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📈 Simple machine learning, learning by simple humans
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Samuel FORESTIER b5d3b124ab Update 'README.md' 5 years ago
GradientDescent We actually don't need to specify the encoding for Python 3 programs... 6 years ago
LinearRegression Falls back on scalar entities on `affineRegression` + Minor fixes 6 years ago
Modules/Graphics Changes random engine by C++11's advised one and takes advantage of some 6 years ago
PolynomialRegression Removes meanNormalization for newton and normequ 6 years ago
bin Binaries will now be under a `bin/` folder to let the project root clean 6 years ago
dev_null We actually don't need to specify the encoding for Python 3 programs... 6 years ago
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README.md Update 'README.md' 5 years ago

README.md

MINDLE Is Not a Deep Learning Experimentation

Requirements

For new C++ codes

  • g++ >= 4.9.2 (of course, we won't upload any binaries here)
  • libsfml-dev >= 2.4.1 (for SFML library)
  • libeigen3-dev >= 3.3.2 (for Eigen library)

For old Python codes

  • Python 3.5.2
  • Theano 0.8.2
  • Numpy 1.11.2
  • Matplotlib 1.5.3
  • TensorFlow 0.11.0rc0

Usage (examples)

  1. Clone the repository : git clone https://github.com/HorlogeSkynet/MINDLE.git
  2. Move into the folder : cd MINDLE/
  3. Compile the C++ : make
  4. Run the C++ programs : ./bin/linearRegression.out
  5. Run the Python programs : python3 dev_null/Theano/LinearRegression/linearRegression.py

Notes to read

  1. Some words about the frameworks we used with Python in the past : Theano & TensorFlow gave us a too high-level abstraction for our needs. We couldn't manage to get something to work, and this is not about getting computations on the GPU; that was just impossible, and we nearly destroy our computers many times... Now, we start from scratch with C++. At least, we would understand the errors thrown.

  2. Based on this "tutorial", you'll be able to perform digits classification by getting the return of loadMNIST.py. If you run it, the code will download and read the training examples automatically.