究
✍️ ストーリー性のある詳細なキャプションが効果的 - 平均長517文字
📹 #Tinyml Microcontroller Boardには高品質な縦型動画（9:16）が最適 - 良い照明とクリアな音声を使用
#Tinyml Microcontroller Board に関連する人気検索
🎬動画愛好家向け
Tinyml Microcontroller Board ReelsTinyml Microcontroller Board動画を見る
📈戦略探求者向け
Tinyml Microcontroller Boardトレンドハッシュタグ最高のTinyml Microcontroller Boardハッシュタグ
🌟もっと探索
Tinyml Microcontroller Boardを探索#microcontrollers#microcontrol#tinyml microcontroller