⚡ Attention all software developers and programmers! MITRE's Top 25 list of dangerous software weaknesses for 2023 is here.
Discover the crucial mistakes to avoid early in your product development process:
https://t.co/yO6N7oon2L
Build secure software from the ground up!
Para aquellos que esten preocupados por el #ransomware en su organización les recomendaria revisar lo siguente (de autores de los cis controls). Indica un grupo de subcontroles enfocados en este tipo de amenazas para org. con poca experiencia en cibersec https://t.co/6okrcIKuvQ
En ciberseguridad, ¿La falta de un control es una vulnerabilidad?, tuve una interesante conversación con chat GPT 4, y llegamos a la conclusión de que no lo es. ¿alguna otra opinión?
Esto de buscar siempre culpables de los problemas actuales resulta agotador y desesperanzador. Así no vamos a sentarnos a resolverlos nunca!
Me siento en el Titanic hundiéndose y la tripulación peleando para definir quién tuvo la culpa!!!
Los nuevos asistentes de AI son herramientas que nos permitirán desarrollar proyectos aún más complejos en cada una de nuestras disciplinas. El que sea reemplazado por estas máquinas, es por que no se ha preocupado de aprender a utilizarlas.
Machine learning competitions are often a good indicator of what techniques actually work well in practice on new datasets.
The very comprehensive State of Competitive Machine Learning 2022 report just came out and contained many interesting and surprising insights!
1) As expected, transformers dominate natural language processing (NLP). ALL NLP-related winning solutions used transformers.
2) Convolutional neural networks still dominate computer vision. And EfficientNet is the most popular pretrained architecture for computer vision -- most people finetune pretrained models rather than training from scratch.
3) Almost twice as many winning solutions used k-fold CV instead of a fixed validation set.
4) Kaggle (barely) remains the most popular competition platform.
5) Almost everyone uses Python.
6) Out of 46 winning solutions using deep learning, 44 used PyTorch, and only 2 used TensorFlow.
7) A big surprise for tabular competitions: the reign of XGBoost seems over. While gradient boosting still wins most tabular competitions, LightGBM is now the preferred approach, with CatBoost coming in second. XGBoost is third.
8) Winning solutions of 7 out of the 10 tabular competitions used gradient boosting, 5 out of 10 used deep neural networks (implemented in PyTorch), and most winning solutions were ensemble methods.
Here's a link to the full report: https://t.co/p9dleTZ80R
De acuerdo a ChatGPT, algunas de las brechas importantes de Chile para ser un pais desarrollado son: Educación, Infraestructura, Innovación y tecnología, desigualdad económica y sostenibilidad ambiental.
A thread on designing poster presentations. I love poster design because there are so many good ways to make a poster. But like any presentation, simple design strategies can optimize communication. Compiled by @__Matt_Carter__ . 🧵1/21
After 5 years of work, security.txt is officially an RFC. I am pleased to announce RFC 9116: https://t.co/uIqSRo28ak.
I would like to use this opportunity to thank those who made this possible. Thank you. ❤️
👉 Why is Twisted Pair cabling the most popular type of Network Cabling?
The first blog post I ever wrote was a deep dive into Ethernet. In honor of that, my first Twitter Thread is going to be a subset of that article which answers the question above.
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#utp#networking
Este año estoy dictando dos cursos en el Master of #DataScience en la @fcfm_Uchile y quiero compartir el material que hemos generado: Pequeño hilo a continuación para los cursos de #MachineLearning y Estadística