Set of Jupyter (iPython) notebooks (and few pdf-presentations) about things that I am interested on, like Computer Science, Statistics and Machine-Learning, Artificial Intelligence (AI), Financial Engineering, Optimization, Stochastic Modelling, Time-Series forecasting, Science in general... and more.
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NEW: Neural Networks (RNN/LSTM/GRU) for time-series forecasting in Commodities Markets using TensorFlow/Keras (Here, just the simplest of the univariate versions. If you are interested in the deep-LSTM multivariate version, contact me)
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NEW: Auto-Machine Learning Model Selection using GridSearchCV: It has some interesting ideas I am working on... hope you like. If you want to know how to go further, please contact me
My favourites?
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Algorithmic Problems in Python: (Computer Science) It contains more than 60 algorithm problems about generators, list-comprehension, map/reduce/filter problems, decorators, regular expressions, simulations, classes... This is still a work-in-progress, but feel free to have a look, and enjoy.
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Doing Science with Python: (Python programming) this one is huge, almost a book... and it is still 'work-in-progress' but it contains not only the basics about data-containers, or basic algorithms, but also advance hash-functions and functional programming... but more specifically, or advanced in Python, maybe you find intriguing the notebooks about Decorators, Closures and Wrappers or the one about Iterables, Generetors and Yield, or related with High Performance Computation (HPC) check HPC Cython vs. Multiprocessing out. Hope you like.
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Doing Statistics with Python: (Statisitics and Machine Learning) Another huge notebook, in fact, I wrote it thinking in a free eBook, but it is quite general (and basic) in terms of statistics.
- If you look for some more advanced statistics, but always with Python implementations, have a look to the notebooks of Volatility Forecasting, Outliers detection for multivariate datasets, K-means without Scikit or EM-Algorithm for clustering and missing values
- If you want to go deeper in Statistics Techniques for Big (distributed) Data, maybe you should have a look into the PCA for Big Data or into Regression Techniques for Big Data notebooks, where I am using Apache Spark with PySpark.
- If you are working in Machine Learning and Deep Learning, have a look into one of the Support Vector Machines (SVM) notebooks (e.g. Cross Validation in SVM, or Classification Metrics with SVM), and more specifically into Deep Learning in Generative Models: Autoencoders, VANs and GANs notebook with TensorFlow and Keras.
- If you are interested in knowing how to work in modelling with MongoDB in order to store your input/output or model results, have a look to the code of MongoDB, Numpy and Scikit-learn notebook.
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Two-Factor Model: (Stochastic Modelling) this one is more technical, from the mathematical point of view, but quite interesting if you are working in commodities modelling, derivatives pricing, or risk-management. This one is the simplest possible version, that made the equivalence between Black-76 and the Two-Factor Model for forward contracts. I will update it soon, with a more object-oriented version. Stay tuned. If you want to have the full object-oriented version, with Robust Calibration of the SDE using Semidefinite Programming and unit-testing, contact me.
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Just for FUN: If you like Astronomy like me, check this out: Web-scrapping NASA Astronomy Picture of the day
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... and if you are interested in Financial Engineering, you will find interesting the notebooks about:
among others... pls, be my guest, and serve yourself.
Have fun!
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