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awesome-machine-learning-jupyter-notebooks-for-colab
A curated list of Machine Learning and Deep Learning tutorials in Jupyter Notebook format ready to run in Google Colaboratory
https://github.com/toxtli/awesome-machine-learning-jupyter-notebooks-for-colab
Last synced: 4 days ago
JSON representation
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Machine Learning
- Supervised Learning plots
- Unsupervised Learning plots
- Machine Learning Basic concepts 1 - cCLXsNhZ7DW89nF_G1NP-&sa=D&ust=1571021489213000)
- Linear Regression 1 - yk3m6p3ylNLtTaEf3nya6exO_wv8f_L&sa=D&ust=1571021489214000)
- Decision Trees 1
- Random Forest 1 - CJybMJDX17jBmG5qoPPg9qj1sm&sa=D&ust=1571021489215000) & [3](https://www.google.com/url?q=https://colab.research.google.com/drive/1-uDIRl1aYqmJX59rAJumHY1T20QqBJiQ&sa=D&ust=1571021489216000) & [4](https://www.google.com/url?q=https://colab.research.google.com/drive/1-uDIRl1aYqmJX59rAJumHY1T20QqBJiQ&sa=D&ust=1571021489216000)
- Naive Bayes 1
- k-Nearest Neighbor 1 - -PTyS5e&sa=D&ust=1571021489218000)
- k-Means 1
- Support Vector Machines 1
- Logistic Regression 1 - thLSakUmCHjSbqI6vd-NkCq&sa=D&ust=1571021489220000) & [3](https://www.google.com/url?q=https://colab.research.google.com/drive/1jhrAtmPgg6Uu0WzMzV-VakWlncQAvk-D&sa=D&ust=1571021489220000)
- Perceptron 1
- Machine Learning Overview 1
- Principal Component Analysis 1
- Topic modeling 1
- Ensembles 1 - 6ulYMxTzDX8-m6nEptYq0yaej&sa=D&ust=1571021489233000)
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Deep Learning
- GPU testing 1
- Artificial Neural Networks from scratch 1
- ANN Activation functions 1
- ANN Loss functions 1
- ANN Gradients 1
- ANN Optimizers 1
- ANN Decision Boundaries 1
- ANN Overfitting 1
- ANN Regularization 1
- Multi-Layer Perceptron 1 - Mj8Wis5XrQ_a4dUJvYln1g&sa=D&ust=1571021489226000) & [4](https://www.google.com/url?q=https://colab.research.google.com/drive/1wHjugM56k0ay5QCmRVMBfAMF96EY7A5k&sa=D&ust=1571021489226000) & [5](https://www.google.com/url?q=https://colab.research.google.com/drive/1Ly0BtKBphUdeqMQBO8Xjweku62Vq3UAX&sa=D&ust=1571021489226000)
- Convolutional Neural Networks 1 - exx&sa=D&ust=1571021489227000) & [4](https://www.google.com/url?q=https://colab.research.google.com/drive/1gi2_Or0rDz5Gg9FkGJjFDxgeiwt5-lXm&sa=D&ust=1571021489227000) & [5](https://www.google.com/url?q=https://colab.research.google.com/drive/1QcnY-LOZU9c7Sp2DsDVeYxLNBx87VNhn&sa=D&ust=1571021489228000) & [6](https://www.google.com/url?q=https://colab.research.google.com/drive/1Il7eimZ5bxQh1qem-NLiwoMBugODltSI&sa=D&ust=1571021489228000) & [7](https://www.google.com/url?q=https://colab.research.google.com/drive/1YHKZgpJuriGYjEzFDNGz2Hf0widu-exx&sa=D&ust=1571021489228000) & [8](https://www.google.com/url?q=https://colab.research.google.com/drive/1iEYJs75hat_URxshmCBMGzHQo5VgdRvN&sa=D&ust=1571021489229000) & [9](https://www.google.com/url?q=https://colab.research.google.com/drive/1w9GxDTBATF6Cc_1582V6uU2OKdQGnp0J&sa=D&ust=1571021489229000)
- CNN from scratch 1
- Data Augmentation 1 - &sa=D&ust=1571021489230000)
- Recurrent Neural Networks 1 - ou-Za75bFgwArvgP3QfNJ4cWuwY-eF&sa=D&ust=1571021489231000) & [3](https://www.google.com/url?q=https://colab.research.google.com/drive/1PEOqq8mBcmc-FMj8lpbVF93cQI4RLgVJ&sa=D&ust=1571021489231000) & [4](https://www.google.com/url?q=https://colab.research.google.com/drive/1XUEAFxxKVmdgC7oPOzVpGInXfUeTcgIQ&sa=D&ust=1571021489231000) & [5](https://www.google.com/url?q=https://colab.research.google.com/drive/1tfDDriSDUh_J9OHwjt-NzT8xRiEDQF7x&sa=D&ust=1571021489232000)
- Autoencoders 1
- Generative Adversarial Networks 1 - 2M5hy3D2ATWzJs2LC5sk7hpts&sa=D&ust=1571021489232000) & [3](https://www.google.com/url?q=https://colab.research.google.com/drive/1aKywiJ5p0eCwDIIWKe8Q205rcKqmR_VX&sa=D&ust=1571021489233000) & [4](https://www.google.com/url?q=https://colab.research.google.com/drive/1QxXqnhyqIZrrGtor2tVa4jY63adS4yc0&sa=D&ust=1571021489233000) & [5](https://www.google.com/url?q=https://colab.research.google.com/drive/1Lw7BqKABvtiSyUHg9DeM5f90_WFGB7uz&sa=D&ust=1571021489233000)
- AutoML 1 - 39j5TMUqHJR&sa=D&ust=1571021489234000)
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Reinforcement Learning
- Reinforcement Learning 1 - EidZd&sa=D&ust=1571021489235000)
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