Classe I Gli occlusori rappresentano, nella storia, il primo tentativo di disporre di uno strumento che, oltre a mantenere in modo stabile e ripetibile il rappor- to statico di occlusione, permettesse quantomeno il movimento di aper- tura e chiusura delle due arcate dentarie. Le cerniere che simulano l’ATM negli occlusori consentono ai modelli del- le arcate dentarie il solo movimento curvilineo a cerniera di apertura e chiusura. Nella maggior parte dei casi, posteriormente ai modelli questi strumenti
General Links
Positive Behaviors
1. Core Concept
The human brain processes information like a computer, in a series of stages from receiving a stimulus to producing a response. The speed of this processing can be measured by Reaction Time (RT).
2. The Information-Processing Model
A three-stage model explaining how sensory input is transformed into motor output.
Input: Information from the environment received via the senses.
Stimulus Identification Stage: Deciding if a
Generative Adversarial Network (GAN)
Deep learning model with two NNs — Generator & Discriminator — that compete to create realistic fake data.
“Generator fake data banata hai, Discriminator usse real/fake pehchanta hai.”
Noise → Generator → Fake Data → Discriminator → Real/Fake
Components:
1️⃣ Generator: makes fake data (tries to fool D)
2️⃣ Discriminator: checks data (real or fake)
Working:
G generates fake samples
D detects fake vs real
Both train together →
🧠 Recurrent Neural Network (RNN)
A neural network for sequential data (time series, speech, text).
It has memory of past inputs to affect current output.
Simple: “RNN past data yaad rakh kar next output predict karta hai.”
x1 → x2 → x3 → ...↓ ↓ ↓
h1 → h2 → h3 → ...
↓ ↓ ↓
y1 y2 y3
Stepwise:1️⃣ Input – Sequential data (text/audio)
2️⃣ Hidden Layer – Current input + previous hidden state
Eq: hₜ = f(Wxₜ + Uhₜ₋₁ + b)
3️⃣
1.Explain the Expectation-Maximization (EM) algorithm and its application to Gaussian Mixture Models.
The Expectation-Maximization (EM) algorithm is an iterative method used to estimate parameters in statistical models that involve latent (hidden) variables, such as missing data or unobserved groupings. It is especially useful for fitting models like Gaussian Mixture Models (GMMs), where the data is assumed to come from a mixture of several Gaussian distributions, but the assignment of each data
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The Expectation-Maximization (EM) algorithm is an iterative method used to estimate the parameters of statistical models that involve latent (unobserved) variables, such as missing data or hidden cluster assignments. It is especially useful for fitting Gaussian Mixture Models (GMMs), where the goal is to model data as a mixture of several Gaussian distributions.
How the EM Algorithm Works
The EM algorithm alternates between two steps: