Bosh sahifaKategoriyalarMashinaviy O'rganish va Neyron Tarmoqlar

🧠 Mashinaviy O'rganish va Neyron Tarmoqlar

Qaror daraxtlari, mustahkamlovchi o'rganish, o'z-o'zini tashkil qiluvchi xaritalar va k-means klasterlash vizual tarzda o'rganiladi.

16 simulyatsiya60 fps real vaqtda0 o'rnatish qadamlari
Decision Tree LiveCART decision tree with Gini impurity splitting; axis-aligned splits emerge level by level… Reinforcement LearningQ-learning agent navigates an 8×8 grid maze. Heatmap shows max Q-values; white arrows show… Self-Organising Map24×24 Kohonen SOM (576 neurons, 3D RGB input). Gaussian neighbourhood update as α and σ… K-Means Clustering — Interactive Machine LearningStep through K-means clustering: watch centroids converge, Voronoi regions update, and… PCA & SVD Visualiser — Principal Component Analysis InteractiveGenerate a 2D dataset, diagonalise covariance matrix into principal components, and watch… PerceptronNewRosenblatt's 1958 algorithm: each misclassified point tilts the weight vector w ← w + η·y·x.… k-Nearest NeighboursNewClassify a point by majority vote among its k closest neighbours. Decision regions update… BackpropagationNewδ pulses flow backward through a small MLP: gradients highlight edges, weights update, and… Random Forest ClassifierNewBagging and random feature selection turn weak decision trees into a robust forest. Compare… t-SNE VisualiserNewt-SNE projects high-dimensional clusters to 2D via gradient descent on KL divergence. Tune… Self-Organising Map (SOM)NewA Kohonen grid folds high-dimensional data into 2D: neuron weights self-organise as σ… Radial Basis Function NetworkNewRadial basis network: N Gaussian bumps of width σ sum into a smooth decision boundary —… K-Means Clustering — Lloyd's AlgorithmNewCluster 2D points with k-means: assign to nearest centroid, move to the mean, repeat. Watch… DBSCAN — Density-Based ClusteringNewDBSCAN grows clusters from dense core points via ε-neighbourhoods and minPts, labelling… Naive Bayes Classifier — Probabilistic DecisionNewClassify 2D points with Gaussian naive Bayes: estimate per-class means and variances, apply… Gradient Descent — 3D Loss Surface & OptimisersNewNavigate a 3D loss landscape (Rosenbrock, Rastrigin, Himmelblau) with SGD, Momentum, RMSprop…
Kategoriya haqida

Mashinaviy O'rganish va Neyron Tarmoqlar

Qaror daraxtlari, mustahkamlovchi o'rganish, o'z-o'zini tashkil qiluvchi xaritalar va k-means klasterlash vizual tarzda o'rganiladi.

Three.js · WebGL 60 FPS CC BY 4.0
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16ushbu kategoriyadagi interaktiv simulyatsiyalar
har qanday zamonaviy brauzerda 60 FPS tezlikda ishlaydi
🌐ingliz, ukrain, polyak, ispan, nemis, yapon, italyan, portugal (Braziliya), fransuz, arab, golland, xitoy, koreys, hind, turk, vyetnam, rus, indonez, fors, bengal, tay, filippin, suaxili, urdu, malay, tamil, ibroniy, yunon, panjobi, amhar, birma, kxmer, gruzin, nepal, mongol, arman, singal, laos, chex, ruminiya, venger, bolgar, shved, dat, fin, norveg, slovak, xorvat, serb, sloven, litva, latviya, eston, island, alban, makedon, bosniya va malta tillarida
🎓CC BY 4.0 — sinflar va LMS uchun bepul foydalanish mumkin

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Mashinaviy O'rganish va Neyron Tarmoqlar simulyatsiyalarini ishga tushirish uchun biror narsa o'rnatishim kerakmi?

Yo'q. Hammasi to'liq brauzeringizda ishlaydi — yuklab olish, plagin yoki hisob talab qilinmaydi. Chrome, Firefox, Edge va Safari'da ishlaydi; simulyatsiyalarning ko'pchiligi mobil qurilmalarda ham ishlaydi.

Bu simulyatsiyalarni o'qitish maqsadida ishlatsam bo'ladimi?

Ha — barcha kontent CC BY 4.0 litsenziyasi ostida ta'lim maqsadida foydalanish uchun bepul. Har qanday simulyatsiyaga to'g'ridan-to'g'ri havola berishingiz yoki uni LMS'ingizga iframe orqali joylashtirishingiz mumkin; API kaliti talab qilinmaydi.

Modellar ilmiy jihatdan aniqmi?

Ular professional dasturiy ta'minot bilan bir xil matematik formulalardan foydalanadi va real vaqtda sonli usulda yechiladi. Har bir simulyatsiya foydalanadigan algoritmlarini sanab o'tadi va ko'pchiligi orqasidagi matematikani tushuntiruvchi maqolaga havola beradi.

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