Si te comes el cuento de que llevar unos balones fue un error, déjame decirte que los zurdos te comen fácil.
Si los balones fueran lo único que se hubiera llevado, lo entendería.
Pero no, fue comida, medicina, comunicaciones.
Nunca se dejen imponer relato de nadie.
🇨🇴 $201.637.565.026 es la cifra con la que cerramos el #CECANDI en el marco de la campaña #EmpresasUnidasPorColombia.
Un aporte que refleja la solidaridad y el compromiso del sector productivo con las familias y regiones afectadas por el terremoto.
🤝 Gracias a las empresas y aliados que se han sumado a esta iniciativa. Este es el poder de trabajar unidos por Colombia.
#MásPaísANDI
El planeta no quiere castigar a los seres humanos. Ni da gritos. Ni está mandando mensajes. Ni hace juicios morales. Este tipo de "animismo" (ahora en boga) es una farsa, producto sobre todo del oportunismo político.
"El efecto Dunning-Krueger: "Las personas con baja calificación llegan a conclusiones equivocadas y fallan en las decisiones, pero no pueden darse cuenta de sus errores debido a su baja calificación".
No comprender los errores conduce a creer en uno mismo y, por tanto, a
FASCINANTE PLUTÓN
En 2006, Plutón perdió su estatus de "planeta". Pero este mundo sigue siendo uno de los más fascinantes.
Así nos lo demostró la New Horizons (abajo).
Pero ¿Por qué ya no es un planeta?
¿Sabías que existen gemelos de Plutón?
¿Podría albergar vida?
Hilo 👇🏼
Overfitting (Lesson 4 of 24): It took me 6 weeks to learn overfitting. I'll share in 6 minutes (business case study included). Let's dive in:
1. Overfitting is a common issue in machine learning and statistical modeling. It occurs when a model is too complex and captures not only the underlying pattern in the data but also the noise.
2. Key Characteristics of Overfitting: High Performance on Training Data, Poor Performance on Test Data, Overly Complex with many parameters, Sensitive to minor fluctuations in training data (not robust).
3. How to Avoid Overfitting (and Underfitting): The goal is to get a model trained to the point where it's robust (not overly sensitive) and generalizes well to new data (unseen during model training). How we do this is to balance bias and variance tradeoff. Common techniques: K-Fold Cross Validation, Regularization (penalizing features), and even simplifying the model.
4. How I learned about overfitting (business case): I was making a forecast model using linear regression. The model had dozens of features: lags, external regressors, economic features, calendar features... You name it, I included it. And the model did really well (on the training data). The problem came when I put my first forecast model into production...
5. Lack of Stability (is a nice way to put it): My model went out-of-wack. The linear regression predicted demand for certain products 100X more than it's recent trends. And luckily the demand planner called me out on it before the purchase orders went into effect.
6. I learned a lot from this: Linear regression models can be highly sensitive. I switched to penalized regression (elastic net) and the model became much more stable. Luckily my organization knew I was onto something, and I was given more chances to improve.
7. The end result: We actually called the end of the Oil Recession of 2016 with my model, and workforce planning was ready to meet the increased demand. This saved us 3 months of inventory time and put us in a competitive advantage when orders began ramping up.
Estimated savings: 10% of sales x 3 months = $6,000,000.
Pretty shocking what a couple data science skills can do for a business.
===
Ready to learn Data Science for Business?
I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://t.co/LR39RJ5XKB
And if you'd like to speed it up, I have a live workshop where I'll share how to use ChatGPT for Data Science: https://t.co/EaMpKrJiqX
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Un día como hoy, en 1867, se fundó la Universidad Nacional de Colombia.
“Inter Aulas Academiæ Quære Verum”
«Busca la verdad en las aulas de la Academia».
Varias Universidades reportan un descenso alarmante en la cantidad de aspirantes para el periodo académico 2022-2.
En la Universidad Distrital por ejemplo (cuyas inscripciones no han cerrado) hay carreras donde nisiquiera se cubren los cupos con los aspirantes.