Nigel Haywood - Director’s Post

Learn how mathematical models are defined and how they relate to analytical schematics. https://t.co/C5evyFxzsU #fluidpower https://lnkd.in/dDQat_pW #SAFPA#fluidpower

Digital Discovery Issue 4 now available! https://lnkd.in/eRF3R3EB In this issue: Bayesian optimisation for reaction additives, “shallow learning” of nitrogen reduction, a Tutorial Review on energy landscapes for machine learning, and much more! #openaccess

How can you develop several datasets to aid in the development of bin-picking algorithms? You can identify suitable picking surfaces for a vacuum cup, which are defined by some relatively simple rules. However, these rules are almost impossible to consistently apply manually. Read the full case of how we enabled the development of novel algorithms through synthetic data: https://lnkd.in/gaxauMuN #syntheticdata #algorithms #datadrivensolutions

Just made my very first Machine Learning Model using a DecisionTreeClassifier. It uses a FLC sensor to detect magnetic distortion underground to determine if there is a landmine underground or not. It takes Inputs:Voltage from Sensor,Height of sensor,Soil Type. Outputs:If there is a mine or not. The Code is available at:https://lnkd.in/dJYzDmv9 The dataset used is:https://lnkd.in/dQrex9_E There is a Command Line interface and a GUI interface available.The model is also included for anyone to use.

How do SVMs (support vector machines) work under the hood? Rukshan Pramoditha's in-depth guide explains their inner workings in the context of linear and non-linear classification and regression tasks.

🚀 Ever wondered how to truly gauge the effectiveness of your machine learning models? 🤔 In this video lecture, we dive deep into the ROC Curve – your secret weapon for understanding model performance beyond simple accuracy. 🧐 🔍 We'll Cover: The ROC Curve: Unraveling its origins in signal detection theory and why it's a game-changer. 📡 True Positive Rate (TPR) & False Positive Rate (FPR): Deciphering sensitivity, recall, fall-out, and specificity. 🤓 True Negative Rate (TNR): Understanding its connection to specificity. ✅ Precision/Recall vs. ROC Curve: Knowing when to use each for optimal insights. 📊 💡 Get Ready To: Master the ROC Curve: Gain the confidence to interpret and utilize this powerful tool. 💪 Unlock Model Insights: Go beyond accuracy and understand the trade-offs between different performance metrics. ⚖️ Make Informed Decisions: Choose the right evaluation metric for your specific use case. 🎯 #MachineLearning #DataScience #ROC #ModelEvaluation #Accuracy #Precision #Recall #Specificity #Sensitivity #DataAnalysis #Statistics https://lnkd.in/dRkgXNNU

Fourier Analysis Part 5: Recursive Least Squares Based Trigonometric Fourier Series We can create Fourier series using recursive least squares. Here, our problem is the orthonormal basis decomposition with given incremental data. It is possible to design a time-varying transformation or adaptive filter by incrementally constructing Fourier series. MathWorks Code: https://lnkd.in/dbesP7JF

I just published a post ------ 'CFD and Machine Learning Part 1: A Simple Heat Transfer Model'