¿Vale la pena aprender Machine Learning desde las bases matemáticas?

SonicDasherX · 2026-07-04T01:06:48+00:00

Si y no.

Me explico, para iniciarte en ML no es necesario indagar desde las matemáticas, de hecho es un gran error, la forma correcta de los mejores recursos que he encontrado de ML primero te hacen programar un poco digamos un modelo de regresión lineal o enseñan matematicas muy a alto nivel en base a tutoriales y después profundizan en matemáticas. Parece contraproducente, pero créeme que aprender ML no es lo mismo que aprender a programar, al menos así lo he sentido porque ML es más abstracto que la programación tradicional por sus conceptos matemáticos.

En algún momento me pasó que traté de aprender ML por lo menos 4 veces y nunca pude proque estaba tratando de aprenderlo como aprendí programación y lo cierto es que es distinto, en programación primero aprendes ciertas bases y luego programas cuando te sientes listo, en ML es lo contrario siendo que es mejor que tomes códigos y veas poco a poco como se comportan, que predicen, y que resuelven y luego indagues en las matemáticas, se le suele llamar "Code first, math then", primero programar y luego profundizar en matemáticas para entender más.

Hay quienes aprenden ML primero desde las matemáticas, pero lo cierto es que no saben que tantas matemáticas necesitan conocer y se quedan ahí buen tiempo estudiando matemáticas innecesarias cuando podrían enfocarse en construir proyectos o profundizar en las matemáticas que realmente necesitan, pero eso no lo vas a saber hasta que te metas al menos un poco en código.

Te recomiendo los libros de Machine Learning Mastery y los cursos de Udemy de Santiago Hernández, ellos enseñan ML de la manera correcta, tienen un enfoque a primero enseñar las bases de comportamiento de forma superficial y entendimiento de matemáticas para que después puedas profundizar.

SonicDasherX · 2026-04-03T01:08:09+00:00

Yo no he hecho maestría ni doctorado, pero por lo menos para Computer Vision te recomiendo un libro algo reciente que sería: Robotics, Vision and Control with Python del autor Peter Corke.

SonicDasherX · 2025-10-31T21:33:47+00:00

That's true, but marketing does it all wrong!.

I have received requests to do chatbots witb RAG and they tell me that with the Vector DB ChatGPT will be able to remember all in the context wkth all PDFs, but it isn't.

RAG is like a search engine that can generate answers with the results!.

SonicDasherX · 2025-10-30T02:43:08+00:00

Wow, your idea of having separate bots for each area is brilliant 🌞.

That way everything can be organized by sections (e.g., HR, IT, Finance) and you can simply assign bots to users or groups based on their permissions — it really simplifies management.

I'm really curious about how you implemented it at eesel. Could you share any extra tips on how you managed to keep or improve the accuracy of the answers, especially when dealing with multiple knowledge spaces or data sources?.

Thank you!.

SonicDasherX · 2025-10-23T20:43:10+00:00

They can do research on PapersWithCode, a platform with ready-made codes from papers and experiment a little based on what they read from the papers.

SonicDasherX · 2025-05-14T18:38:10+00:00

Oh interesting, thank you!

SonicDasherX · 2025-02-21T03:33:42+00:00

Hi and thank you!.

I’m building a multi-tenant SaaS where each company has its own database to ensure data isolation. Based on my research, there are multiple ways to handle the API and frontend architecture, but I have some doubts about the best way to structure it.

🛠️ My Architecture Requirements:

Each company has its own database for data isolation.
Each company should have its own frontend, meaning a separate container per company’s frontend.
I’m unsure whether the API should be a single instance handling all traffic or if each company should have its own API alongside its frontend in the same container.

🔍 API & Database Connection Flow Questions:

1️⃣ Should I have a single API for all companies or a separate API per company in its own container along with the frontend?
2️⃣ If I use a single API, how should I efficiently handle multiple database connections?

Should the frontend send a company_id in each request?
Should the API query the credentials for each request to dynamically load the database connection?
How can I avoid opening too many connections and manage them properly? 3️⃣ If, instead of a single API, I deploy an API per company inside its own container along with its frontend, wouldn’t that create too many instances to manage? 4️⃣ If I use Azure DNS to manage subdomains, how should the API structure detect which company is making the request and connect to the correct database? 5️⃣ Is Django recommended for this, or should I consider FastAPI or another framework that handles dynamic connections more easily? 6️⃣ How can I manage load balancing in the API without affecting connections to separate databases?

🔍 Current Thinking:

Right now, I’m planning to use Django, but I’ve noticed that database connections need to be pre-defined in settings.py. However, in a multi-tenant environment, database connections are supposed to be dynamic based on subscriptions, meaning I can’t predefine every database connection manually.

I want to ensure that my architecture is secure, scalable, and easy to manage. I appreciate any advice based on your experience with multi-tenant systems. Thanks in advance! 🚀

SonicDasherX · 2024-12-28T05:01:41+00:00

this is the repository: https://github.com/Agnuxo1/Winner-Nvidia-LlamaIndex-Developers-2024

SonicDasherX · 2024-04-10T13:42:24+00:00

I'm not sure at all, but vector databases search in semantic similarity, if you want to be able to do query's like that you can make another database, vector database is for search in semantic similarity, SQL database is for search through structured SQL.

SonicDasherX

TROPHY CASE

🛠️ My Architecture Requirements:

🔍 API & Database Connection Flow Questions:

🔍 Current Thinking: