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+--- Thread: Introduction to LLMs Transformer Attention Deepseek pytorch (/Thread-Introduction-to-LLMs-Transformer-Attention-Deepseek-pytorch)
Introduction to LLMs Transformer Attention Deepseek pytorch - AD-TEAM - 05-26-2025
![[Image: 537368816_que-es-udemy-analisis-opiniones.jpg]](https://img100.pixhost.to/images/404/537368816_que-es-udemy-analisis-opiniones.jpg)
1.28 GB | 8min 57s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English
Files Included :
1 - Introduction to Course.mp4 (42.46 MB)
24 - Attention Math Intro.mp4 (4.52 MB)
25 - Attention QueryKeyValue example.mp4 (14.14 MB)
26 - Attention QKV transformer.mp4 (14.14 MB)
27 - Encoded value.mp4 (12.15 MB)
28 - Attention formulae.mp4 (11.26 MB)
29 - Calculate QK transpose.mp4 (26.36 MB)
30 - Attention softmax.mp4 (10.21 MB)
31 - Why multiply by V in attention.mp4 (13.76 MB)
32 - Attention code overview.mp4 (37.96 MB)
33 - Attention code.mp4 (97.14 MB)
34 - Attention code Part2.mp4 (66.49 MB)
35 - Mask self attention.mp4 (24.92 MB)
36 - Mask Self Attention code overview.mp4 (17.68 MB)
37 - Mask Self Attention code.mp4 (28.6 MB)
38 - Encoder decoder transformer.mp4 (25.71 MB)
39 - Types of Transformer.mp4 (10.19 MB)
40 - Multimodal attention.mp4 (14.41 MB)
41 - MultiHead Attention.mp4 (21.86 MB)
42 - MultiHead Attention Code Part1.mp4 (12.28 MB)
43 - Multihead attention code overview.mp4 (12.29 MB)
44 - Multihead attention encoder decoder attention code.mp4 (60.45 MB)
45 - Deepseek R1 training.mp4 (12.98 MB)
46 - Deepseek R1zero.mp4 (23.23 MB)
47 - Deepseek R1 Architecture.mp4 (100.64 MB)
49 - Deepseek R1 paper Intro.mp4 (54.46 MB)
50 - Deepseek R1 Paper Aha moments.mp4 (28.45 MB)
51 - Deepseek R1 Paper Aha moments Part 2.mp4 (23.7 MB)
52 - Deepseek R1 summary.mp4 (8.94 MB)
2 - AI History.mp4 (9.11 MB)
3 - Language as bag of Words.mp4 (12.64 MB)
4 - Word embedding.mp4 (11.63 MB)
5 - Vector Embedding.mp4 (6.56 MB)
6 - Types of Embedding.mp4 (8.84 MB)
7 - Encoding Decoding context.mp4 (16.82 MB)
8 - Attention Encoder Decoder context.mp4 (10.07 MB)
10 - GPT vs Bert Model.mp4 (7.9 MB)
11 - Context length and number of Parameter.mp4 (14.41 MB)
9 - Transformer Architecture with Attention.mp4 (22.55 MB)
12 - Tokenization.mp4 (17.04 MB)
13 - Code Tokenization.mp4 (67.33 MB)
14 - Transformer architecture.mp4 (26.47 MB)
15 - Transformer block.mp4 (24.67 MB)
16 - Decoder Transformer setup and code.mp4 (39.45 MB)
18 - Transformer model code architecture.mp4 (119.2 MB)
19 - Transforme model summary.mp4 (5.81 MB)
20 - Transformer code generate token.mp4 (37.83 MB)
21 - Transformer attention.mp4 (5.79 MB)
22 - Word embedding.mp4 (8.16 MB)
23 - Positional encoding.mp4 (10.66 MB)]
Screenshot
![[Image: w61zLOEP_o.jpg]](https://images2.imgbox.com/56/55/w61zLOEP_o.jpg)
1 - Introduction to Course.mp4 (42.46 MB)
24 - Attention Math Intro.mp4 (4.52 MB)
25 - Attention QueryKeyValue example.mp4 (14.14 MB)
26 - Attention QKV transformer.mp4 (14.14 MB)
27 - Encoded value.mp4 (12.15 MB)
28 - Attention formulae.mp4 (11.26 MB)
29 - Calculate QK transpose.mp4 (26.36 MB)
30 - Attention softmax.mp4 (10.21 MB)
31 - Why multiply by V in attention.mp4 (13.76 MB)
32 - Attention code overview.mp4 (37.96 MB)
33 - Attention code.mp4 (97.14 MB)
34 - Attention code Part2.mp4 (66.49 MB)
35 - Mask self attention.mp4 (24.92 MB)
36 - Mask Self Attention code overview.mp4 (17.68 MB)
37 - Mask Self Attention code.mp4 (28.6 MB)
38 - Encoder decoder transformer.mp4 (25.71 MB)
39 - Types of Transformer.mp4 (10.19 MB)
40 - Multimodal attention.mp4 (14.41 MB)
41 - MultiHead Attention.mp4 (21.86 MB)
42 - MultiHead Attention Code Part1.mp4 (12.28 MB)
43 - Multihead attention code overview.mp4 (12.29 MB)
44 - Multihead attention encoder decoder attention code.mp4 (60.45 MB)
45 - Deepseek R1 training.mp4 (12.98 MB)
46 - Deepseek R1zero.mp4 (23.23 MB)
47 - Deepseek R1 Architecture.mp4 (100.64 MB)
49 - Deepseek R1 paper Intro.mp4 (54.46 MB)
50 - Deepseek R1 Paper Aha moments.mp4 (28.45 MB)
51 - Deepseek R1 Paper Aha moments Part 2.mp4 (23.7 MB)
52 - Deepseek R1 summary.mp4 (8.94 MB)
2 - AI History.mp4 (9.11 MB)
3 - Language as bag of Words.mp4 (12.64 MB)
4 - Word embedding.mp4 (11.63 MB)
5 - Vector Embedding.mp4 (6.56 MB)
6 - Types of Embedding.mp4 (8.84 MB)
7 - Encoding Decoding context.mp4 (16.82 MB)
8 - Attention Encoder Decoder context.mp4 (10.07 MB)
10 - GPT vs Bert Model.mp4 (7.9 MB)
11 - Context length and number of Parameter.mp4 (14.41 MB)
9 - Transformer Architecture with Attention.mp4 (22.55 MB)
12 - Tokenization.mp4 (17.04 MB)
13 - Code Tokenization.mp4 (67.33 MB)
14 - Transformer architecture.mp4 (26.47 MB)
15 - Transformer block.mp4 (24.67 MB)
16 - Decoder Transformer setup and code.mp4 (39.45 MB)
18 - Transformer model code architecture.mp4 (119.2 MB)
19 - Transforme model summary.mp4 (5.81 MB)
20 - Transformer code generate token.mp4 (37.83 MB)
21 - Transformer attention.mp4 (5.79 MB)
22 - Word embedding.mp4 (8.16 MB)
23 - Positional encoding.mp4 (10.66 MB)]
Screenshot
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