​All about AI with Cheat-Sheets(+100 Cheat-sheets), Free Online Books, Courses, Videos and Lectures, Papers, Tutorials, Researchers, Websites, Datasets, Conferences, Frameworks, Tools
https://github.com/Niraj-Lunavat/Artificial-Intelligence

🔗 Niraj-Lunavat/Artificial-Intelligence
Awesome AI Learning with +100 AI Cheat-Sheets, Free online Books, Top Courses, Best Videos and Lectures, Papers, Tutorials, +99 Researchers, Premium Websites, +121 Datasets, Conferences, Frameworks...

​What I have Learned After Building A Successful AI PoC
I recently completed an AI PoC that has reached production and I wanted to share what I have learned on how to improve the chances
https://towardsdatascience.com/what-i-have-learned-after-building-a-successful-ai-poc-3bd24efea4e2?source=collection_home---4------1-----------------------

🔗 What I have Learned After Building A Successful AI PoC
I recently completed an AI PoC that has reached production and I wanted to share what I have learned on how to improve the chances of any…

​Proximal Policy Optimization Tutorial (Part 1: Actor-Critic Method)
Let’s code from scratch an RL football agent!
https://towardsdatascience.com/proximal-policy-optimization-tutorial-part-1-actor-critic-method-d53f9afffbf6?source=collection_home---4------0-----------------------

🔗 Proximal Policy Optimization Tutorial (Part 1: Actor-Critic Method)
Let’s code from scratch an RL football agent!

Flask and Deep Learning Keras/TensorFlow Web Services (13.1)

https://www.youtube.com/watch?v=H73m9XvKHug

🎥 Flask and Deep Learning Keras/TensorFlow Web Services (13.1)
👁 2 раз ⏳ 1061 сек.
To make use of your neural network in a website or other external application you will typically wrap the neural network in a RESTful application programming interface (API) through HTTP or HTTPS. This video shows how to use Flask to expose your Keras neural network as a RESTful API service.

Code for This Video:
https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_13_01_flask.ipynb

Course Homepage: https://sites.wustl.edu/jeffheaton/t81-558/

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https://www.you

​MSURU: Large Scale E-commerce Image Classification With Weakly Supervised Search Data

https://research.fb.com/publications/msuru-large-scale-e-commerce-image-classification-with-weakly-supervised-search-data/

🔗 MSURU: Large Scale E-commerce Image Classification With Weakly Supervised Search Data
In this paper we present a deployed image recognition system used in a large scale commerce search engine, which we call MSURU. It is designed to process product images uploaded daily to Facebook Marketplace. Social commerce is a growing area within Facebook and understanding visual representations of product content is important for search and recommendation applications on Marketplace.

​Дайджест соревнований по анализу данных – Сергей Брянский

🔗 Дайджест соревнований по анализу данных – Сергей Брянский
Обзор новых соревнований по анализу данных от 10 августа 2019 года. Сергей Брянский рассказывает про актуальные соревнования, в которых можно принять участие. Календарь соревнований: http://mltrainings.ru/ Узнать о новых тренировках и видео можно из групп: ВКонтакте https://vk.com/mltrainings Facebook https://www.facebook.com/groups/1413405125598651/ Telegram https://xn--r1a.website/mltrainings

​AutoDL 2019

https://autodl.chalearn.org/

🔗 AutoDL challenges

​RAPIDS: The platform inside and outside - Joshua Patterson | ODSC East 2019

🔗 RAPIDS: The platform inside and outside - Joshua Patterson | ODSC East 2019
Python has seen terrific progress as the data science language of choice. With the introduction of Pandas, users could interact with data in python in a way that fells intuitive. In addition, open-source packages such as Scikit-Learn have democratized and accelerated data science. RAPIDS seeks to have a similar impact on the Python data science community by accelerating data science with GPUs. RAPIDS is an open-source suite of tools for GPU data science. Launched in October, RAPIDS includes cuDF, a libr

​NVIDIA Clocks World’s Fastest BERT Training Time and Largest Transformer Based Model, Paving Path For Advanced Conversational AI

https://devblogs.nvidia.com/training-bert-with-gpus/

🔗 NVIDIA Clocks World’s Fastest BERT Training Time and Largest Transformer Based Model, Paving Path Fo
NVIDIA DGX SuperPOD trains BERT-Large in just 53 minutes, and trains GPT-2 8B, the largest Transformer Network Ever with 8.3Bn parameters Conversational AI is an essential building block of human interactions with intelligent machines and applications – from robots and cars, to home assistants and mobile apps. Getting computers to understand human languages, with all their …

​Видеокурс по нейронным сетям в Университете Шербрук
https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH - полный курс
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Neural networks class - Université de Sherbrooke - YouTube
These are the videos I use to teach my Neural networks class at Université de Sherbrooke. The videos, along with the slides and research paper references, ar...


🎥 Neural networks [1.1] : Feedforward neural network - artificial neuron
👁 1 раз ⏳ 471 сек.


🎥 Neural networks [1.2] : Feedforward neural network - activation function
👁 1 раз ⏳ 356 сек.


🎥 Neural networks [1.3] : Feedforward neural network - capacity of single neuron
👁 1 раз ⏳ 485 сек.


🎥 Neural networks [1.4] : Feedforward neural network - multilayer neural network
👁 1 раз ⏳ 791 сек.

​Leading Data Science Teams: A Framework To Help Guide Data Science Project Managers - Jeffrey Saltz

🔗 Leading Data Science Teams: A Framework To Help Guide Data Science Project Managers - Jeffrey Saltz
Data science managers (and senior leaders managing data science teams) need to think through many questions relating to how to best execute their data science efforts. For example, what is the most effective way to lead a data science project? How to make sure my data science team does not expose my organization to issues relating to the misuse of data and/or algorithms? How do I validate the results provided by the data science team? This video will provide a framework managers can use to help ensure a

​We are excited to release Behaviour Suite for Reinforcement Learning, or ‘bsuite’ – a collection of carefully-designed experiments that investigate core capabilities of RL agents

GitHub: http://github.com/deepmind/bsuite /
Paper: https://arxiv.org/abs/1908.03568v1

🔗 deepmind/bsuite
bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent - deepmind/bsuite

​Project Euphonia’s Personalized Speech Recognition for Non-Standard Speech

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
http://ai.googleblog.com/2019/08/project-euphonias-personalized-speech.html

🔗 Project Euphonia’s Personalized Speech Recognition for Non-Standard Speech
Posted by Joel Shor and Dotan Emanuel, Research Engineers, Google Research, Tel Aviv The utility of technology is dependent on its acces...

​Biological Data Science and Why Domain Expertise and Context is King in
If I showed you a picture of a cat and told you it was a pink panda bear, would you believe me?

https://towardsdatascience.com/biological-data-science-and-why-domain-expertise-and-context-is-king-in-55562787721?source=collection_home---4------4-----------------------

🔗 Biological Data Science and Why Domain Expertise and Context is King
If I showed you a picture of a cat and told you it was a pink panda bear, would you believe me?

​The Martian Chronicles — When Deep Learning meets Global Collaboration
36 Earthlings from 18 countries, working together for 2 months, to identify anomalies on the surface of Mars using Deep Learning

https://towardsdatascience.com/the-martian-chronicles-when-deep-learning-meets-global-collaboration-872425ba2787?source=collection_home---4------0-----------------------

🔗 The Martian Chronicles — When Deep Learning meets Global Collaboration
36 Earthlings from 18 countries, working together for 2 months, to identify anomalies on the surface of Mars using Deep Learning

​An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents

https://arxiv.org/abs/1812.07069
https://github.com/uber-research/atari-model-zoo/

🔗 uber-research/atari-model-zoo
A binary release of trained deep reinforcement learning models trained in the Atari machine learning benchmark, and a software release that enables easy visualization and analysis of models, and co...

​📝 Bobkov_A_V_-_Sistemy_raspoznavania_obrazov_-_2018.pdf - 💾60 072 928

🎥 When science fiction becomes the tester's reality: deep learning and IoT testing
👁 1 раз ⏳ 1495 сек.
Jaroslaw Hryszko's talk at SQA Days-25 conference. May 31-June 1. Saint Petersburg. Russia
www.sqadays.com

Deep Learning with Keras - Python

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
- Deep Learning with Keras and Python (Course Introduction)
- Convolutional Neural Networks (CNN) in Keras - Python
- Word2vec with Gensim - Python
- Recurrent Neural Networks (RNN / LSTM )with Keras - Python
- LSTM input output shape , Ways to improve accuracy of predictions in Keras
- Deep Learning Chatbot using Keras and Python - Part I (Pre-processing text for inputs into LSTM)
- Deep Learning Chatbot using Keras and Python - Part 2 (Text/word2vec inputs into LSTM)
- Activation Functions in Neural Networks (Sigmoid, ReLU, tanh, softmax)
- Object Recognition App for Visually Impaired
- Perceptron and Gradient Descent Algorithm - Scikit learn
- Neural Networks and Backpropogation Scikit learn

#video

🎥 Deep Learning with Keras and Python (Course Introduction)
👁 1 раз ⏳ 100 сек.
Deep Learning has been a hot area of interest. Through this series we start learning some famous deep learning models like Deep Neural Networks, Re...

🎥 Convolutional Neural Networks (CNN) in Keras - Python
👁 1 раз ⏳ 756 сек.
In this tutorial we learn to make a convnet or Convolutional Neural Network or CNN in python using keras library with theano backend. It is okay if...

🎥 Word2vec with Gensim - Python
👁 1 раз ⏳ 557 сек.
This video explains word2vec concepts and also helps implement it in gensim library of python.

Word2vec extracts features from text and assigns v...

🎥 Recurrent Neural Networks (RNN / LSTM )with Keras - Python
👁 1 раз ⏳ 711 сек.
In this tutorial, we learn about Recurrent Neural Networks (LSTM and RNN). Recurrent neural Networks or RNNs have been very successful and popular...

🎥 LSTM input output shape , Ways to improve accuracy of predictions in Keras
👁 1 раз ⏳ 637 сек.
In this tutorial we look at how we decide the input shape and output shape for an LSTM.
We also tweak various parameters like Normalization, Activ...

🎥 Deep Learning Chatbot using Keras and Python - Part I (Pre-processing text for inputs into LSTM)
👁 1 раз ⏳ 384 сек.
This is the first part of tutorial for making our own Deep Learning or Machine Learning chat bot using keras.
In this video we pre-process a conv...

🎥 Deep Learning Chatbot using Keras and Python - Part 2 (Text/word2vec inputs into LSTM)
👁 1 раз ⏳ 486 сек.
This is the second part of tutorial for making our own Deep Learning or Machine Learning chat bot using keras.
In this video we input our pre-pr...

🎥 Activation Functions in Neural Networks (Sigmoid, ReLU, tanh, softmax)
👁 1 раз ⏳ 276 сек.
Activation Functions in Neural Networks are used to contain the output between fixed values and also add a non linearity to the output.

Activatio...

🎥 Object Recognition App for Visually Impaired
👁 1 раз ⏳ 65 сек.
This App was made for IBM I-Care Watson challenge by Sai Keshav Kolluru, Shreyans Shrimal and Sudharsan Krishnaswamy of IIT Bhubaneswar.

We used...

🎥 Perceptron and Gradient Descent Algorithm - Scikit learn
👁 1 раз ⏳ 425 сек.
The Perceptron Algorithm is generally used for classification and is much like the simple regression. The weights of the perceptron are trained usi...

​Is Deep Reinforcement Learning Really Superhuman on Atari?
https://arxiv.org/abs/1908.04683

🔗 Is Deep Reinforcement Learning Really Superhuman on Atari?
Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is not straightforward. In the Arcade Learning Environment (ALE), small changes in environment parameters such as stochasticity or the maximum allowed play time can lead to very different performance. In this work, we discuss the difficulties of comparing different agents trained on ALE. In order to take a step further towards reproducible and comparable DRL, we introduce SABER, a Standardized Atari BEnchmark for general Reinforcement learning algorithms. Our methodology extends previous recommendations and contains a complete set of environment parameters as well as train and test procedures. We then use SABER to evaluate the current state of the art, Rainbow. Furthermore, we introduce a human world records baseline, and argue that previous claims of expert or superhuman performance of DRL might not be accurate. Finally, we propose Rainbow-IQN by extending Rainbow with Implicit Quantile Networks (IQN) leading to new state-of-the-art

🎥 Automatic Mathematics
👁 1 раз ⏳ 842 сек.
There are 7 math problems that each have a 1 million dollar prize attached to them by the Clay Mathematics Institute. Only 1 of these problems have been solved so far, meaning the other 6 are open to anyone in the world with the proper motivation to solve. In this episode, I'm going to explain how recent advancements in Artificial Intelligence can help an individual solve these problems and possibly win the prize money. There are 2 specific techniques I have in mind, the newly released "Ramanujan Machine" w