Python data processing
About
- howto
- matplotlib
- numpy
- 1-loading-data.ipynb
- 2-generate-data.ipynb
- 3-slice-reshape-select.ipynb
- 4-vectors-operations.ipynb
- 5-matrices.ipynb
- 6-diagonals.ipynb
- 7-scan-ndarray.ipynb
- 8-cartesian.ipynb
- pandas
- ml
- Introduction
- Back propagation
- chain_rule.ipynb
- Circle boundary classification
- Cross Entropy Loss - relation with NLLLoss and LogSoftmax
- Linear regression
- Binary classification
- Logistic regression
- Logistic regression(II)
- Neural network example 1
- Neural network example 2
- Logistic regression(custom functions)(pytorch)
- Linear regression(pytorch)
- Non-linear boundary(1 layer NN PyTorch)
- Non-linear boundary(2 layer NN PyTorch
- Logistic regression - 3 classes
- MNIST - training fully-connected NN (I)
- MNIST (II)
- MNIST - Accuracy (training vs test data sets)
- MNIST - save/load NN state
- Logistic_regression
- 0_gradients.html
- 1-logistic-py-secvential.ipynb
- 2-logistic-numpy-vectorial.ipynb
- 3-logistic-torch.ipynb
- 4-logistic-tf.ipynb
- logistic_regression
- Pytorch
- 00_nn_defs.html
- 01-manage-networks.ipynb
- 02-network-weights-and-shapes.ipynb
- 03-network-weights-and-shapes.ipynb
- 04-network-weights-and-shapes.ipynb
- 05-network-weights-and-shapes.ipynb
- 1a-2inputs-1output.ipynb
- 1b-2inputs-1output.ipynb
- 1c-2inputs-1output.ipynb
- 1d-2inputs-1output.ipynb
- 2a-2inputs-2outputs.ipynb
- 2b-2inputs-2outputs.ipynb
- 2c-2inputs-2outputs.ipynb
- 2d-2inputs-2outputs.ipynb
- 3a-2inputs-3outputs.ipynb
- 3b-2inputs-3outputs.ipynb
- 3c-2inputs-3outputs.ipynb
- 3d-2inputs-3outputs.ipynb
- 4a-3inputs-2output.ipynb
- 4b-3inputs-2outputs.ipynb
- 4c-3inputs-2-outpus.ipynb
- 4d-3inputs-2outputs.ipynb
- 5a-2in-2-1-n-1out.ipynb
- 5b-2in-2-1-n-1out.ipynb
- 5c-2in-2-1-n-1out.ipynb
- 5d-2in-2-1-n-1out.ipynb
- 6a-3in-3-1-n-1out.ipynb
- 6b-3in-3-1-n-1out.ipynb
- 6c-3in-3-1-n-1out.ipynb
- 6d-3in-3-1-n-1out.ipynb
- neuron.ipynb
- nn_img
- statistics
Corciu Mihai - Oltenita - 2021 - mc200520052005@yahoo.com