AI, Python, Cognitive Neuroscience
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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
VideoBERT: A Joint Model for Video and Language Representation Learning

Sun et al.: https://lnkd.in/ek7MYKP

#ComputerVision #PatternRecognition #ArtificialIntelligence

✴️ @AI_Python_EN
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Prof. Chris Manning, Director of StanfordAILab & founder of Stanfordnlp, shared inspiring thoughts on research trends and challenges in #computervision and #NLP at #CVPR2019. View full interview:

http://bit.ly/2KR21hO

✴️ @AI_Python_EN
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just published my (free) 81-page guide on learning #ComputerVision, #DeepLearning, and #OpenCV!
Includes step-by-step instructions on:
- Getting Started
- Face Applications
- Object Detection
- OCR
- Embedded/IOT
- and more!
Check it out here:
http://pyimg.co/getstarted
And if you liked it, please do give it a share to spread the word. Thank you!
#Python #Keras #MachineLearning #ArtificialIntelligence #AI

❇️ @AI_Python_EN
ICCV 2019 | Best Paper Award: SinGAN: Learning a Generative Model from a Single Natural Image
https://lnkd.in/fS3ZBAP

ICCV 2019 | Best Student Paper Award: PLMP — Point-Line Minimal Problems in Complete Multi-View Visibility
https://lnkd.in/f7CDuq2

ICCV 2019 | Best Paper Honorable Mentions
Paper: Asynchronous Single-Photon 3D Imaging
https://lnkd.in/fMpQPCj

Paper: Specifying Object Attributes and Relations in Interactive Scene Generation
https://lnkd.in/fmjk9eZ

You can find all papers on the ICCV 2019 open access website:
https://lnkd.in/gaBwvS4

Source: Synced

#machinelearning #deeplearning #computervision #iccv2019

❇️ @AI_Python_EN
New tutorial! Traffic Sign Classification with #Keras and #TensorFlow 2.0

- 95% accurate
- Includes pre-trained model
- Full tutorial w/ #Python code

http://pyimg.co/5wzc5

#DeepLearning #MachineLearning #ArtificialIntelligence #DataScience #AI #computervision

❇️ @AI_Python_EN
Vanishing/exploring gradients problem is a well often problem especially when training big networks, so visualizing gradients is a must when training neural networks. Here is the small network's on MNIST dataset gradients flow. A detailed article is on the way to explain many things in deep learning.

#machinelearning #deeplearning #artificialintelligence #computervision #neuralnetwork

❇️ @AI_Python_EN
Free 81-page guide on learning #ComputerVision, #DeepLearning, and #OpenCV!

Includes step-by-step instructions on:
- Getting Started
- Face Applications
- Object Detection
- OCR
- Embedded/IoT
- ...and more

https://www.pyimagesearch.com/start-here