正在加载视频...

视频加载失败

返回首页

Misery in Vector Breakthrough#2

AK_husbandoTP's profile picture

Arknights Husbando: Thirstposting

6,952 subscribers

121,647 次观看 • 2 个月前 •via X (Twitter)

喜剧游戏
LIVE

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Misery 大陸版：鋒矢突破#2 - Vector Breakthrough #2
0:13

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

oyuki ❄️

412,420 次观看 • 7 个月前

Put Vector in #SonicMovie3?
0:11

Put Vector in #SonicMovie3?

dhim

14,519 次观看 • 1 年前

Misery loves company 💖 • #misery #lipsync #newpiercing #lippiercing #piercingtok
0:10

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

𝕸𝖎𝖘𝖙𝖞 𝕴𝖉𝖊

114,464 次观看 • 28 天前

We are just going to call them Mr. & Mrs. Misery. Season 2 Part 2 March 19th #BeautyInBlack #BeautyInBlackNetflix
0:19

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

Beauty In Black

53,421 次观看 • 6 个月前

The breakthrough hiding in plain sight. 2.2.26
0:12

The breakthrough hiding in plain sight. 2.2.26

TaylorMade Golf

92,376 次观看 • 8 个月前

Without even looking the uncle rushed in and put himself into misery
0:15

Without even looking the uncle rushed in and put himself into misery

D R A V E N

37,921 次观看 • 9 个月前

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).
0:17

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).

Tom Yeh

192,022 次观看 • 2 年前

#BYLER: misery business
0:17

#BYLER: misery business

fred 🇵🇸❀

15,844 次观看 • 8 个月前

MISERY (Reiner, 1990)
0:14

MISERY (Reiner, 1990)

Daily Squibs

39,945 次观看 • 10 个月前

cmon sweet misery
0:10

Sensitive content

cmon sweet misery

eve ૮ . . ྀིა⁩

24,799 次观看 • 8 个月前

ENDLESS MISERY #deltarune
0:19

ENDLESS MISERY #deltarune

audio 🎁

142,052 次观看 • 2 个月前

The Vector Feathering TAPE VST Demo is available in community files. (link below) Rive
0:12

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

Dimitri Novikov 🇺🇦

16,425 次观看 • 1 年前

New feature in vector charts this week: We derive buoy directionality & channel centerlines
0:16

New feature in vector charts this week: We derive buoy directionality & channel centerlines

Chris Dalke

17,108 次观看 • 3 个月前

Misery loves company . (featuring Rhulk, Nezarec and Calus) (misery x CPR x Reese's Puffs meme) #Destiny2 #Destiny2Art #Destiny2MOTW
0:14

Misery loves company . (featuring Rhulk, Nezarec and Calus) (misery x CPR x Reese's Puffs meme) #Destiny2 #Destiny2Art #Destiny2MOTW

Soul! (DM for commission)

247,706 次观看 • 3 年前

Offset vector paths now available in Figma Draw. You can now take any flattened vector path and offset it to expand or retract it. It even lets you round out those sharp edges.
0:12

Offset vector paths now available in Figma Draw. You can now take any flattened vector path and offset it to expand or retract it. It even lets you round out those sharp edges.

miggi from figgi

55,585 次观看 • 1 年前