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Master Hyperparameter Tuning in Machine Learning

Towards AI

Explore strategies and practical implementation on tuning an ML model to achieve the optimal performancePhoto by Scott Webb on Unsplash Hyperparameter tuning is a critical step in both traditional machine learning and deep learning that significantly impacts model performance.

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Headroom for AI development

Machine Learning (Theory)

As an example, the speech recognition community spent decades focusing on Hidden Markov Models at the expense of other architectures, before eventually being disrupted by advancements in deep learning. Support Vector Machines were disrupted by deep learning, and convolutional neural networks were displaced by transformers.

AI 157
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Machine Learning Algorithms Explained with Real-World Use Cases

How to Learn Machine Learning

Based on such data, the model learns the mapping of inputs to outputs. Some examples of supervised algorithms are linear regression, logistic regression, support vector machines, and decision trees. DQN is one of the most well-known algorithms in this domain and uses deep-learning-based Q-value function approximation.

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A Gentle Introduction to Principal Component Analysis (PCA) in Python

Flipboard

Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.

Python 132
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Classifiers in Machine Learning

Pickl AI

Examples include: Spam vs. Not Spam Disease Positive vs. Negative Fraudulent Transaction vs. Legitimate Transaction Popular algorithms for binary classification include Logistic Regression, Support Vector Machines (SVM), and Decision Trees. These models can detect subtle patterns that might be missed by human radiologists.

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Binary classification

Dataconomy

Support vector machine (SVM) Support vector machines excel in high-dimensional spaces, making them suitable for complex classification tasks. Additionally, overfitting, where a model learns noise instead of underlying patterns, can lead to poor generalization to unseen data.

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Multiclass classification of thalassemia types using complete blood count and HPLC data with machine learning

Flipboard

This work aims to assess the performance of numerous combinations of machine learning methods to detect alpha and beta-thalassemia in their minor and major types. The analyzed models are K-nearest Neighbor (KNN), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost).