AI, Python, Cognitive Neuroscience
3.86K subscribers
1.09K photos
47 videos
78 files
892 links
Download Telegram
Network Science meets Deep Learning

By Vinay Uday Prabhu: https://lnkd.in/e78XRWx

#deeplearning #neuralnetworks #technology

✴️ @AI_Python_EN
"Neural Networks for Machine Learning by Geoffrey Hinton" (Coursera 2013)

Video Lectures: https://lnkd.in/eSJjXGd

#ArtificialIntelligence #DeepLearning #NeuralNetworks #MachineLearning

✴️ @AI_Python_EN
"Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups"

By Thomas Wolf: https://lnkd.in/etyMzjQ

#ArtificialInteligence #DeepLearning #MachineLearning #NeuralNetworks #Research

✴️ @AI_Python_EN
image_2019-04-27_21-09-51.png
1.1 MB
The best way to learn #DeepLearning is by practicing it. But which framework to use? Here are 5 articles to get you started!

A Comprehensive Introduction to #PyTorch - https://bit.ly/2L8Rj7n

Learn How to Build Quick & Accurate Neural Networks using PyTorch (& 4 Case Studies) - https://bit.ly/2Vts9nY

Get Started with Deep Learning using #Keras and #TensorFlow in #R - https://bit.ly/2Iro2BY

TensorFlow 101: Understanding Tensors and Graphs - https://bit.ly/2GNg195

An Introduction to Implementing #NeuralNetworks using TensorFlow - https://bit.ly/2V17cBs

✴️ @AI_Python_EN
Swift + TensorFlow

Create a simple NN and CNN.

Notebook by Zaid Alyafeai: https://lnkd.in/e5zWxZ5

#ArtificialIntelligence #DeepLearning #NeuralNetworks

✴️ @AI_Python_EN
Walkthrough - When and How to Use MLP, CNN, and RNN Neural Networks - Jason Brownlee

To follow posts: https://lnkd.in/ev9S2hh

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #MLP #CNN #RNN #neuralnetworks

✴️ @AI_Python_EN
Can #neuralnetworks be made to reason?" Conversation with Ian Goodfellow

Full version: https://www.youtube.com/watch?v=Z6rxFNMGdn0


✴️ @AI_Python
Understanding Neural Networks via Feature Visualization: A survey

Nguyen et al.: https://lnkd.in/eRZMuTS

#neuralnetworks #generatornetwork #generativemodels

✴️ @AI_Python_EN
ery interesting paper on machine learning algorithms. This paper compares polynomial regression vs neural networks applying on several well known datasets (including MNIST). The results are worth looking.

Other datasets tested: (1) census data of engineers salaries in Silicon Valley; (2) million song data; (3) concrete strength data; (4) letter recognition data; (5) New York city taxi data; (6) forest cover type data; (7) Harvard/MIT MOOC course completion data; (8) amateur athletic competitions; (9) NCI cancer genomics; (10) MNIST image classification; and (11) United States 2016 Presidential Election.

I haven't reproduced the paper myself but I am very tempted in doing it.

Link here: https://lnkd.in/fd-VNtk

#machinelearning #petroleumengineering #artificialintelligence #data #algorithms #neuralnetworks #predictiveanalytics

✴️ @AI_Python_EN
Convolutional #NeuralNetworks (CNN) for Image Classification — a step by step illustrated tutorial: https://dy.si/hMqCH
BigData #AI #MachineLearning #ComputerVision #DataScientists #DataScience #DeepLearning #Algorithms

✴️ @AI_Python_EN
This is the reference implementation of Diff2Vec - "Fast Sequence Based Embedding With Diffusion Graphs" (CompleNet 2018). Diff2Vec is a node embedding algorithm which scales up to networks with millions of nodes. It can be used for node classification, node level regression, latent space community detection and link prediction. Enjoy!

https://lnkd.in/dXiy5-U

#technology #machinelearning #datamining #datascience #deeplearning #neuralnetworks #pytorch #tensorflow #diffusion #Algorithms

✴️ @AI_Python_EN