yandexdataschool / Practical_dl
Licence: mit
DL course co-developed by YSDA, HSE and Skoltech
Stars: ✭ 1,006
Projects that are alternatives of or similar to Practical dl
Practical rl
A course in reinforcement learning in the wild
Stars: ✭ 4,741 (+371.27%)
Mutual labels: jupyter-notebook, course-materials, lasagne, theano
Csc deeplearning
3-day dive into deep learning at csc
Stars: ✭ 22 (-97.81%)
Mutual labels: jupyter-notebook, course, lasagne, theano
Ppd599
USC urban data science course series with Python and Jupyter
Stars: ✭ 1,062 (+5.57%)
Mutual labels: jupyter-notebook, course, course-materials
Psgan
Periodic Spatial Generative Adversarial Networks
Stars: ✭ 108 (-89.26%)
Mutual labels: jupyter-notebook, lasagne, theano
Course Computational Literary Analysis
Course materials for Introduction to Computational Literary Analysis, taught at UC Berkeley in Summer 2018, 2019, and 2020, and at Columbia University in Fall 2020.
Stars: ✭ 74 (-92.64%)
Mutual labels: jupyter-notebook, course, course-materials
Deep Learning Python
Intro to Deep Learning, including recurrent, convolution, and feed forward neural networks.
Stars: ✭ 94 (-90.66%)
Mutual labels: jupyter-notebook, lasagne, theano
Natural Language Processing Specialization
This repo contains my coursework, assignments, and Slides for Natural Language Processing Specialization by deeplearning.ai on Coursera
Stars: ✭ 151 (-84.99%)
Mutual labels: jupyter-notebook, course, course-materials
Ml Mipt
Open Machine Learning course at MIPT
Stars: ✭ 480 (-52.29%)
Mutual labels: jupyter-notebook, course
Machine Learning Book
《机器学习宝典》包含：谷歌机器学习速成课程（招式）+机器学习术语表（口诀）+机器学习规则（心得）+机器学习中的常识性问题 （内功）。该资源适用于机器学习、深度学习研究人员和爱好者参考！
Stars: ✭ 616 (-38.77%)
Mutual labels: jupyter-notebook, course
Deepo
Setup and customize deep learning environment in seconds.
Stars: ✭ 6,145 (+510.83%)
Mutual labels: lasagne, theano
Course julia day
Notes for getting to know the Julia programming language in one day.
Stars: ✭ 23 (-97.71%)
Mutual labels: jupyter-notebook, course
Deepalignmentnetwork
A deep neural network for face alignment
Stars: ✭ 480 (-52.29%)
Mutual labels: lasagne, theano
Machine Learning Curriculum
💻 Make machines learn so that you don't have to struggle to program them; The ultimate list
Stars: ✭ 761 (-24.35%)
Mutual labels: course, theano
Deep Learning Vm
Sets up a VM with Keras, TensorFlow, TFLearn and Theano installed
Stars: ✭ 23 (-97.71%)
Mutual labels: jupyter-notebook, theano
Mlcourse
Machine learning course materials.
Stars: ✭ 388 (-61.43%)
Mutual labels: jupyter-notebook, course-materials
Docker Hy.github.io
DevOps with Docker course by the University of Helsinki, Course material
Stars: ✭ 41 (-95.92%)
Mutual labels: course, course-materials
Python plotting snippets
Tips and tricks for plotting in python
Stars: ✭ 28 (-97.22%)
Mutual labels: jupyter-notebook, course
Deep learning course
This repo supplements Deep Learning course taught at YSDA and HSE @spring'21. For previous iteration visit the fall20 branch.
Lecture and seminar materials for each week are in ./week* folders. Homeworks are in ./homework* folders.
General info
- Create cloud jupyter session from this repo -
- Telegram chat room (russian).
- YSDA deadlines & admin stuff can be found at the YSDA LMS (ysda students only).
- Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
Syllabus
-
week01 Intro to deep learning
- [ ] Lecture: Deep learning -- introduction, backpropagation algorithm
- [ ] Seminar: Neural networks in numpy
- [ ] Homework 1 is out!
- [ ] Please begin worrying about installing pytorch. You will need it next week!
Contributors & course staff
Course materials and teaching performed by
- Victor Lempitsky - all main track lectures (1-11)
- Victor Yurchenko - intro notebooks, admin stuff
- Vadim Lebedev - notebooks, admin stuff
- Dmitry Ulyanov - notebooks on generative models & autoencoders
- Fedor Ratnikov - pytorch & nlp notebooks, one bonus lecture
- Oleg Vasilev - notebooks, technical issue resolution
- Arseniy Ashukha - image captioning materials
- Mikhail Khalman - variational autoencoder materials
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].