This article introduces how to use Python and the PhiData library to build a simple AI agent. This agent can interact with a simulated environment (including temperature and humidity), and make decisions based on real-time sensor data. Key points Define a simple environment class, simulating changes in temperature and humidity. Create an AI agent class, … Read more
Abstract arXiv:2310.08278v3 [cs.LG] February 8, 2024 Original paper link: https://arxiv.org/pdf/2310.08278 In recent years, foundation models have caused a paradigm shift in the field of machine learning due to their unprecedented zero-shot and few-shot generalization capabilities. However, despite the success of foundation models in fields such as natural language processing and computer vision, the development of … Read more
Guest Blog Author: Dr. Erwin John T. Carpio As a physician and radiologist, I have always wanted to learn and develop machine learning models and apply them to my field. However, machine learning felt like a foreign language to me, and with my limited programming experience and non-computer science background, I thought it was challenging … Read more
Written by / TensorFlow Model Optimization Team We are pleased to announce the release of the Quantization Aware Training (QAT) API, which is part of the TensorFlow Model Optimization Toolkit. With QAT, you can leverage the advantages of quantization in performance and size while maintaining accuracy close to the original. This work is part of … Read more
Written by / Li Xihan, Google Developers Expert This article is excerpted from “Simple and Rough TensorFlow 2.0” TensorBoard: Visualizing the Training Process Sometimes, you want to observe the changes of various parameters during the model training process (for example, the value of the loss function). While you can check this through command line output, … Read more
XGBoost is well-known as a powerful tool in data science competitions and is widely used in the industry. This article shares a collection of frequently asked interview questions about XGBoost that I have compiled over the years, hoping to deepen everyone’s understanding of XGBoost and, more importantly, to provide some assistance when seeking opportunities. 1. … Read more
This article is a part of Chapter 10 of the book “Introduction to Machine Learning Basics” (by Huang Haiguang). XGBoost Algorithm XGBoost is a machine learning algorithm based on the Gradient Boosting Decision Tree (GBDT) invented in February 2014 by PhD student Chen Tianqi from the University of Washington. This algorithm not only has excellent … Read more
Click the top to follow us! XGBoost Feature Engineering: From Beginner to Expert Recently, I’ve been diving into XGBoost and found that feature engineering is indeed a profound subject. This thing can be said to be the lifeline of model performance; if done poorly, all efforts can be in vain. Today, let’s chat about the … Read more
Hello everyone! Niu Ge is back! Today we are going to talk about a very powerful machine learning library – XGBoost. When it comes to it, we must mention its dominance in major data competitions. It’s like the “timely rain Song Jiang” in the machine learning world, always able to help you improve model performance … Read more
↓Recommended Attention↓ Original: Combining Price Momentum and Crowding for Margin ETF Trading Strategies Source: China Galaxy Securities Research Institute 1. Introduction This article mainly introduces the trading strategies for margin ETFs. As an index tracking tool, ETFs have good asset allocation value. At the same time, by combining price diffusion and Minsky moments, the advantages … Read more