How to make a pizza: Learning a compositional layer-based GAN model. Or “MIT’s AI learns to Become Pizza Guru. All pizza design will soon be automated. ”
https://arxiv.org/abs/1906.02839
#gan #ai #computervision

✴️ @AI_Python_EN

Check out Scene Representation Networks:
https://youtu.be/6vMEBWD8O20
new continuous 3D-aware scene representation reconstructs appearance and geometry just from posed images, generalizes across scenes for single-shot reconstruction, and naturally handles non-rigid deformation!
https://arxiv.org/abs/1906.01618
#computervision

✴️ @AI_Python_EN

All the datasets (there are a lot) released at #cvpr2019 are now indexed in
http://visualdata.io . Check them out!
#computervision #machinelearning #dataset

✴️ @AI_Python_EN

TensorFlow 2.0 Beta has just been released!! This time, I am a big fan. The new version is so good, so easy & intuitive, and game changing compared to the previous TensorFlow 1 versions. It has such massive value that I decided to make a huge course on TensorFlow 2.0, covering most of the useful models in Deep Learning and Artificial Intelligence. Seriously this is one of the most complete guides I’ve ever made: inside we implement ANNs, CNNs, RNNs, Deep Q-Learning, Transfer Learning, Fine Tuning, APIs for Mobile Apps, Computer Vision, Deep NLP, Data Validation, TensorFlow Extended and even Distributed Training handling multiple GPUs, all that in TensorFlow 2.0!

And that’s not all, during these first 72 hours you get three amazing Bonuses, including the highly demanded Yolo v3, one of the most powerful models in Computer Vision.

Link here:
https://lnkd.in/gBtZuMN

#machinelearning #deeplearning, #artificialintelligence #computervision #nlp #completeguide
✴️ @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

VideoBERT: A Joint Model for Video and Language Representation Learning

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

#ComputerVision #PatternRecognition #ArtificialIntelligence

✴️ @AI_Python_EN

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

10 new posts on datahacker.rs.
Introduction to #ComputerVision using #OpenCV (#Python and #C++)

https://lnkd.in/gZtj_g6

✴️ @AI_Python_EN

Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
https://deepai.org/publication/improving-deep-lesion-detection-using-3d-contextual-and-spatial-attention by Qingyi Tao et al.
#ComputerVision #PatternRecognition

✴️ @AI_Python_EN

.

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