​Ethics of Facial Recognition: How to Make Business Uses Fair and Transparent

🔗 Ethics of Facial Recognition: How to Make Business Uses Fair and Transparent
With the growing popularity of computer vision and facial recognition, businesses strive to adopt innovations to keep heads above water…

​Build your own Whatsapp Chat Analyzer

🔗 Build your own Whatsapp Chat Analyzer
A step-by-step guide using Python

​Announcing the 6th Fine-Grained Visual Categorization Workshop

🔗 Announcing the 6th Fine-Grained Visual Categorization Workshop
Posted by Christine Kaeser-Chen, Software Engineer and Serge Belongie, Visiting Faculty, Google AI In recent years, fine-grained visual ...

🎥 Deep Neural Networks for Artificial Intelligence and Machine Learning by Dr Malleswar Yenugu
👁 2 раз ⏳ 3035 сек.
This is a lecture on how the neural networks be used for applications of Artificial Intelligence and Machine Learning...Hope you will like it.
Thanks
Dr. Malleswar Yenugu

Oriol Vinyals: DeepMind AlphaStar, StarCraft, and Language | Artificial Intelligence Podcast
https://www.youtube.com/watch?v=Kedt2or9xlo

🎥 Oriol Vinyals: DeepMind AlphaStar, StarCraft, and Language | Artificial Intelligence Podcast
👁 2 раз ⏳ 6361 сек.
Oriol Vinyals is a senior research scientist at Google DeepMind. Before that he was at Google Brain and Berkeley. His research has been cited over 39,000 times. He is one of the most brilliant and impactful minds in the field of deep learning. He is behind some of the biggest papers and ideas in AI, including sequence to sequence learning, audio generation, image captioning, neural machine translation, and reinforcement learning. He is a co-lead (with David Silver) of the AlphaStar project, creating an agen

​Unsupervised Learning Project: Creating Customer Segments

🔗 Unsupervised Learning Project: Creating Customer Segments
Learn how to develop and end-to-end Clustering and Dimensionality Reduction Project!

🎥 Stanford HAI 2019 - Introduction to Stanford HAI: Fei-Fei Li
👁 1 раз ⏳ 1678 сек.

​Detecting faces with Python and OpenCV Face Detection Neural Network

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

https://medium.com/@himashaharinda/detecting-faces-with-python-and-opencv-face-detection-neural-network-f72890ae531c?source=topic_page---------0------------------1

🔗 Detecting faces with Python and OpenCV Face Detection Neural Network
Now, we all know that Artificial Intelligence is becoming more and more real and its filling the gaps between capabilities of humans and…

🎥 Neural Networks and Python: Image Classification -- Part 2
👁 1 раз ⏳ 818 сек.
General Description:
In this series of videos, we will be using the TensorFlow Python module to construct a neural network that classifies whether a given image of an article of clothing

We will be obtaining image data from the Fashion MNIST dataset. The intent of these videos is to showcase the use of TensorFlow as well as showing a simple example of how to construct and use a simple neural network.

This video is part of a series on Machine Learning in Python. The link to the playlist may be accessed her

​Machine Learning to Big Data — Scaling Inverted Indexing with Solr

🔗 Machine Learning to Big Data — Scaling Inverted Indexing with Solr
Motivation

​Taming Recurrent Neural Networks for Better Summarization
http://www.abigailsee.com/2017/04/16/taming-rnns-for-better-summarization.html

🔗 Taming Recurrent Neural Networks for Better Summarization | Abigail See
This is a blog post about our latest paper, Get To The Point: Summarization with Pointer-Generator Networks, to appear at ACL 2017. The code is available here.

🎥 Exploring And Attacking Neural Networks With Activation Atlases
👁 1 раз ⏳ 245 сек.
📝 The paper "Exploring Neural Networks with Activation Atlases" is available here:
https://distill.pub/2019/activation-atlas/

❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Bruno Brito, Bryan Learn, Christian Ahlin, Christoph Jadanowski, Claudio Fernandes, Dennis Abts, Eric Haddad, Eric M

🎥 Data Science Tutorial - NO EXP REQUIRED | Python - #grindreel #lambdaschool
👁 1 раз ⏳ 858 сек.
🔥 Land the job! Get help with a resume and cover letter https://bit.ly/2CNoxTm
📚My Courses: https://grindreel.academy/
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​bentoML: One Model to Rule Them All

🔗 bentoML: One Model to Rule Them All
The machine learning community focuses too much on predictive performance. But machine learning models are always a small part of a complex system. This post discusses our obsession with finding the best model and emphasizes what we should do instead: Take a step back and see the bigger picture in which the machine learning model is embedded.

🎥 AI in 2040
👁 18 раз ⏳ 781 сек.
What does the field of Artificial Intelligence look like in 2040? It's a really hard question to answer since there are still so many unanswered questions about the nature of reality and computing. In this episode, I'll make my best predictions about AI hardware, AI software, and the societal impact of AI in 2040. We'll cover quantum mechanics, neuromorphic computing, DNA storage, decentralized computing, basic income, and mind-body machines. Enjoy!

Code for this video:
https://github.com/llSourcell/quant

🎥 Deep neural networks step by step forward propagation #part 2
👁 1 раз ⏳ 1243 сек.
Now when we have initialized our parameters, we will do the forward propagation module. We will start by implementing some basic functions that we will use later when implementing the model. We will complete three functions in this order:
• LINEAR
• LINEAR - ACTIVATION where ACTIVATION will be either ReLU or Sigmoid.
• [LINEAR - RELU] × (L-1) - LINEAR - SIGMOID (whole model)

I could write all these functions in one block, but then it's harder to understand code, so I will leave it so for learning purposes.

🎥 Theoretical Deep Learning. The Information Bottleneck method. Part 2
👁 1 раз ⏳ 5876 сек.
In this class we continue discussing how can we use the information bottleneck framework for neural networks study. In particular, we learn how to prevent NNs from overfitting by introducing a specific penalty term into the loss function, and reveal an objective very similar to evidence lower bound from bayesian statistics.

Find out more: https://github.com/deepmipt/tdl

Our open-source framework to develop and deploy conversational assistants: https://deeppavlov.ai/

​http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/

🔗

​Liberty Mutual Insurance joins MIT's Quest for Intelligence

http://news.mit.edu/2019/liberty-mutual-insurance-establishes-artificial-intelligence-collaboration-mit-0430

🔗 Liberty Mutual Insurance joins MIT's Quest for Intelligence
Company announces $25 million, five-year collaboration.