Armando

Canto feo, sin sentimientos, pero bien recio. 🇲🇽

[Joyce Meyer @JoyceMeyer]

The joy of the Lord is our strength (Nehemiah 8:10). But what is the joy of the Lord? It is being glad in our hearts because of what Jesus has done for us. Although forgiving our sins is at the top of the list, He does countless other things for us, many of which we don’t even notice. Think about these:

• He protects us (Psalm 121:7–8).
• He is patient with us (2 Peter 3:9).
• He is with us all the time (Psalm 139:7).
• He helps us (Isaiah 41:10).
• He loves us unconditionally (Romans 5:8).
• He accepts us and never rejects us (John 6:37).
• He heals us in our broken places (Psalm 147:3).
• He gives us hope (Jeremiah 29:11).
• And He does thousands of other things.

[Armando @armandochagoya]

@_axtone Gracias, valiosa información

[Miguel Ángel Durán @midudev]

¡Certificaciones gratis en IA, Programación y Cloud por tiempo limitado! Hasta el 31 de octubre. De Oracle ↓

[HyruleMuse ▲✨ @_axtone]

Recursos gratuitos para aprender sobre arquitectura y diseño de software 🧵🏗️

[Armando @armandochagoya]

@angelagarcia92 @Bibliomaniatico Simplemente no es el tiempo correcto

[Armando @armandochagoya]

@DanielHabif 😂

[Brais Moure @MoureDev]

Sorteo 5 libros de Git y GitHub desde cero para celebrar su segundo aniversario!

✅ Sígueme
❤️ Dale Like
🔁 Comparte

🌐 Válido para todo el mundo. El viernes 18 publico el resultado.

[Armando @armandochagoya]

@mkfsfedora Me rifo, justo hoy estaba leyendo la documentación para entrarle a git, me interesa y, mucho

[DANIEL HABIF @DanielHabif]

Mateo 22:39... Amarás a tu prójimo como a ti mismo.

- ¿También a los que tienen tatuajes?

¡Si! Incluso a los que hacen preguntas ridículas.

[Miguel Ángel Durán @midudev]

¡Aprende Inglés sin gastar dinero!

De 0 → https://t.co/p1mQ1fPPE7
Speaking → https://t.co/hHm1aFUvT9
Gramática → https://t.co/GwhjcT64Vo
Con películas → https://t.co/Fw10X9SPh4
Cambridge → https://t.co/rPCBO0RSsD
Listening → https://t.co/L4yBoAlkka

[Aurimas Griciūnas @Aurimas_Gr]

Fundamentals of a 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲.

With the rise of GenAI, Vector Databases skyrocketed in popularity. The truth is that a Vector Database is also useful for different kinds of AI Systems outside of a Large Language Model context.

When it comes to Machine Learning, we often deal with Vector Embeddings. Vector Databases were created to perform specifically well when working with them:

➡️ Storing.
➡️ Updating.
➡️ Retrieving.

When we talk about retrieval, we refer to retrieving set of vectors that are most similar to a query in a form of a vector that is embedded in the same Latent space. This retrieval procedure is called Approximate Nearest Neighbour (ANN) search.

A query here could be in a form of an object like an image for which we would like to find similar images. Or it could be a question for which we want to retrieve relevant context that could later be transformed into an answer via a LLM.

Let’s look into how one would interact with a Vector Database:

𝗪𝗿𝗶𝘁𝗶𝗻𝗴/𝗨𝗽𝗱𝗮𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮.

1. Choose a ML model to be used to generate Vector Embeddings.
2. Embed any type of information: text, images, audio, tabular. Choice of ML model used for embedding will depend on the type of data.
3. Get a Vector representation of your data by running it through the Embedding Model.
4. Store additional metadata together with the Vector Embedding. This data would later be used to pre-filter or post-filter ANN search results.
5. Vector DB indexes Vector Embedding and metadata separately. There are multiple methods that can be used for creating vector indexes, some of them: Random Projection, Product Quantization, Locality-sensitive Hashing.
6. Vector data is stored together with indexes for Vector Embeddings and metadata connected to the Embedded objects.

𝗥𝗲𝗮𝗱𝗶𝗻𝗴 𝗗𝗮𝘁𝗮.

7. A query to be executed against a Vector Database will usually consist of two parts:

➡️ Data that will be used for ANN search. e.g. an image for which you want to find similar ones.
➡️ Metadata query to exclude Vectors that hold specific qualities known beforehand. E.g. given that you are looking for similar images of apartments - exclude apartments in a specific location.

8. You execute Metadata Query against the metadata index. It could be done before or after the ANN search procedure.
9. You embed the data into the Latent space with the same model that was used for writing the data to the Vector DB.
10. ANN search procedure is applied and a set of Vector embeddings are retrieved. Popular similarity measures for ANN search include: Cosine Similarity, Euclidean Distance, Dot Product.

How are you using Vector DBs? Let me know in the comment section!

#LLM #AI #MachineLearning

[Brais Moure @MoureDev]

Para que te hagas un cuadro.

[Brais Moure @MoureDev]

¿Ya te has apuntado?

La próxima semana comenzamos el curso de Java desde cero.

🗓️ En directo: Miércoles 2 a las 20h (España)

Te dejo el horario y el repo ↓

[S4vitar @S4vitar]

He encontrado este canal y la verdad que está genial de cara a los interesados en Bug Bounty.

El chaval graba el PoC de la vulnerabilidad y la cantidad que le pagaron por ello. Está genial para hacerse la idea de ataques en páginas web reales.

https://t.co/VcAqDtYWlb

[Armando @armandochagoya]

@_axtone Chocalas 🤜🏼

[Armando @armandochagoya]

@MoureDev ¡Hola!

[Héctor de León (El loco de los perros) ⛧ @powerhdeleon]

PHP es un lenguaje de programación con el cual he trabajado sin descanso desde el 2004.

Es el lenguaje que utilizo en el 90% del backend de frakcio.

He decidido liberar un curso GRATIS de introducción a PHP.

Puede dar RT, puede servir a uno de tus seguidores.

1/2

[Erick Gutiérrez @Erickisback1]

Una explicación clara y concisa del porqué es bueno enseñar Náhuatl en las escuelas y lo ridículo que resultan los “argumentos” en contra, por @nochaveznada, experto lingüista