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juliaintro

Contains code and presentations for getting started with Julia (and other things)

juliaoulu20

Julia Workshop 2020 @ University of Oulu, Finnland

jupyter

Course material on Jupyter Notebooks.

jupyter-and-its-moons

Notebooks introducing scientific computing with Python & Jupyter

jupyter-notebook-for-data-science

Jupyter Notebook for Data Science, published by Packt

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

keras

Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.

keras-hyperparameter-tuning

This code does a hyperparameter grid search on a neural network. The hyperparameters searched through are epochs, batches, and optimizers.

keras-tutorial

Tutorial teaching the basics of Keras and some deep learning concepts

labs

Labs for the Foundations of Applied Mathematics curriculum.

languages-and-codegen-tutorial

A tutorial on defining domain-specific languages and transforming them to high-performance code

latex-templates

A collection of LaTeX templates used for research, courses, and miscellanea.

learn-statistical-learning-method

Implementation of Statistical Learning Method, Second Edition.《统计学习方法》第二版，算法实现。

learned_gradient_tomography

Solving ill-posed inverse problems using iterative deep neural networks

learned_matrix_inverse_problems

lecture_cil_public

Public repository for CIL

lectures

repository with the lectures for MLSS Skoltech

lehrfempp

Simplistic Finite Element Framework for research and eduction

letkf

Automatically exported from code.google.com/p/miyoshi

libpku

北京大学课程资料整理

lihang-code

《统计学习方法》的代码实现

linear_regression_gradient_descent

Linear Regression using Gradient Descent algorithm

linearalgebra_matlabtutorials

listings

Code listings for Modern Fortran: Building Efficient Parallel Applications

lookup-table-by-cuda

m4650-repo

Course Repository for CU-Denver Undergrad Numerical Analysis, Fall 2016

ma265

MA265-Fall 2016

machine-learning

EECS 545 001 FA 2017 Syllabus • Introduction – Overview – Linear Algebra Review – Probability Review – Convex Optimization – Newton’s Method, Gradient Descent, Stochastic Gradient Descent • Classification – K-nearest neighbors (KNN) – Bayes Classifiers – Discriminant Analysis – The Naive Bayes – Logistic Regression • Regression – Linear Regression – Least Squares – Probabilistic Interpretation (connection to MLE) – Ridge Regression – Robust Regression • Kernel Methods – Positive Definite Symmetric (PSD) Kernels – Kernel Ridge Regression – Kernel Density Estimation – Separating Hyperplanes – Support Vector Machine (SVM) – Gaussian Processes • Regularization – L2 Regularization – L1 Regularization, Sparsity and Feature Selection – Bias-Variance Tradeoff – Empirical Risk Minimization – Cross Validation, Model Selection 1 • Unsupervised Learning – Principle Components Analysis (PCA) – Independent Components Analysis (ICA) – Clustering, K-Means – Spectral Clustering – Gaussian Mixture Models – The Expectation Maximization Algorithm – Factor Analysis – Dimensionality Reduction • Neural Networks – Perceptron – MLP and back-propagation • Ensemble Methods • Boosting • Decision Trees • Advanced Topics: – On-Line Learning – Learning Theory ∗ Sample Complexity ∗ VC-Dimension – Graphical Models ∗ Bayesian Networks ∗ Structure Learning ∗ Hidden Markov Models (HMM) ∗ Markov Networks – Reinforcement Learning – Markov Decision Processes 2

machine-learning-1

Course P2.11 of Master HPC; Python, Scikit-Learn

machine-learning-learning-notes

周志华《机器学习》又称西瓜书是一本较为全面的书籍，书中详细介绍了机器学习领域不同类型的算法(例如：监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等)，记录了本人在学习过程中的理解思路与扩展知识点，希望对新人阅读西瓜书有所帮助！