SenseCraft AI is a free, web-based platform designed for beginners, focusing on a no-code approach and application-orientation to simplify and accelerate the creation of AI applications.
This article explores the application of Laplace approximated Bayesian optimization for hyperparameter tuning, focusing on regularization techniques in machine learning models. The author discusses the challenges of hyperparameter optimization, particularly in high-dimensional spaces, and presents a case study using logistic regression with L2 regularization. The article compares grid search and Bayesian optimization methods, highlighting the advantages of the latter in efficiently finding optimal regularization coefficients. It also explores the potential for individualized regularization parameters for different variables.
The article discusses the credibility of using Random Forest Variable Importance for identifying causal links in data where the output is binary. It contrasts this method with fitting a Logistic Regression model and examining its coefficients. The discussion highlights the challenges of extracting causality from observational data without controlled experiments, emphasizing the importance of domain knowledge and the use of partial dependence plots for interpreting model results.
The article discusses methods for data scientists to answer 'what if' questions regarding the impact of actions or events without having conducted prior experiments. It focuses on creating counterfactual predictions using machine learning techniques and compares a proposed method with Google's Causal Impact. The approach involves using historical data and control groups to estimate the effect of modifications, addressing challenges such as seasonality, confounders, and temporal drift.
This article provides an overview of feature selection in machine learning, detailing methods to maximize model accuracy, minimize computational costs, and introduce a novel method called History-based Feature Selection (HBFS).
Researchers from the University of California San Diego have developed a mathematical formula that explains how neural networks learn and detect relevant patterns in data, providing insight into the mechanisms behind neural network learning and enabling improvements in machine learning efficiency.
Creativity and a Jetson Orin Nano Super can help hobbyists build accessible robots that can reason and interact with the world. The article discusses building a robot using accessible hardware like Arduino and Raspberry Pi, eventually upgrading to more capable hardware like the Jetson Orin Nano Super to run a large language model (LLM) onboard.
Discussion on the challenges and promises of deep learning for outlier detection in various data modalities, including image and tabular data, with a focus on self-supervised learning techniques.
An explanation of the differences between encoder- and decoder-style large language model (LLM) architectures, including their roles in tasks such as classification, text generation, and translation.
A detailed explanation of the Transformer model, a key architecture in modern deep learning for tasks like neural machine translation, focusing on components like self-attention, encoder and decoder stacks, positional encoding, and training.
