Reza Zadeh:

Creating super slow motion videos by predicting missing frames using a neural network, instead of simple interpolation. With code.

Code: https://github.com/avinashpaliwal/Super-SloMo

Project: https://people.cs.umass.edu/~hzjiang/projects/superslomo/

#AI #DeepLearning #MachineLearning #DataScience #neuralnetwork


✴️ @AI_Python_EN
🗣 @AI_Python_arXiv
❇️ @AI_Python

Deep Learning for beating Traffic is a sound idea given the fact that on an average we almost waste a week in traffic each year.

There is a great course on Deep learning by MIT through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of #deeplearning methods and their application.

Also, the program has some cool programming challenge to test you on the concepts like #DeepTraffic wherein you have to create a #neuralnetwork to drive a vehicle (or multiple vehicles) as fast as possible through dense traffic.

Link to Course: https://lnkd.in/fGbjB3y

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

Here are the COMPLETE Lecture notes on Professor Andrew Ng's


Stanford Machine Learning Lecture: https://lnkd.in/gR5sRHg

#lecturing #machinelearning #beginner #artificialintellegence #fundamentals #artificailintelligence #neuralnetwork #repository #datascientists #computervision #neuralnetworks

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

Do Neural Networks Need To Think Like Humans?

#neuralnetwork


https://www.youtube.com/watch?v=YFL-MI5xzgg

✴️ @AI_Python_EN

How neural networks learn' - Part III: The learning dynamics behind generalization and overfitting:


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

#neuralnetwork

✴️ @AI_Python_EN

NODE - Neural Ordinary Differential Equations

This was recently presented as a new approach in NeurIPS.

The idea?
Instead of specifying a discrete sequence of hidden layers, they parameterized the derivative of the hidden state using a neural network. The output of the network is computed using a black- box differential equation solver.

They also propose CNF - or Continuous Normalizing Flow

The continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions. For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. This allows end-to-end training of ODEs within larger models.

Paper: https://lnkd.in/ddMJQAS
#Github: Examples of implementations coming soon to our repository
#neuralnetwork #deeplearning #machinelearning

✴️ @AI_Python_EN

Brandon Rohrer is a data scientist at Facebook. He's very knowledgeable in Machine Learning and knows how to explain complex concepts in an easy to understand manner.
Here comes his free course on #deeplearning #neuralnetwork #DeepNeuralNetworks.

How Deep Neural Networks Work

https://end-to-end-machine-learning.teachable.com/p/how-deep-neural-networks-work/


✴️ @AI_Python_EN

THE IMPACT OF OVERFITTING AND HOW AVOID THEM?


12-years-old Tanmay Bakhsi (today 14) tell about Incredibly well spoken manner about what is overfitting and how to avoid them. He told difference about deep vs shallow neural network and difference about how to train them

Qlue: The solution is early stopping algorithm

Interested to know more, Full video with best resolution is avaliable in his youtube channel: https://lnkd.in/fzrFCvU

#deepleaning #neuralnetwork #artificailintelligence

✴️ @AI_Python_EN

In a paper published two days ago in Nature, a group of scientists designed a recurrent neural network that decoded cortical signals to speech signals.
This problem considered much harder than decoding muscle movement from brain signal as the signals that responsible for spoken words are much difficult to decode.
Nature (paywall): https://lnkd.in/fM8EsuE
direct link to pdf: https://lnkd.in/ftrEbe5
#ai #neuralnetwork #science #rnn #neuroscience

✴️ @AI_Python_EN

Interesting paper with a simple and straightforward explanation about NN pruning, based on the following hypothesis:

"Dense, randomly-initialized, feed-forward networks contain subnetworks that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations."

#machinelearning #deeplearning #neuralnetwork #NN

✴️ @AI_Python_EN