A Primer to the 42 Most Commonly Used Machine Learning Algorithms 1st Edition by Murad Durmus ISBN 9798375226071 8375226076

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ISBN 10: 8375226076
ISBN 13: 9798375226071
Author: Murad Durmus

Misconceptions
Mental shortcuts
Deviations from logical reasoning
We all have them, especially when we are pressed for time, face ambiguity or complexity, or need to process large amounts of data …
By becoming more aware of our human biases, we can work towards making less biased conclusions in all aspects of life. This skill is precious as it enables us to navigate the intricacies of our thoughts and arrive at more informed and fair decisions. Let us utilize our understanding of cognitive bias as a means of personal development and gaining a deeper comprehension of the world around us.
“Let’s learn more about our human biases to make less biased conclusions in the future.”
With The Cognitive Biases Compendium, you can explore over 150 cognitive biases affecting your thinking and behavior. Discover the secrets behind biases like Confirmation Bias and Availability Heuristic, and learn how to use them to make better decisions, solve problems more effectively, and communicate more accurately. This journey into the depths of your mind will open your eyes to a more mindful and insightful life.

A Primer to the 42 Most Commonly Used Machine Learning Algorithms 1st Edition Table of contents:

• ADABOOST

• ADAM Optimization

• Agglomerative Clustering

• ARMA/ARIMA Model

• BERT

• Convolutional Neural Network (CNN)

• DBSCAN

• Decision Tree

• Deep Q-Learning

• EfficientNet

• Factor Analysis of Correspondences

• GAN (Generative Adversarial Network)

• GMM (Gaussian Mixture Model)

• GPT-3

• Gradient Boosting Machine

• Gradient Descent

• Graph Neural Networks

• Hierarchical Clustering

• Hidden Markov Model (HMM)

• Independent Component Analysis

• Isolation Forest

• K-Means

• K-Nearest Neighbour

• Linear Regression

• Logistic Regression

• LSTM (Long Short-Term Memory)

• Mean Shift

• MobileNet

• Monte Carlo Algorithm

• Multimodal Parallel Network

• Naive Bayes Classifiers

• Proximal Policy Optimization

• Principal Component Analysis (PCA)

• Q-Learning

• Random Forests

• Recurrent Neural Network (RNN)

• ResNet

• Spatial Temporal Graph Convolutional Networks

• Stochastic Gradient Descent

• Support Vector Machine (SVM)

• WaveNet

• XGBoost

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Tags: Murad Durmus, 42 Most Commonly, Machine Learning Algorithms