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⭕ Reinforcement Learning: • Reinforcement Learning...
Natural Language Processing: • Natural Language Proce...
⭕ Transformers from Scratch: • Natural Language Proce...
⭕ ChatGPT Playlist: • ChatGPT
⭕ Convolutional Neural Networks: • Convolution Neural Net...
⭕ The Math You Should Know : • The Math You Should Know
⭕ Probability Theory for Machine Learning: • Probability Theory for...
⭕ Coding Machine Learning: • Code Machine Learning
MATH COURSES (7 day free trial)
📕 Mathematics for Machine Learning: imp.i384100.net/MathML
📕 Calculus: imp.i384100.net/Calculus
📕 Statistics for Data Science: imp.i384100.net/AdvancedStati...
📕 Bayesian Statistics: imp.i384100.net/BayesianStati...
📕 Linear Algebra: imp.i384100.net/LinearAlgebra
📕 Probability: imp.i384100.net/Probability
OTHER RELATED COURSES (7 day free trial)
📕 ⭐ Deep Learning Specialization: imp.i384100.net/Deep-Learning
📕 Python for Everybody: imp.i384100.net/python
📕 MLOps Course: imp.i384100.net/MLOps
📕 Natural Language Processing (NLP): imp.i384100.net/NLP
📕 Machine Learning in Production: imp.i384100.net/MLProduction
📕 Data Science Specialization: imp.i384100.net/DataScience
📕 Tensorflow: imp.i384100.net/Tensorflow
REFERENCES
[1] The main Paper: arxiv.org/abs/1706.03762
[2] Tensor2Tensor has some code with a tutorial: www.tensorflow.org/tutorials/...
[3] Transformer very intuitively explained - Amazing: jalammar.github.io/illustrated...
[4] Medium Blog on intuitive explanation: / what-is-a-transformer
[5] Pretrained word embeddings: nlp.stanford.edu/projects/glove/
[6] Intuitive explanation of Layer normalization: mlexplained.com/2018/11/30/an...
[7] Paper that gives even better results than transformers (Pervasive Attention): arxiv.org/abs/1808.03867
[8] BERT uses transformers to pretrain neural nets for common NLP tasks. : ai.googleblog.com/2018/11/ope...
[9] Stanford Lecture on RNN: cs231n.stanford.edu/slides/201...
[10] Colah’s Blog: colah.github.io/posts/2015-08...
[11] Wiki for timeseries of events: en.wikipedia.org/wiki/Transfo...)
Негізгі бет Transformer Neural Networks - EXPLAINED! (Attention is all you need)
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