| 0 - Introduction to scientific computing | |||
| 00-Python-ws20.pdf | 166.77 KB | ||
| 00-problems-ws20.pdf | 158 KB | ||
| annvenv.yml | 236 B | ||
| slides.pdf | 639.54 KB | ||
| 01-02 - Introduction-Optimization | |||
| 01-02-lecture.pdf | 1.2 MB | ||
| 01-problems.pdf | 55.63 KB | ||
| 02-problems.pdf | 64.79 KB | ||
| lec01-quiz.pdf | 70.31 KB | ||
| 03-Regression | |||
| 03-Regression-problems.pdf | 75.44 KB | ||
| 03-Regression.pdf | 168.65 KB | ||
| lec02-quizz.pdf | 518.27 KB | ||
| 04-Classification | |||
| 04-Classification.pdf | 350.54 KB | ||
| 04_log_regression_data.npy | 23.56 KB | ||
| lec03-quiz.pdf | 141.25 KB | ||
| problems-04-Classfication.pdf | 57.74 KB | ||
| 05-Model Selection | |||
| 05-ModelSelection.pdf | 352.98 KB | ||
| lec05-quiz.pdf | 529.98 KB | ||
| tutorial | |||
| 05-ModelSelection.pdf | 67.73 KB | ||
| 05_model_selection_data.npy | 448 B | ||
| A5_E1.py | 1013 B | ||
| A5_E2.py | 869 B | ||
| A5_E3.py | 1.97 KB | ||
| 06-Biological Neural Networks | |||
| 06-BiologicalNN.pdf | 4.38 MB | ||
| lec06-quiz.pdf | 60.28 KB | ||
| 07-Artificial Neural Networks | |||
| 07-ArtificialNN.pdf | 476.49 KB | ||
| 07_tutorial_problems.pdf | 170.72 KB | ||
| lec07-quiz.pdf | 59.38 KB | ||
| 08-Perceptron | |||
| 08-Perceptron.pdf | 161.12 KB | ||
| 08-problems.pdf | 65.47 KB | ||
| Problem_Perceptron.py | 2.14 KB | ||
| lec08-quiz.pdf | 116.93 KB | ||
| 09-Multilayer Perceptron | |||
| 09-MLP.pdf | 171.59 KB | ||
| lec09-quiz.pdf | 119.2 KB | ||
| tutorial | |||
| 09-problems.pdf | 159.07 KB | ||
| prob_mlp.py | 1.69 KB | ||
| xor.npy | 117.31 KB | ||
| 10-Convolutional Neural Networks | |||
| 10-CNN.pdf | 993.4 KB | ||
| 10-problems.pdf | 242.84 KB | ||
| lec10-quiz.pdf | 80.53 KB | ||
| 11-Deep Neural Networks | |||
| 11-DeepNN.pdf | 242.48 KB | ||
| lec11-quiz.pdf | 57.73 KB | ||
| problem_transfer_learning.py | 1.8 KB | ||
| wrap-11-DeepNN.pdf | 79.81 KB | ||
| 12-Recurrent Neural Networks | |||
| 12-RecurrentNN.pdf | 326.81 KB | ||
| lec12-quiz.pdf | 57.03 KB | ||
| prob-12-RecurrentNN.pdf | 78.5 KB | ||
| problem_2.py | 1.49 KB | ||
| 13-Hopfield Network | |||
| 13-HopfieldNetwork.pdf | 291.27 KB | ||
| images.npy | 63.02 KB | ||
| lec13-quiz.pdf | 93.18 KB | ||
| prob-13-HopfieldNetwork.pdf | 83.04 KB | ||
| 14- Boltzmann Machine | |||
| 14-BoltzmannMachine.pdf | 410.42 KB | ||
| lec14-quiz.pdf | 75.2 KB | ||
| prob-14-BolzmannMachine.pdf | 75.24 KB | ||
| prob_rbm.py | 1.43 KB | ||
| 15- Review Session | |||
| 15-Review-notes.pdf | 1019.04 KB | ||
| notes-all.pdf | 4.08 MB | ||
| syllabus-ANN.pdf | 36.84 KB | ||
| ▲ 58 total files | |||
Textbook in PDF format
Artificial neural networks (ANN) were inspired by the architecture and function of the brain. Nevertheless, their greatest strength is not that they are good models of the brain, but rather that they are powerful function approximators. Since the 1980's many types of ANN have been developed and tricks for training ANNs on data proliferated. Recent advances in computing hardware and the availability of large datasets has made it possible to train ANNs such that they perform better than humans, e.g., on image recognition. In this class, students will, firstly, gain a theoretical understanding of the principles underlying the methods applied to neural networks and, secondly, learn practical skills in implementing neural networks and applying them for data analysis
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| 5.79 MB | andryold1 | 2 years | 1 | 0 | |
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| 19.51 MB | andryold1 | 3 years | 7 | 1 | |
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