Teaching materials
Neural Networks and Neural Networks for Data Science.
- Lecture 0
- Lecture 2 optimization
- Lecture 2 tensors
- NNDS demos
- Theme kit test
- NN: NN2627_01_Intro (PDF)
- NN: NN2627_Linear_models (PDF)
- NN: NN2627_MLPs (PDF)
- NNDS: Lecture_0_about_the_course (PDF)
- NNDS: Lecture_2_optimization (PDF)
- NNDS: Lecture_2_preliminaries (PDF)
- NNDS: Lecture_2_tensors (PDF)
- NNDS: Lecture_3_supervised_learning (PDF)
- NNDS: Lecture_4_Fully_connected (PDF)
- NNDS: Lecture_CNNs (PDF)
- PT01_Introduction_to_PyTorch.html
- PT01_Introduction_to_PyTorch.ipynb
- PT01 Introduction to PyTorch (Open in Colab)
- PT01_Introduction_to_PyTorch_solutions.html
- PT01_Introduction_to_PyTorch_solutions.ipynb
- PT01 Introduction to PyTorch solutions (Open in Colab)
- PT02_Logistic_regression.html
- PT02_Logistic_regression.ipynb
- PT02 Logistic regression (Open in Colab)
- PT02_Logistic_regression_solutions.html
- PT02_Logistic_regression_solutions.ipynb
- PT02 Logistic regression solutions (Open in Colab)
- PT03_Automatic_differentiation.html
- PT03_Automatic_differentiation.ipynb
- PT03 Automatic differentiation (Open in Colab)
- PT03_Automatic_differentiation_solutions.html
- PT03_Automatic_differentiation_solutions.ipynb
- PT03 Automatic differentiation solutions (Open in Colab)