Datum | Č.T. | S/L | Náplň | Učitel | Materiály |
---|---|---|---|---|---|
25-26.09.2023 | 1 | L | Intro: Introduction to the course, Python, NumPy, GitHub, Jupyter Notebook and the environment. | PV | Python & Numpy Notebook Notebook (Zip) |
02-03.10.2023 | 2 | S | 1D regression and 2D classification: Revision of the regression and classification theory, analytic gradient computation, gradient in computational graph and loss minimization. | KZ | template_for_students.zip labs_02_regression.pdf |
09-10.10.2023 | 3 | L | Autograd: Computational graphs, backpropagation and the automatic gradient computation. | JV | HW1: Autograd engine_lab engine_ref lab03.pdf |
16-17.10.2023 | 4 | S | Classification I - Intro: Revision of the classification theory, KNN and the linear classifier. | AK | |
23-24.10.2023 | 5 | L | Classification II - NN: Classification using neural networks. | AK | HW2: Classification |
30-31.10.2023 | 6 | S | CNN I - Intro: Introduction to the convolution and to the PyTorch library. | JV | lab06.zip lab06_ref.zip |
06-07.11.2023 | 7 | L | Optimization: Convergence rate, oscillations, diminishing gradients. | KZ | optimizers_student_template.py.zip |
13-14.11.2023 | 8 | S | CNN II: Training a classifier: Training a simple CNN classifier. | JV | lab08.zip lab08_ref.zip |
20-21.11.2023 | 9 | L | Dean's day | ||
27-28.11.2023 | 10 | S | CNN III - Semantic segmentation: Segmentation of images using CNN. | PV | HW 3 - Segmentation HW3 |
04-05.12.2023 | 11 | L | RL I Intro:Introduction to the reinforcement learning. Policy gradient. | TT | HW4: Reinforcement learning |
11-12.12.2023 | 12 | S | RL II - Deep learning:Deep reinforcement learning. | TT | |
18-19.12.2023 | 13 | L | CNN IV - Semantic Segmentation: Presentations, Team projects | PV | |
08-09.01.2023 | 14 | S | Transformers: Applications of the transformers. | DC | lab05.zip |