ダウンロード "20 papers to master Language modeling?"

Here are 20 research papers you should read to master Language modeling. ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ PAPERS [1 📚] A Mathematical Theory of Communication: https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf [2 📚] A Neural Probabilistic Language Model: https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf [3 📚] NLP (Almost) from scratch: https://www.jmlr.org/papers/volume12/collobert11a/collobert11a.pdf [4 📚] Phoneme Recognition using Time Delay Neural Networks: https://www.cs.toronto.edu/~fritz/absps/waibelTDNN.pdf [5 📚] Efficient Estimation of words in vector space: https://arxiv.org/pdf/1301.3781.pdf [6 📚] GloVe: https://nlp.stanford.edu/pubs/glove.pdf [7 📚] Enriching word vectors with subword information: https://arxiv.org/pdf/1607.04606.pdf [8 📚] A Convolution Neural Network for modeling sentences: https://arxiv.org/pdf/1404.2188.pdf [9 📚] Learning Internal Representations by error propagation: https://apps.dtic.mil/dtic/tr/fulltext/u2/a164453.pdf [10 📚] Sequence Modeling (from the deep learning book): https://www.deeplearningbook.org/contents/rnn.html#pf1d [11 📚] Long Short Term Memory: http://www.bioinf.jku.at/publications/older/2604.pdf [12 📚] Colah's blog to understanding LSTM: https://colah.github.io/posts/2015-08-Understanding-LSTMs/ [13 📚] Training Recurrent Neural Networks (PhD Thesis): https://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf [14 📚] Deep contextualized word representations: https://arxiv.org/pdf/1802.05365.pdf [15 📚] Attention is all you need: https://arxiv.org/pdf/1706.03762.pdf [16📚] BERT: https://arxiv.org/pdf/1810.04805.pdf [17 📚] Improving language understanding by generative pretraining: https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf [18 📚] Language Models are multi task learners:https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf [19 📚] Language models are few shot learners: https://arxiv.org/pdf/2005.14165.pdf [20 📚] Sentence BERT: https://arxiv.org/pdf/1908.10084.pdf [21 📚] ChatGPT blog: https://openai.com/blog/chatgpt [22 📚] Llama-2: https://scontent-lax3-1.xx.fbcdn.net/v/t39.2365-6/10000000_662098952474184_2584067087619170692_n.pdf?_nc_cat=105&ccb=1-7&_nc_sid=3c67a6&_nc_ohc=cvmnxtGA6rQAX9vXmYA&_nc_ht=scontent-lax3-1.xx&oh=00_AfAfUpzEkX3nP_ptxAOoqKL2pO7s0fzAsAyYEXPFmg1fLQ&oe=64E1FF7F MY VIDEOS ON THESE TOPICS 💻 Convolution in NLP: https://www.youtube.com/watch?v=AGWieLbom_g 💻 LSTM: https://www.youtube.com/watch?v=QciIcRxJvsM 💻 Transformers: https://www.youtube.com/watch?v=TQQlZhbC5ps 💻 Coding Transformers (playlist): https://www.youtube.com/playlist?list=PLTl9hO2Oobd97qfWC40gOSU8C0iu0m2l4 💻 BERT: https://www.youtube.com/watch?v=xI0HHN5XKDo 💻 GPT: https://www.youtube.com/watch?v=3IweGfgytgY 💻 ChatGPT: https://www.youtube.com/watch?v=NpmnWgQgcsA 💻 Llama: https://www.youtube.com/watch?v=yZ9jkgN2xHQ&t=220s 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/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 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 #chatgpt #deeplearning #machinelearning #bert #gpt