Sentence Tokenization in Transformer Code from scratch! скачать в хорошем качестве

Sentence Tokenization in Transformer Code from scratch!

ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporiu... 📚 Medium Blog:   / dataemporium   💻 Github: https://github.com/ajhalthor 👔 LinkedIn:   / ajay-halthor-477974bb   RESOURCES [ 1 🔎 ] Samanantar: The paper https://paperswithcode.com/paper/sama... [ 2 🔎] Samanantar: Download https://ai4bharat.iitm.ac.in/samanantar [ 3 🔎] Code for video:https://github.com/ajhalthor/Transfor... PLAYLISTS FROM MY CHANNEL ⭕ Transformers from scratch playlist:    • Self Attention in Transformer Neural Netwo...   ⭕ ChatGPT Playlist of all other videos:    • ChatGPT   ⭕ Transformer Neural Networks:    • Natural Language Processing 101   ⭕ Convolutional Neural Networks:    • Convolution Neural Networks   ⭕ The Math You Should Know :    • The Math You Should Know   ⭕ Probability Theory for Machine Learning:    • Probability Theory for Machine Learning   ⭕ Coding Machine Learning:    • Code Machine Learning   MATH COURSES (7 day free trial) 📕 Mathematics for Machine Learning: https://imp.i384100.net/MathML 📕 Calculus: https://imp.i384100.net/Calculus 📕 Statistics for Data Science: https://imp.i384100.net/AdvancedStati... 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStati... 📕 Linear Algebra: https://imp.i384100.net/LinearAlgebra 📕 Probability: https://imp.i384100.net/Probability OTHER RELATED COURSES (7 day free trial) 📕 ⭐ Deep Learning Specialization: https://imp.i384100.net/Deep-Learning 📕 Python for Everybody: https://imp.i384100.net/python 📕 MLOps Course: https://imp.i384100.net/MLOps 📕 Natural Language Processing (NLP): https://imp.i384100.net/NLP 📕 Machine Learning in Production: https://imp.i384100.net/MLProduction 📕 Data Science Specialization: https://imp.i384100.net/DataScience 📕 Tensorflow: https://imp.i384100.net/Tensorflow TIMESTAMP 0:00 Dataset Source 2:39 Alpha Syllabery Explained 5:33 Reading & Processing Sentences 8:43 Pytorch Dataset & TextDataset 10:13 Batching Sentences 12:05 Character to Number Encoding 14:50 Masking 18:15 Creating a Class