​Simple plotting in python (so you can concentrate on the bigger thing)

🔗 Simple plotting in python (so you can concentrate on the bigger thing)
(so you can concentrate on the bigger thing)

​Самый мягкий и пушистый путь в Machine Learning и Deep Neural Networks
Современное машинное обучение позволяет делать невероятные вещи. Нейросети работают на пользу общества: находят преступников, распознают угрозы, помогают диагностировать болезни и принимать сложные решения. Алгоритмы могут переплюнуть человека и в творчестве: они рисуют картины, пишут песни и делают из обычных снимков шедевры. А те, кто разрабатывает эти алгоритмы, часто представляются карикатурным учеными.

Не все так страшно! Собрать нейронную сеть из базовых моделей может любой, кто сколько-то знаком с программированием. И даже не обязательно учить Python, всё можно сделать на родном JavaScript. Как легко начать и зачем машинное обучение фронтендерам, рассказал Алексей Охрименко (obenjiro) на FrontendConf, а мы переложили в текст — чтобы названия архитектур и полезные ссылки были под рукой.

Spoiler. Alert!
Этот рассказ:

Не для тех, кто «уже» работает с Machine Learning. Что-то интересное будет, но маловероятно, что под катом вас ждут открытия.
Не о Transfer Learning. Не будем говорить о том, как написать нейронную сеть на Python, а потом работать с ней из JavaScript. Никаких читов — будем писать глубокие нейронные сети именно на JS.
Не о всех деталях. Вообще все концепции в одну статью не поместятся, но необходимое, конечно, разберем.

🔗 Самый мягкий и пушистый путь в Machine Learning и Deep Neural Networks
Современное машинное обучение позволяет делать невероятные вещи. Нейросети работают на пользу общества: находят преступников, распознают угрозы, помогают диагнос...

🎥 TensorFlow 2.0 Tutorial for Beginners 19 - Multi Step Prediction using LSTM | Time Series Prediction
👁 1 раз ⏳ 4386 сек.
In this lesson, you will learn a multi-step time series prediction using RNN LSTM for household power consumption prediction. We will predict the power consumption of the coming week based on the power consumption of past weeks.

Download the working file: https://github.com/laxmimerit/Multi-Step-Time-Series-Prediction-using-RNN-LSTM-for-household-power-consumption

### Like Facebook Page:
https://www.facebook.com/kgptalkie/

## Watch Full Playlists:
### Deep Learning with TensorFlow 2.0 Tutorials
https:

Finally, Style Transfer For Smoke Simulations! 💨

📝 The paper "Transport-Based Neural Style Transfer for Smoke Simulations" is available here:
http://www.byungsoo.me/project/neural...

🎥 Finally, Style Transfer For Smoke Simulations! 💨
👁 2 раз ⏳ 348 сек.
❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers

📝 The paper "Transport-Based Neural Style Transfer for Smoke Simulations" is available here:
http://www.byungsoo.me/project/neural-flow-style/index.html

💨 My fluid control paper is available here, pick it up if you're interested!
https://users.cg.tuwien.ac.at/zsolnai/gfx/real_time_fluid_control_eg/

Wavelet Turbulence - one of the best papers ever written:
http://www.tkim.graphics/WTURB/

🙏 We would like to thank our

​With 180+ papers mentioning
Transformers and its predecessors, it was high time to put out a real paper that people could cite.

https://arxiv.org/abs/1910.03771

🔗 Transformers: State-of-the-art Natural Language Processing
Recent advances in modern Natural Language Processing (NLP) research have been dominated by the combination of Transfer Learning methods with large-scale Transformer language models. With them came a paradigm shift in NLP with the starting point for training a model on a downstream task moving from a blank specific model to a general-purpose pretrained architecture. Still, creating these general-purpose models remains an expensive and time-consuming process restricting the use of these methods to a small sub-set of the wider NLP community. In this paper, we present Transformers, a library for state-of-the-art NLP, making these developments available to the community by gathering state-of-the-art general-purpose pretrained models under a unified API together with an ecosystem of libraries, examples, tutorials and scripts targeting many downstream NLP tasks. Transformers features carefully crafted model implementations and high-performance pretrained weights for two main deep learning frameworks, PyTorch and Te

🎥 Machine Learning Overview | MLAIT | DSC-LPU
👁 1 раз ⏳ 995 сек.
#MachineLearning #DSC #MLAIT

Resources: https://github.com/patidarparas13/Machine-Learning-Tutorials

Twitter: @patidarparas13,@mlait1908,@dsclpu
LinkedIn : https://linkedin.com/in/patidarparas13

Thank You!

​Getting Started With Machine Learning, Part 3: Writing Your First Machine Learning Program

🔗 Getting Started With Machine Learning, Part 3: Writing Your First Machine Learning Program
A simple example using sci-kit learn

​How Many Samples Do I Need in My Test?

🔗 How Many Samples Do I Need in My Test?
What you need to know to calculate a minimum test size for a binomial test — and how to calculate it

​Feature Extraction Techniques

🔗 Feature Extraction Techniques
An end to end guide on how to reduce a dataset dimensionality using Feature Extraction Techniques such as: PCA, ICA, LDA, LLE, t-SNE and…

​​​📹Artificial caricature

Agents learn to draw simplified (artistic?) portraits via trial and error.

Project website: https://learning-to-paint.github.io
ArXiV: https://arxiv.org/abs/1910.01007

🔗 Unsupervised Doodling and Painting with Improved SPIRAL

​Creating a Weapon Detector in 5 simple steps

🔗 Creating a Weapon Detector in 5 simple steps
Object detection using mask-RCNN on custom dataset

🎥 Deep Learning Weekly (VIII) - Two Stage Object Detection - Алмаз Зиноллаев
👁 1 раз ⏳ 2626 сек.
Алмаз Зиноллаев последовательно рассказал, как итеративно развивался и усложнялся пайплайн, посвященный задаче детекции объектов на изображении

Presentation: https://yadi.sk/i/97o5GkLOSK0lUg

​100 Free Data Science Books

https://www.learndatasci.com/free-data-science-books/

🔗 100+ Free Data Science Books

​Kaggle Live Coding: Automating report generation | Kaggle

🔗 Kaggle Live Coding: Automating report generation | Kaggle
This week Rachael will continue to work on her forum clustering project (https://www.kaggle.com/rebeccaturner/forum-post-embeddings-clustering-1-0). Now that we've got our clusters, we need to generate better reports for them! SUBSCRIBE: https://www.youtube.com/c/kaggle?sub_... About Kaggle: Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and do your data science work. Kaggle's platform is the fastest way to get started on a new data science project. Spi

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🎥 On the GPU - Deep Learning and Neural Networks with Python and Pytorch p.7
👁 2 раз ⏳ 1923 сек.
Text-based tutorials and sample code: https://pythonprogramming.net/gpu-deep-learning-neural-network-pytorch/


How to use cloud GPUs: https://pythonprogramming.net/cloud-gpu-compare-and-setup-linode-rtx-6000/
Linode Cloud GPUs $20 credit: https://linode.com/sentdex

Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join
Discord: https://discord.gg/sentdex
Support the content: https://pythonprogramming.net/support-donate/
Twitter: https://twitter.com/sentdex
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​Exploring Massively Multilingual, Massive Neural Machine Translation

http://ai.googleblog.com/2019/10/exploring-massively-multilingual.html

article: https://arxiv.org/pdf/1907.05019.pdf

🔗 Exploring Massively Multilingual, Massive Neural Machine Translation
Posted by Ankur Bapna, Software Engineer and Orhan Firat, Research Scientist, Google Research “... perhaps the way [of translation] is t...

​Top 5 Metrics for Evaluating Your Deep Learning Program's GPU Performance - Exxact

https://blog.exxactcorp.com/top-5-metrics-for-evaluating-your-deep-learning-programs-gpu-performance/

🔗 Top 5 Metrics for Evaluating Your Deep Learning Program's GPU Performance - Exxact
In this blog article, we discuss the top 5 metrics for evaluating your deep learning program's GPU performance. You don't want to miss this one!

​Detection and Classification of Blood Cells with Deep Learning (Part 2 — Training and Evaluation)

🔗 Detection and Classification of Blood Cells with Deep Learning (Part 2 — Training and Evaluation)
Tackling the BCCD Dataset with Tensorflow Object Detection API

​Line Detection: Make an Autonomous Car see Road Lines

🔗 Line Detection: Make an Autonomous Car see Road Lines
Step by step you you can turn a video stream into a line detector via Computer Vision

🎥 [VDT19] Applied machine learning: a few lessons I learned the hard way by Alessandro Giusti
👁 1 раз ⏳ 2681 сек.
Machine Learning is easy; solving problems with machine learning is hard. In the last 10 years, I approached dozens of real-world problems with machine learning and deep learning, with varying degrees of success. In the process I made many mistakes and eventually learned how to avoid them. I noticed that many of these mistakes are pretty common among novice practitioners, so this talk might save you some time.