Misery in Vector Breakthrough#2

Misery 大陸版：鋒矢突破#2 - Vector Breakthrough #2

Put Vector in #SonicMovie3?

In Umoupen you can mask vector layers with raster... layers, or raster layers with vector layers, anything can mask or be maskedshow more

Misery loves company 💖 • #misery #lipsync #newpiercing #lippiercing #piercingtok

We are just going to call them Mr. &... Mrs. Misery. Season 2 Part 2 March 19th #BeautyInBlack #BeautyInBlackNetflixshow more

The breakthrough hiding in plain sight. 2.2.26

Without even looking the uncle rushed in and put... himself into miseryshow more

Vector Database by Hand ✍️ Vector databases are revolutionizing... how we search and analyze complex data. They have become the backbone of Retrieval Augmented Generation (#RAG). How do vector databases work? [1] Given ↳ A dataset of three sentences, each has 3 words (or tokens) ↳ In practice, a dataset may contain millions or billions of sentences. The max number of tokens may be tens of thousands (e.g., 32,768 mistral-7b). Process "how are you" [2] 🟨 Word Embeddings ↳ For each word, look up corresponding word embedding vector from a table of 22 vectors, where 22 is the vocabulary size. ↳ In practice, the vocabulary size can be tens of thousands. The word embedding dimensions are in the thousands (e.g., 1024, 4096) [3] 🟩 Encoding ↳ Feed the sequence of word embeddings to an encoder to obtain a sequence of feature vectors, one per word. ↳ Here, the encoder is a simple one layer perceptron (linear layer + ReLU) ↳ In practice, the encoder is a transformer or one of its many variants. [4] 🟩 Mean Pooling ↳ Merge the sequence of feature vectors into a single vector using "mean pooling" which is to average across the columns. ↳ The result is a single vector. We often call it "text embeddings" or "sentence embeddings." ↳ Other pooling techniques are possible, such as CLS. But mean pooling is the most common. [5] 🟦 Indexing ↳ Reduce the dimensions of the text embedding vector by a projection matrix. The reduction rate is 50% (4->2). ↳ In practice, the values in this projection matrix is much more random. ↳ The purpose is similar to that of hashing, which is to obtain a short representation to allow faster comparison and retrieval. ↳ The resulting dimension-reduced index vector is saved in the vector storage. [6] Process "who are you" ↳ Repeat [2]-[5] [7] Process "who am I" ↳ Repeat [2]-[5] Now we have indexed our dataset in the vector database. [8] 🟥 Query: "am I you" ↳ Repeat [2]-[5] ↳ The result is a 2-d query vector. [9] 🟥 Dot Products ↳ Take dot product between the query vector and database vectors. They are all 2-d. ↳ The purpose is to use dot product to estimate similarity. ↳ By transposing the query vector, this step becomes a matrix multiplication. [10] 🟥 Nearest Neighbor ↳ Find the largest dot product by linear scan. ↳ The sentence with the highest dot product is "who am I" ↳ In practice, because scanning billions of vectors is slow, we use an Approximate Nearest Neighbor (ANN) algorithm like the Hierarchical Navigable Small Worlds (HNSW).show more

#BYLER: misery business

MISERY (Reiner, 1990)

ENDLESS MISERY #deltarune

cmon sweet misery

The Vector Feathering TAPE VST Demo is available in... community files. (link below) Riveshow more

Misery loves company . (featuring Rhulk, Nezarec and Calus)... (misery x CPR x Reese's Puffs meme) #Destiny2 #Destiny2Art #Destiny2MOTWshow more

New feature in vector charts this week: We derive... buoy directionality & channel centerlinesshow more