OrthoMorph

Superpesimist Risktaker, Superpositioned Ph.D state

[OrthoMorph @OrthoMorph]

สัตว์หิมพานต์และถิ่นที่อยู่ https://t.co/UC7GKXQBzJ

[OrthoMorph @OrthoMorph]

Google Warned Millions Before Quake Hit Venezuela. Can Your Phone Save You https://t.co/8wJ6emLTCT

[Trung Phan @TrungTPhan]

Lee Kuan Yew:

“Air conditioning was a most important invention for us, perhaps one of the signal inventions of history. It changed the nature of civilization by making development possible in the tropics. Without air conditioning you can work only in the cool early-morning hours or at dusk. The first thing I did upon becoming prime minister was to install air conditioners in buildings where the civil service worked. This was key to public efficiency."

[OrthoMorph @OrthoMorph]

อโวคาโดที่โยนเม็ดไว้ตั้งแต่4-5ปีก่อนติดดอกครั้งแรกแล้วตั้งแต่เดือนก่อน พันธุ์อะไรไม่รู้ โยนไปหลากหลาย ไม่รู้ว่าผู้รอดคือใคร ห้าๆๆ เมื่อเช้าก็เพิ่งเห็นโกโก้ที่ปลูกตามยถากรรมติดดอกครั้งที่ต้นมันเต็มเลย ปีนี้น่าตื่นเต้น ต้องลุ้นว่าจะได้ผลบ้างม้ายย

[PyQuant News 🐍 @pyquantnews]

Free code from the book:

Hands-On Machine Learning for Algorithmic Trading

Get it here:

[Quant Science @quantscience_]

Machine learning for quant finance.

In this thread, I'll share 3 months of research on developing predictive ML models in finance.

A thread 🧵

[William | IA @ia_william]

ChatGPT has over 180 million active users

But hardly anyone knows how to make money with it

Here are my 4 simple steps to turn ChatGPT into a cash machine:

[PyQuant News 🐍 @pyquantnews]

Free YouTube channel for deep learning from UC Berkeley University.

Learn deep learning with end-to-end projects:

[Quant Science @quantscience_]

The 8 formulas all algorithmic traders need to know:

H/T @pyquantnews

[Didier 'Dirac's ghost' Gaulin @DiracGhost]

New John Baez just dropped. A 129 pages text on 'what is entropy' and it's for free, big homie.

[Josep Ferrer @iamjosepferrer]

Logistic Regression clearly explained 👇🏻

[Quant Science @quantscience_]

If you're just starting with algorithmic trading in python, you need data.

The fastest way to get data: yfinance.

Let me explain:

[Kirk Borne @KirkDBorne]

#FeatureEngineering in Practice — Approaching (Almost) Any #MachineLearning Problem!
⬇️
Get book: https://t.co/FqeZMf5HCv by @abhi1thakur (4X @Kaggle Grandmaster)
-or-
Download PDF copy: https://t.co/sqoyRtJws8
—————
#BigData #AI #DataScience #DataScientists #DeepLearning #Python

[Valeriy M., PhD, MBA, CQF @predict_addict]

Econometrics was and always will be the original causality.

Econometrics folks like Sir Clive Granger have done it without creating Bayesian Networks and contrived Bayesian assumptions.

Granger also made significant contributions to nonlinear time series analysis and was awarded Nobel prize for his contributions to time series together with Robert Engle.

#timeseries
#econometrics
#machinelearning

[Epic Maps 🗺️ @theepicmap]

Germany: one country, two societies.

[Matt Dancho (Business Science) @mdancho84]

Logistic Regression is the most important foundational algorithm in Classification Modeling. In 2 minutes, I'll teach you what took me 2 months to learn. Let's dive in:

1. Logistic regression is a statistical method used for analyzing a dataset in which there are one or more independent variables that determine a binary outcome (in which there are only two possible outcomes). This is commonly called a binary classification problem.

2. The Logit (Log-Odds): The formula estimates the log-odds or logit. The right-hand side is the same as the form for linear regression. But the left-hand side is the logit function, which is the natural log of the odds ratio. The logit function is what distinguishes logistic regression from other types of regression.

3. The S-Curve: Logistic regression uses a sigmoid (or logistic) function to model the data. This function maps any real-valued number into a value between 0 and 1, making it suitable for a probability estimation. This is where the S-curve shape comes in.

4. Why not Linear Regression? The shape of the S-curve often fits the binary outcome better than a linear regression. Linear regression assumes the relationship is linear, which often does not hold for binary outcomes, where the relationship between the independent variables and the probability of the outcome is typically not linear but sigmoidal (S-shaped).

5. Coefficient Estimation: Like linear regression, logistic regression calculates coefficients for each independent variable. However, these coefficients are in the log-odds scale.

6. Coefficient Interpretation (Log-Odds to Odds): Exponentiating a coefficient converts it from log odds to odds. For example, if a coefficient is 0.5, the odds ratio is exp(0.5), which is approximately 1.65. This means that with a one-unit increase in the predictor, the odds of the outcome increase by a factor of 1.65.

7. Model evaluation: The evaluation metrics for linear regression (like R-squared) are not suitable for assessing the performance of a model in a classification context. For Logistic regression, I normally use classification-specific evaluation metrics like AUC, precision, recall, F1 score, ROC curve, etc.

There you have it- my top 7 concepts on Logistic Regression Models. The next problem you'll face is how to apply data science to business.

I'd like to help.

I’ve spent 100 hours consolidating my learnings into a free 5-day course, How to Solve Business Problems with Data Science. It comes with:

300+ lines of R and Python code
5 bonus trainings
2 systematic frameworks
1 complete roadmap to avoid mistakes and start solving business problems with data science, TODAY.

👉 Here it is for free: https://t.co/YXG4pL97ZN

[Valeriy M., PhD, MBA, CQF @predict_addict]

People often ask about a good book on statistics.

Larry Wasserman is in my opinion one of the best (the best?) statistics professors there . Larry is also the academic ambassador of conformal prediction who was the first to introduce conformal prediction to the leading academia in the USA 🇺🇸.

His statistic book is available open access for free and there is also companion R code for the book.

#statistics #books

[Probability and Statistics @probnstat]

On Principal Component Analysis (PCA)

[Historic Vids @historyinmemes]

Map of the internet fiber optic cables at the bottom of the world's oceans