Russia, Ulyanovsk Ulyanovsk State Technical University
- Brief history of basic technologies
- Definitions
- Basics of ML
- Basic syntax
- Arithmetic
- Strings
- Lists
- Exploratory data analysis with Pandas
- Data visualization
- Kaggle competitions
- Titanic task
- Linear regression
- Cost function
- Gradient descent
- Linear regression with multiple variables
- Debug (learning rate)
- Normal equation
- Features and polynomial regression
- Multi-class classification
- Feature scaling
- Decision tree
- K-nearest neighbors
- Classification problem
- Sigmoid function
- Decision boundary
- Cost function
- Regularization. Problem of overfitting
- Error analysis. Metrics
- Evaluating hypothesis. Train / test / validation set
- High bias / high variance (model selection, regularization, learning curves)
- Feature extraction:
- Images
- GEO
- Date and time
- Timeseries
- Texts. One-hot encoding
- Clustering (k-means, c-means, hierarchical clustering)
- Principal component analysis
- Bayes theorem
- Naive bayes
- Support vector machines
- Non-linear hypothesis
- Neurons and the brain
- Forward propagation (XNOR example)
- Back propagation
- Parameters initializing
- Convolution. Feature representation as hierarchy
- Filters, stride, padding
- Pooling
- Popular architectures: AlexNet, VGG, ResNet,
- Classification, localization, regression
- Basics of recurrent NN
- LSTM
- Time-series analysis
- Text analysis
- Decision trees
- Random forest
- XGBoost
- CatBoost
- Probability theory for Machine Learning
- Список книг по искусственному интеллекту
- Python Data Science Handbook
- Ways to load files to google colab
- ML course by Andrew Ng
- Lectures notes of the Coursera Course by Andrew Ng: https://github.com/diefimov/MTH594_MachineLearning
- ML course by Dmitry Efimov
- ML course by OpenData Science
- MIT Deep learning
- Recommended book: Python for Data Analysis ru from Pandas author.
- Recommended book:François Chollet. Deep Learning with Python. Manning, 2018 and (https://github.com/fchollet/deep-learning-with-python-notebooks)
- 100+ Free Data Science Books
- Gallery of interesting IPython notebooks
- NumPy - arrays & matrices
- Pandas - working with datasets
- Matplotlib - data visualization
- SkiKit learn - ML library
- Pandas lessons
- Pandas tutorial
- Google collaboratory - powerful Jupyter Notebook environment for deep learning
- Awesome deepvision links
- SSD: Single-Shot MultiBox Detector implementation in Keras
- Word2vec with Keras
- https://github.com/qati/DeepLearningCourse
- https://github.com/roebius/deeplearning_keras2
- https://github.com/enggen/Deep-Learning-Coursera
- https://github.com/fchollet/deep-learning-with-python-notebooks
- Russian: Отличная статья про то, как создать ИНС, с примерами на Python
- Russian: Анализ данных при помощи Python. Графики в pandas и matplotlib.
- Russian: Основы pandas
- ROC AUC
- (https://towardsdatascience.com/visual-attention-model-in-deep-learning-708813c2912c)
- (https://medium.com/@sunnerli/visual-attention-in-deep-learning-77653f611855)
- (https://arxiv.org/pdf/1512.01693.pdf)
- (https://arxiv.org/pdf/1406.6247.pdf)
- (https://arxiv.org/pdf/1605.01335.pdf)
- (https://openreview.net/pdf?id=B14TlG-RW)