Unet Segmentation in Keras TensorFlow

🎥 Unet Segmentation in Keras TensorFlow
👁 1 раз ⏳ 2116 сек.
About: This video is all about the most popular and widely used Segmentation Model called UNET. UNet is built for biomedical Image Segmentation. It is base model for any segmentation task. It follows a encoder decoder approach. It used skip connection to get the local information during down sampling path,
and use it during upsampling path.

The UNet is built using Keras TensorFlow.

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CODE: https://github.com/nikhilroxtomar/UNet-Segmentation-in-Ke

​🔗 4 Machine Learning Techniques with Python – Rinu Gour – Medium
4 Machine Learning Techniques with Python

​This is a super cool resource: Papers With Code now includes 950+ ML tasks, 500+ evaluation tables (including SOTA results) and 8500+ papers with code. Probably the largest collection of NLP tasks I've seen including 140+ tasks and 100 datasets.

https://paperswithcode.com/sota

🔗 Browse the State-of-the-Art in Machine Learning
522 leaderboards • 967 tasks • 708 datasets • 9199 papers with code.

Matplotlib Tutorial: Learn basics of Python’s powerful Plotting library
https://towardsdatascience.com/matplotlib-tutorial-learn-basics-of-pythons-powerful-plotting-library-b5d1b8f67596?source=collection_home---4------1---------------------

Machine Learning Chatbot with Tensorflow

🎥 Machine Learning Chatbot with Tensorflow
👁 1 раз ⏳ 1044 сек.
In this video, I’ll share the GitHub project I followed along to build a chatbot.

If you want the full tutorial, you can find it on Sentdex https://pythonprogramming.net/chatbot-deep-learning-python-tensorflow/

Here are the content links of this video. You can find it on out github at
https://github.com/KnowledgeMavens/chatbot
Build Your Own Chatbot
Can be used to for Twitter - Charles the AI - https://twitter.com/Charles_the_AI?lang=en
Build custom chatbot
Download Dataset https://www.reddit.com/r/datas

​The power of deeper networks for expressing natural functions”,

Rolnick and Tegmark: https://arxiv.org/abs/1705.05502

#artificalintelligence #deeplearning #machinelearning

🔗 The power of deeper networks for expressing natural functions

Kaggle PLAsTiCC: классификация космических объектов — Сергей Злобин

🎥 Kaggle PLAsTiCC: классификация космических объектов — Сергей Злобин
👁 1 раз ⏳ 2265 сек.
Сергей Злобин рассказывает про задачу классификации космических объектов (Kaggle PLAsTiCC Astronomical Classification), в которой Сергею вместе с его командой не хватило всего одного места до золота. Из видео вы сможете узнать про признаки на основе подгонки кривых под точки, аугментацию астрономических данных и эвристические правки предсказаний отдельных классов.

Слайды: https://gh.mltrainings.ru/presentations/Zlobin_KagglePlasticc_2019.pdf

Узнать о текущих соревнованиях можно на сайте http://mltraining

​https://m.youtube.com/watch?v=I2R6wmDwD8o

🔗

Model-Free Prediction: Reinforcement Learning
https://towardsdatascience.com/model-free-prediction-reinforcement-learning-507297e8e2ad?source=collection_home---4------1---------------------

​So many papers applying deep learning to theoretical and experimental physics!
Fascinating.

https://physicsml.github.io/pages/papers.html

🔗 Papers | 〈 physics | machine learning 〉

​Amazing Machine Learning Open Source of the Year (v.2019)
https://medium.mybridge.co/amazing-machine-learning-open-source-tools-projects-of-the-year-v-2019-95d772e4e985

🔗 Amazing Machine Learning Open Source Tools & Projects of the Year (v.2019)
For the past year, we’ve compared nearly 22,000 Machine Learning open source tools and projects to pick Top 49 (0.22% chance).

https://habr.com/ru/company/mailru/blog/438392/

Краткая история одной «умной ленты»

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

​Improving Evolutionary Strategies with Generative Neural Networks

https://arxiv.org/abs/1901.11271

🔗 Improving Evolutionary Strategies with Generative Neural Networks
Evolutionary Strategies (ES) are a popular family of black-box zeroth-order optimization algorithms which rely on search distributions to efficiently optimize a large variety of objective functions. This paper investigates the potential benefits of using highly flexible search distributions in classical ES algorithms, in contrast to standard ones (typically Gaussians). We model such distributions with Generative Neural Networks (GNNs) and introduce a new training algorithm that leverages their expressiveness to accelerate the ES procedure. We show that this tailored algorithm can readily incorporate existing ES algorithms, and outperforms the state-of-the-art on diverse objective functions.

​Article Image
Understanding Neural ODE's

https://jontysinai.github.io/jekyll/update/2019/01/18/understanding-neural-odes.html

🔗 Understanding Neural ODE's
In this blogpost I explore how ODE’s can be used to solve data modelling problems. I take a deep dive into the data modelling problem at hand and present ODE’s (which model rates of change) as an a...

​Maximum Likelihood Estimation

🔗 Maximum Likelihood Estimation – Towards Data Science
Fundamentals of Machine Learning (Part 2)

Unmaking Graphs
https://towardsdatascience.com/unmaking-graphs-f584625c5bfd?source=collection_home---4------1---------------------

​Building a Neural Network Only Using NumPy

🔗 Building a Neural Network Only Using NumPy – Lukas Frei – Medium
Using Andrew Ng’s Project Structure to Build a Neural Net in Python

​Can neural networks learn commonsense reasoning?

ATOMIC | An Atlas of Machine Commonsense for If-Then Reasoning: https://homes.cs.washington.edu/~msap/atomic/

🔗 ATOMIC Knowledge Graph Browser

Making deep neural networks paint to understand how they work
https://towardsdatascience.com/making-deep-neural-networks-paint-to-understand-how-they-work-4be0901582ee?source=collection_home---4------0---------------------

​Machine Learning Tutorial Python - 13: K Means Clustering

🔗 Machine Learning Tutorial Python - 13: K Means Clustering
Code: https://github.com/codebasics/py/blob/master/ML/13_kmeans/13_kmeans_tutorial.ipynb K Means algorithm is unsupervised machine learning technique used to...