Image Classification using Transfer Learning in PyTorch
https://www.learnopencv.com/image-classification-using-transfer-learning-in-pytorch/
https://www.learnopencv.com/image-classification-using-transfer-learning-in-pytorch/
LearnOpenCV – Learn OpenCV, PyTorch, Keras, Tensorflow with code, & tutorials
Transfer Learning For PyTorch Image Classification
Transfer Learning with Pytorch for precise image classification: Explore how to classify ten animal types using the CalTech256 dataset for effective results.
Generative Modeling with Sparse Transformers
https://openai.com/blog/sparse-transformer/
https://openai.com/blog/sparse-transformer/
Openai
Generative modeling with sparse transformers
We’ve developed the Sparse Transformer, a deep neural network which sets new records at predicting what comes next in a sequence—whether text, images, or sound. It uses an algorithmic improvement of the attention mechanism to extract patterns from sequences…
COURSE
[CSCI-GA.2566-001] Fall 2018 Foundations of Machine Learning.
https://cs.nyu.edu/~mohri/courses.html
[CSCI-GA.2566-001] Fall 2018 Foundations of Machine Learning.
https://cs.nyu.edu/~mohri/courses.html
Deep Learning for Speech and Language
2nd Winter School
at Universitat Politècnica de Catalunya (2018)
https://telecombcn-dl.github.io/2018-dlsl/
2nd Winter School
at Universitat Politècnica de Catalunya (2018)
https://telecombcn-dl.github.io/2018-dlsl/
telecombcn-dl.github.io
Deep Learning for Artificial Intelligence
Deep Learning for Speech and Language 2018
A collection of research papers on decision, classification and regression trees with implementations.
https://github.com/benedekrozemberczki/awesome-decision-tree-papers
https://github.com/benedekrozemberczki/awesome-decision-tree-papers
GitHub
GitHub - benedekrozemberczki/awesome-decision-tree-papers: A collection of research papers on decision, classification and regression…
A collection of research papers on decision, classification and regression trees with implementations. - benedekrozemberczki/awesome-decision-tree-papers
SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices.
SOD - An Embedded Computer Vision & Machine Learning Library
https://sod.pixlab.io/
api https://sod.pixlab.io/api.html
samples https://sod.pixlab.io/samples.html
guide https://sod.pixlab.io/intro.html
github https://github.com/symisc/sod
SOD - An Embedded Computer Vision & Machine Learning Library
https://sod.pixlab.io/
api https://sod.pixlab.io/api.html
samples https://sod.pixlab.io/samples.html
guide https://sod.pixlab.io/intro.html
github https://github.com/symisc/sod
sod.pixlab.io
SOD - An Embedded, Modern Computer Vision and Machine Learning Library
SOD is an embedded, cross-platform computer vision and machine learning library that exposes a set of APIs for deep-learning, advanced media processing & analysis including real-time multi-class object detection.
Yann LeCun
ISSCC 2019: Deep Learning Hardware: Past, Present, and Future - Yann LeCun
slides
https://drive.google.com/file/d/1OeEULePrBCP17ofTaa2ThzwObHsY9KZg/view
video
https://www.youtube.com/watch?v=YzD7Z2yRL7Y&feature=youtu.be
ISSCC 2019: Deep Learning Hardware: Past, Present, and Future - Yann LeCun
slides
https://drive.google.com/file/d/1OeEULePrBCP17ofTaa2ThzwObHsY9KZg/view
video
https://www.youtube.com/watch?v=YzD7Z2yRL7Y&feature=youtu.be
YouTube
ISSCC 2019: Deep Learning Hardware: Past, Present, and Future - Yann LeCun
Yann LeCun, Facebook AI Research & New York University, New York, NY
Deep learning has caused revolutions in computer understanding of images, audio, and text,
enabling new applications such as information search and filtering, autonomous driving,
radiology…
Deep learning has caused revolutions in computer understanding of images, audio, and text,
enabling new applications such as information search and filtering, autonomous driving,
radiology…
COURSE
Probabilistic Graphical Models
Spring 2019 • Carnegie Mellon University
Lisa Lee
https://sailinglab.github.io/pgm-spring-2019/lectures/
Probabilistic Graphical Models
Spring 2019 • Carnegie Mellon University
Lisa Lee
https://sailinglab.github.io/pgm-spring-2019/lectures/
sailinglab.github.io
10-708 PGM | Schedule
10-708 - Probabilistic Graphical Models - Carnegie Mellon University - Spring 2019
Deep Learning lecture
The full deck of (600+) slides, by Gilles Louppe: https://glouppe.github.io/info8010-deep-learning/pdf/lec-all.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning
The full deck of (600+) slides, by Gilles Louppe: https://glouppe.github.io/info8010-deep-learning/pdf/lec-all.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning
Full Stack Deep Learning Bootcamp
(Most of) Lectures of Day 1: https://fullstackdeeplearning.com/march2019
Happy learning!
#ArtificialIntelligence #DeepLearning #MachineLearning
(Most of) Lectures of Day 1: https://fullstackdeeplearning.com/march2019
Happy learning!
#ArtificialIntelligence #DeepLearning #MachineLearning
Fullstackdeeplearning
Full Stack Deep Learning
Hands-on program for software developers familiar with the basics of deep learning seeking to expand their skills.
A short video explaining our recent ICLR'19 paper: "Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic" by
Mikael Henaff, Alfredo Canziani, & Yann LeCun
https://openreview.net/forum?id=HygQBn0cYm
https://youtu.be/X2s7gy3wIYw
Mikael Henaff, Alfredo Canziani, & Yann LeCun
https://openreview.net/forum?id=HygQBn0cYm
https://youtu.be/X2s7gy3wIYw
OpenReview
Model-Predictive Policy Learning with Uncertainty Regularization...
A model-based RL approach which uses a differentiable uncertainty penalty to learn driving policies from purely observational data.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin, Duke University
https://www.nature.com/articles/s42256-019-0048-x
ArXiv Version:
https://arxiv.org/pdf/1811.10154
#artificialintelligence #explainableai
#blackbox
Cynthia Rudin, Duke University
https://www.nature.com/articles/s42256-019-0048-x
ArXiv Version:
https://arxiv.org/pdf/1811.10154
#artificialintelligence #explainableai
#blackbox
Nature
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Nature Machine Intelligence - There has been a recent rise of interest in developing methods for ‘explainable AI’, where models are created to explain how a first ‘black...
2018 ACM Tur8ng Award
https://m.youtube.com/watch?v=Fn589zeMij4&feature=youtu.be
And
https://vimeo.com/333389735
https://m.youtube.com/watch?v=Fn589zeMij4&feature=youtu.be
And
https://vimeo.com/333389735
YouTube
CACM Jun 2019 - 2018 ACM Turing Award
Once treated by the field with skepticism (if not outright derision), the artificial neural networks that 2018 ACM A.M. Turing Award recipients Geoffrey Hint...
FastSpeech: Fast, Robust and Controllable Text to Speech
Ren et al.: https://arxiv.org/abs/1905.09263
#ArtificialIntelligence #DeepLearning #MachineLearning
Ren et al.: https://arxiv.org/abs/1905.09263
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
FastSpeech: Fast, Robust and Controllable Text to Speech
Neural network based end-to-end text to speech (TTS) has significantly improved the quality of synthesized speech. Prominent methods (e.g., Tacotron 2) usually first generate mel-spectrogram from...
A Transformer Chatbot Tutorial with TensorFlow 2.0
Article by Bryan M. Li: https://medium.com/tensorflow/a-transformer-chatbot-tutorial-with-tensorflow-2-0-88bf59e66fe2
#ArtificialIntelligence #MachineLearning #TensorFlow #Chatbot
Article by Bryan M. Li: https://medium.com/tensorflow/a-transformer-chatbot-tutorial-with-tensorflow-2-0-88bf59e66fe2
#ArtificialIntelligence #MachineLearning #TensorFlow #Chatbot
Medium
A Transformer Chatbot Tutorial with TensorFlow 2.0
A guest article by Bryan M. Li, FOR.ai
Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
Hoang NT and Takanori Maehara: https://arxiv.org/abs/1905.09550
#MachineLearning #NeuralNetworks #InformationTheory #SpectralTheory
Hoang NT and Takanori Maehara: https://arxiv.org/abs/1905.09550
#MachineLearning #NeuralNetworks #InformationTheory #SpectralTheory
arXiv.org
Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
Graph neural networks have become one of the most important techniques to solve machine learning problems on graph-structured data. Recent work on vertex classification proposed deep and...
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Zakharov et al.: https://arxiv.org/abs/1905.08233
Video: https://youtu.be/p1b5aiTrGzY
Animating heads using only few shots of target person (or even 1 shot). Keypoints, adaptive instance norms and GANs.
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks
Zakharov et al.: https://arxiv.org/abs/1905.08233
Video: https://youtu.be/p1b5aiTrGzY
Animating heads using only few shots of target person (or even 1 shot). Keypoints, adaptive instance norms and GANs.
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks
arXiv.org
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Several recent works have shown how highly realistic human head images can be obtained by training convolutional neural networks to generate them. In order to create a personalized talking head...
Speech2Face: Learning the Face Behind a Voice
https://arxiv.org/abs/1905.09773
#ArtificialIntelligence #DeepLearning #Multimedia
https://arxiv.org/abs/1905.09773
#ArtificialIntelligence #DeepLearning #Multimedia
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Zakharov et al.: https://arxiv.org/abs/1905.08233
@ArtificialIntelligenceArticles
#ComputerVision #GenerativeAdversarialNetworks #MachineLearning
Zakharov et al.: https://arxiv.org/abs/1905.08233
@ArtificialIntelligenceArticles
#ComputerVision #GenerativeAdversarialNetworks #MachineLearning
Text Classification Algorithms: A Survey
https://medium.com/text-classification-algorithms/text-classification-algorithms-a-survey-a215b7ab7e2d
https://medium.com/text-classification-algorithms/text-classification-algorithms-a-survey-a215b7ab7e2d
Medium
Text Classification Algorithms: A Survey
Text feature extraction and pre-processing for classification algorithms are very significant. In this section, we start to talk about text cleaning since most of the documents contain a lot of…