![]() ![]() Notebooks that need you to tamper with the PYTHONPATH or to start Jupyter from a certain directory for modules to import correctly. Also sharing these notebooks is quite often an unnecessary pain. In strong contrast to this, and actually more often to find in practise, are notebooks with cells containing pages of incomprehensible source code, distracting you from the actual analysis. Notebooks that are beautifully designed and perfectly convey ideas and concepts by having the perfect balance between text, code and visualisations like in my all time favourite Probabilistic Programming and Bayesian Methods for Hackers. But as Pythagoras already noted “If there be light, then there is darkness.” and with Jupyter notebooks it’s no difference of course.īeing in the data science domain for quite some years, I have seen good but also a lot of ugly. ![]() Due to this unique characteristic, Jupyter notebooks have achieved a strong adoption particularly in the data science community. This combination makes it extremely useful for explorative tasks where the source code, documentation and even visualisations of your analysis are strongly intertwined. Python and R, as well as rich text elements like paragraphs, equations, figures, links, etc. In a nutshell, a notebook is an interactive document displayed in your browser which contains source code, e.g. ![]() Park for the MIMIC enhancements (from ).If you have ever done something analytical or anything closely related to data science in Python, there is just no way you have not heard of Jupyter or IPython notebooks. You can cite this fork in a similar way, but please be sure to reference the original work. mlrose: Machine Learning, Randomized Optimization and SEarch package for Python. Please also keep the original author's citation: mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix. You can cite mlrose in research publications and reports as follows: Mlrose was written by Genevieve Hayes and is distributed under the 3-Clause BSD license. The official mlrose documentation can be found here.Ī Jupyter notebook containing the examples used in the documentation is also available here. ![]() Once it is installed, simply import it like so: import mlrose_hiive Documentation The latest version can be installed using pip: pip install mlrose-hiive Mlrose was written in Python 3 and requires NumPy, SciPy and Scikit-Learn (sklearn). Supports classification and regression neural networks.Optimize the weights of neural networks, linear regression models and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm or gradient descent.Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens and Max-K Color optimization problems.Define your own fitness function for optimization or use a pre-defined function.Solve discrete-value (bit-string and integer-string), continuous-value and tour optimization (travelling salesperson) problems.Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay or exponential decay.Define the algorithm's initial state or start from a random state.Solve both maximization and minimization problems.Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm and (discrete) MIMIC.Main Features Randomized Optimization Algorithms It also has the flexibility to solve user-defined optimization problems.Īt the time of development, there did not exist a single Python package that collected all of this functionality together in the one location. It includes implementations of all randomized optimization algorithms taught in this course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem continuous-valued optimization problems, such as the neural network weight problem and tour optimization problems, such as the Travelling Salesperson problem. Mlrose was initially developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning. Mlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces. Mlrose: Machine Learning, Randomized Optimization and SEarch ![]()
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