लक्ष्य निर्धारित कर सम्यकत्व के लिए पुरुषार्थ करेंगे तो मोक्ष का मार्ग मिलेगा- पूज्य श्री अतिशयमुनिजी म.सा.
लक्ष्य निर्धारित कर सम्यकत्व के लिए पुरुषार्थ करेंगे तो मोक्ष का मार्ग मिलेगा- पूज्य श्री अतिशयमुनिजी म.सा.
Posts
Machine Learning MCQs - Basics & Supervised Learning
Machine Learning MCQs - Basics & Supervised Learning Machine Learning MCQs machine Learning mcqs Basics & Supervised Learning (1-100) 1. What is the main goal of supervised learning? A. To group data without labels B. To discover hidden patterns C. To learn a mapping from inputs to known outputs D. To minimize entropy Answer: C Supervised learning uses labeled data to train models to predict outcomes based on input. 2. Which of the following is a supervised learning algorithm? A. K-means B. Linear Regression C. PCA D. DBSCAN Answer: B Linear Regression is a supervised learning algorithm that models the relationship between inputs and outputs. 3. What is the difference between classification and regression? A. Classification predicts continuous output, regression predicts categories B. Classification predicts categories, regression predicts continuous out...
What is Dropout? How does it prevent overfitting in a neural network?
What is Dropout? How does it prevent overfitting in a neural network?
What is Confusion Matrix and Advanced Classification Metrics?
What is Confusion Matrix and Advanced Classification Metrics?
Python Web Development Frameworks That You Should Know!
Python Web Development Frameworks That You Should Know!
Solving the Travelling Salesman Problem with MiniSom hands on practical
Solving the Travelling Salesman Problem with MiniSom
The Central Limit Theorem, a hands-on introduction
The Central Limit Theorem, a hands-on introduction
Solving a regression problem using a Sequential Neural Network Model in Keras
Solving a regression problem using a Sequential Neural Network Model in Keras
Preprocessing of raw data in NLP: Tokenization, Stemming, Lemmatization and Vectorization
Preprocessing of raw data in NLP:
clustering methods in machine learning
Different Clustering Methods Hierarchical Methods Agglomerative hierarchical clustering — Each object initially represents a cluster of its own. Then clusters are successively merged until the desired cluster structure is obtained. Divisive hierarchical clustering — All objects initially belong to one cluster. Then the cluster is divided into sub-clusters, which are successively divided into their own sub-clusters. This process continues until the desired cluster structure is obtained. The hierarchical clustering methods could be further divided according to the manner that the similarity measure is calculated Single-link clustering (also called the connectedness, the minimum method or the nearest neighbor method) — methods that consider the distance between two clusters to be equal to the shortest distance from any member of one cluster to any member of the other cluster. If the data consist of similarities, the similarity between a pair of clusters is considered to be eq...