Handledningar i maskininlärning

Learn feature engineering with this hands-on guide. Explore techniques like encoding, scaling, and handling missing values in Python.

Learn how forward propagation works in neural networks, from mathematical foundations to practical implementation in Python. Master this essential deep learning concept with code examples and visualizations.

Learn how the softmax activation function transforms logits into probabilities for multi-class classification. Compare softmax vs sigmoid and implement in Python with TensorFlow and PyTorch.

Learn about linear regression, its purpose, and how to implement it using the scikit-learn library. Includes practical examples.

Master feature extraction techniques with hands-on Python examples for image, audio, and time series data. Learn how to transform raw data into meaningful features and overcome common challenges in machine learning applications.

Discover the importance of dimensionality reduction, its techniques, and how to apply them to image datasets while visualizing and comparing data in lower-dimensional spaces.

From binary to nuance: explore how fuzzy logic powers intelligent AI systems and mimics human decision-making behavior.

Understand the basics of Reinforcement Learning (RL) and explore the Gymnasium software package to build and test RL algorithms using Python.

Understand the core concepts of Self Organizing Maps and learn how to implement them in Python using MiniSom.

Learn the first principles of Proximal Policy Optimization, including its implementation in PyTorch with Gymnasium!

Learn about Snowflake Cortex AI and how it can be used for LLMs and machine learning.

A guide to understanding and implementing the A* search algorithm in Python. See how to create efficient solutions for complex search problems with practical code examples. Learn optimization strategies used in production environments.