Machine Learning Problems

Ai Counterfeit Detection C41Ce1

1. The problem is to develop an AI-based system for counterfeit product detection and vendor authenticity verification. 2. While this is a conceptual and technical problem rather t

Learning Rate 6Ff86E

1. The problem asks which learning curve, A or B, corresponds to a learning rate $\alpha$ that is too large during gradient descent. 2. Feature scaling often involves rescaling fea

Naive Bayes Classification 636448

1. **Асуудлыг тодорхойлох:** Өгөгдсөн санамсаргүй вектор $\mathbf{X} = (X_1, X_2, X_3, X_4) = (0, 4.26, 4, 1)$ утга нь аль категорт харьяалагдахыг Гэнэн Байесын алгоритмаар олох.

Loss Function Critical Points 8Db1E0

1. **Problem Statement:** Find all critical points of the loss function $$L(w) = (w - 4)^4 - 3(w - 2)^3 + 10$$ and classify them using the second derivative test. 2. **Formula and

Bptt Formula D3B47F

1. The problem is to understand the detailed formula for Backpropagation Through Time (BPTT), which is used to train recurrent neural networks (RNNs). 2. BPTT unfolds the RNN throu

Relu Differential C24A2F

1. **Problem Statement:** We have a neuron function defined as $f(x) = \text{ReLU}(wx + b)$ where $\text{ReLU}(z) = \max(0, z)$.

Negative Gradient 35Cb54

1. **Problem statement:** We are given the loss function $$L(w) = (w - 3)^2 + 2$$ and the weight update rule in gradient descent: $$w_{k+1} = w_k - \eta \nabla L(w_k)$$.

Gradient Descent 4B8F86

1. **Problem Statement:** We are given the loss function $$L(w) = (w - 3)^2 + 2$$ and the weight update rule in gradient descent: $$w_{k+1} = w_k - \eta \nabla L(w_k)$$. We need to

Gradient Descent 213350

1. **Problem Statement:** We are given the loss function $$L(w) = (w - 3)^2 + 2$$ and the gradient descent update rule $$w_{k+1} = w_k - \eta \nabla L(w_k)$$. We need to:

Backpropagation Example

1. **Problem Statement:** We need to develop and train a simple neural network using the backpropagation algorithm with a given dataset. 2. **Setup:** Let's consider a simple neura

Backpropagation Training

1. **Problem Statement:** We are asked to use the backpropagation algorithm to develop and train a neural network given input-output pairs. 2. **Understanding Backpropagation:** Ba

Gradient Descent Speed

1. Το πρόβλημα ζητά να αξιολογήσουμε αν η ταχύτητα προσέγγισης του ελαχίστου στο gradient descent είναι σταθερή και ανεξάρτητη από τη μορφή της συνάρτησης. 2. Ο αλγόριθμος gradient

Hmm Viterbi Tanh

1. **Problem 1: Compute the joint probability $p(x,z)$ for given sequences $x=[6,3,1,2,4]$ and $z=[L,F,F,L,L]$ using the HMM parameters.** 2. The joint probability factorizes as:

Multiple Linear Regression

1. **Problem Statement:** We are given the hypothesis function for multiple linear regression:

Svm Clarification

1. The user input "SVM" is ambiguous and does not specify a clear math problem. 2. "SVM" commonly stands for Support Vector Machine, a concept in machine learning, which involves o

Evaluation Metrics

1. The problem is to understand the evaluation metrics used to assess model performance in classification tasks. 2. Accuracy measures overall correctness: $$\text{Accuracy} = \frac

Lagrange Regression

1. **Problem statement:** We have a noiseless dataset $D = \{(x_1,y_1), \dots, (x_N,y_N)\}$ with distinct $x_i \in \mathbb{R}$. Define the Lagrange basis functions

Misclassified Explanation

1. Masalah yang ditanyakan adalah bagaimana menentukan jumlah data yang salah klasifikasi (misclassified) dan mengapa bisa bernilai 1. 2. Misclassified data adalah data yang predik