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📚 Notes and Important Formulas ⬅️ "Matrices, Linear Algebra, and Probability"

👨🏻‍💻 This booklet serves as an essential resource for individuals initiating their studies in data science. It consolidates comprehensive information on matrices, linear algebra, and probability, thereby eliminating the necessity of consulting multiple sources.

✏️ The document encompasses nearly all pertinent formulas and key concepts. It addresses foundational topics such as determinants and matrix inverses, as well as advanced subjects including eigenvalues, eigenvectors, Singular Value Decomposition (SVD), and probability distributions.

🌐 #DataScience #Python #Math

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📝 12 Essential Articles for Data Scientists

🏷 Article: Seq2Seq Learning with NN
https://arxiv.org/pdf/1409.3215
An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning.

🏷 Article: GANs
https://arxiv.org/pdf/1406.2661
An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence.

🏷 Article: Attention is All You Need
https://arxiv.org/pdf/1706.03762
This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models.

🏷 Article: Deep Residual Learning
https://arxiv.org/pdf/1512.03385
This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process.

🏷 Article: Batch Normalization
https://arxiv.org/pdf/1502.03167
This paper introduced a technique that facilitates faster and more stable training of neural networks.

🏷 Article: Dropout
https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
A straightforward method designed to prevent overfitting in neural networks.

🏷 Article: ImageNet Classification with DCNN
https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
The first successful application of a deep neural network for image recognition.

🏷 Article: Support-Vector Machines
https://link.springer.com/content/pdf/10.1007/BF00994018.pdf
This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification.

🏷 Article: A Few Useful Things to Know About ML
https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf
A comprehensive collection of practical and empirical insights regarding machine learning.

🏷 Article: Gradient Boosting Machine
https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf
This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM.

🏷 Article: Latent Dirichlet Allocation
https://jmlr.org/papers/volume3/blei03a/blei03a.pdf
This work introduced a model for text analysis capable of identifying the topics discussed within an article.

🏷 Article: Random Forests
https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy.

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