7/
Es este sábado 6/6 en Estación F (Carballo 580)
Anotate acá 👇🏼
https://t.co/osMJdvoJUr
Si te copa, por favor dale RT. Necesitamos que esto llegue a devs, diseñadores, builders, mentores y marcas que quieran bancar algo con impacto real 🙌🏼
Gente, estoy craneando algo y necesito de ustedes 🙏
Quiero traer Halketon a Rosario: una hackathon donde builders, diseñadores y perfiles tech construyen software real para ayudar a 18 ONGs.
Tengo 3 días para armarlo 🫡
Si sos de Rosario y querés construir → Link en el próximo tweet👇🏼
Si querés sponsorear o mentorear → Mis DMs están abiertos!
Cada RT ayuda 🫶🏻
My girlfriend called me at 2am crying. She had seen a photo on Instagram of me and another girl at a party.
She sent me the photo. I looked at it and I'm like, what? Only my nose looks like the guy in the photo! I keep telling her, “We're not the same person,” but she is not ready to accept it.
She then forwarded the photo to my friends asking them to confirm.
Even they were confused. Bro that really does look like you.
Now, at this point, the only hope I have is my last line of defense - a Cosine Similarity Test.
I know you guys are thinking, what the hell is this Cosine Similarity.
Cosine similarity is a mathematical way to measure how similar two things are by treating them as vectors in space. Think of it like measuring the angle between two arrows - the smaller the angle, the more similar they are.
In math, cosine similarity works like this:
cos(θ) = A·B / (|A| × |B|)
Where:
- A·B is the dot product of A and B.
- |A| and |B| are the magnitudes.
Understanding the Scale (-1 to 1):
- cos(0°) = 1 : Perfectly identical
- cos(45°) = 0.7 : Partially similar
- cos(90°) = 0 : No similarity at all
- cos(180°) = -1 : Complete opposites
Now let me prove to my girlfriend that the guy in the photo is not me. Let's say my facial features are Vector A and the guy in the photo is Vector B:
Vector A = [2, 4, 6, 8]
Vector B = [1, 2, 3, 4]
Step 1: Calculate Dot Product
Multiply each corresponding element and add them all up:
A·B = (2×1) + (4×2) + (6×3) + (8×4)
A·B = 2 + 8 + 18 + 32
A·B = 60
Step 2: Calculate Magnitude
Take the square root of the sum of squares of each element:
A = [2, 4, 6, 8]
|A| = √(2² + 4² + 6² + 8²)
|A| = √(4+16+36+64)
|A| = √120
B = [1, 2, 3, 4]
|B| = √(1² + 2² + 3² + 4²)
|B| = √(1+4+9+16)
|B| = √30
|A| × |B| = √120 × √30
|A| × |B| = √3600
|A| × |B| = 60
Step 3: Apply the Formula
cos(θ) = A·B / (|A| × |B|)
cos(θ) = 60 / 60
cos(θ) = 1
Cosine of 1 means perfectly identical.
Congratulations 🎉, you just learned Cosine Similarity.
Bonus:
Why does AI/ML care about cosine similarity?
Recommendation Systems: Netflix uses it to find movies similar to what you have watched.
Image Recognition: AI systems compare feature vectors extracted from images to identify faces or detect similarities between pictures.
Document Classification: Text classification systems use it to categorize emails as spam or not spam by comparing document vectors.
Martes de @Negocios_Arg
Miércoles de @alquimia_hub
Jueves nuevamente de @Negocios_Arg
Qué linda comunidad que se está armando en Argentina! 🙌🏼
Seguro me falten mil comunidades, se aceptan sugerencias en los comentarios 👀
@ThiagoMonechesi Thiago! Fijate que en la comunidad aparecio una chica que hace mkt y hablo algo de ads hoy, se ve que tenia bastante exp, muy interesante lo que comento, nada te dejo la inquietud ahi..
@gonzamartinese Uff tantas cosas por mejorar del sistema digital argentino, estaria bueno armar un hacka con una tematica general para mejorar/repensar este tipo de apps.
Gran oportunidad si estas emprendiendo en el rubro para compartir ideas y tener diferentes puntos de vista, doy fe que te cruzas con gente muy genia!
(Tiro spoiler: tienen reus semanales)
Contanos sobre tu Pyme o proyecto técnico en ingeniería / industria en el canal de Reddit de la comunidad y te mandamos una invitación al grupito de industria que tenemos.
https://t.co/GMFdSjKEGc