Plot of Randomly Generated Faces Using the Loaded GAN Model
How to Explore the GAN Latent Space When Generating Faces
https://machinelearningmastery.com/how-to-interpolate-and-perform-vector-arithmetic-with-faces-using-a-generative-adversarial-network/
How to Explore the GAN Latent Space When Generating Faces
https://machinelearningmastery.com/how-to-interpolate-and-perform-vector-arithmetic-with-faces-using-a-generative-adversarial-network/
Facebook is open-sourcing DLRM — a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware community develop new, more efficient ways to work with categorical data.
fb: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
link: https://arxiv.org/abs/1906.03109
fb: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
link: https://arxiv.org/abs/1906.03109
Meta
We are open-sourcing a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware…
Everything you need to know about TensorFlow 2.0
Keras-APIs, SavedModels, TensorBoard, Keras-Tuner and more.
https://hackernoon.com/everything-you-need-to-know-about-tensorflow-2-0-b0856960c074
Keras-APIs, SavedModels, TensorBoard, Keras-Tuner and more.
https://hackernoon.com/everything-you-need-to-know-about-tensorflow-2-0-b0856960c074
Hackernoon
Everything you need to know about TensorFlow 2.0 | HackerNoon
On June 26 of 2019, I will be giving a TensorFlow (TF) 2.0 workshop at the <a href="https://www.papis.io/latam-2019">PAPIs.io LATAM conference in São Paulo</a>. Aside from the happiness of being representing <a href="https://www.daitan.com/">Daitan</a> as…
PyTorchPipe
PyTorchPipe (PTP) is a component-oriented framework that facilitates development of computational multi-modal pipelines and comparison of diverse neural network-based models.
https://github.com/IBM/pytorchpipe
PyTorchPipe (PTP) is a component-oriented framework that facilitates development of computational multi-modal pipelines and comparison of diverse neural network-based models.
https://github.com/IBM/pytorchpipe
GitHub
GitHub - IBM/pytorchpipe: PyTorchPipe (PTP) is a component-oriented framework for rapid prototyping and training of computational…
PyTorchPipe (PTP) is a component-oriented framework for rapid prototyping and training of computational pipelines combining vision and language - GitHub - IBM/pytorchpipe: PyTorchPipe (PTP) is a co...
How to Develop a Conditional GAN (cGAN) From Scratch
Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images.
Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images.
Check the data science channel there you will find a lot of articles, links and advanced researches .
Join and learn hot topics of data science @opendatascience
Join and learn hot topics of data science @opendatascience
Literature of Deep Learning for Graphs
This is a paper list about deep learning for graphs.
https://github.com/DeepGraphLearning/LiteratureDL4Graph
This is a paper list about deep learning for graphs.
https://github.com/DeepGraphLearning/LiteratureDL4Graph
GitHub
GitHub - DeepGraphLearning/LiteratureDL4Graph: A comprehensive collection of recent papers on graph deep learning
A comprehensive collection of recent papers on graph deep learning - DeepGraphLearning/LiteratureDL4Graph
Forwarded from Artificial Intelligence
Rank-consistent Ordinal Regression for Neural Networks
Article: https://arxiv.org/abs/1901.07884
PyTorch: https://github.com/Raschka-research-group/coral-cnn
Article: https://arxiv.org/abs/1901.07884
PyTorch: https://github.com/Raschka-research-group/coral-cnn
arXiv.org
Rank consistent ordinal regression for neural networks with...
In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category...
How to Identify and Diagnose GAN Failure Modes
https://machinelearningmastery.com/practical-guide-to-gan-failure-modes/
https://machinelearningmastery.com/practical-guide-to-gan-failure-modes/
🤬1
Predicting the Generalization Gap in Deep Neural Networks
http://ai.googleblog.com/2019/07/predicting-generalization-gap-in-deep.html
http://ai.googleblog.com/2019/07/predicting-generalization-gap-in-deep.html
Googleblog
Predicting the Generalization Gap in Deep Neural Networks
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data
Article: https://arxiv.org/abs/1907.04240
PDF: https://arxiv.org/pdf/1907.04240.pdf
Article: https://arxiv.org/abs/1907.04240
PDF: https://arxiv.org/pdf/1907.04240.pdf
arXiv.org
Bayesian deep learning with hierarchical prior: Predictions from...
Datasets in engineering applications are often limited and contaminated,
mainly due to unavoidable measurement noise and signal distortion. Thus, using
conventional data-driven approaches to build...
mainly due to unavoidable measurement noise and signal distortion. Thus, using
conventional data-driven approaches to build...
A Tour of Generative Adversarial Network Models
https://machinelearningmastery.com/tour-of-generative-adversarial-network-models/
https://machinelearningmastery.com/tour-of-generative-adversarial-network-models/
MachineLearningMastery.com
A Tour of Generative Adversarial Network Models - MachineLearningMastery.com
Generative Adversarial Networks, or GANs, are deep learning architecture generative models that have seen wide success.
There are thousands of papers on GANs and many hundreds of named-GANs, that is, models with a defined name that often includes
There are thousands of papers on GANs and many hundreds of named-GANs, that is, models with a defined name that often includes
Advancing Semi-supervised Learning with Unsupervised Data Augmentation
http://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html
http://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html
research.google
Advancing Semi-supervised Learning with Unsupervised Data Augmentation
Posted by Qizhe Xie, Student Researcher and Thang Luong, Senior Research Scientist, Google Research, Brain Team Success in deep learning has larg...
TRFL a library of reinforcement learning building blocks By the Research Engineering team at DeepMind:
https://github.com/deepmind/trfl
https://github.com/deepmind/trfl
GitHub
GitHub - google-deepmind/trfl: TensorFlow Reinforcement Learning
TensorFlow Reinforcement Learning. Contribute to google-deepmind/trfl development by creating an account on GitHub.
Multilingual Universal Sentence Encoder for Semantic Retrieval
http://ai.googleblog.com/2019/07/multilingual-universal-sentence-encoder.html
http://ai.googleblog.com/2019/07/multilingual-universal-sentence-encoder.html
research.google
Multilingual Universal Sentence Encoder for Semantic Retrieval
Posted by Yinfei Yang and Amin Ahmad, Software Engineers, Google Research Since it was introduced last year, “Universal Sentence Encoder (USE) for...
How to Code the GAN Training Algorithm and Loss Functions
https://machinelearningmastery.com/how-to-code-the-generative-adversarial-network-training-algorithm-and-loss-functions/
https://machinelearningmastery.com/how-to-code-the-generative-adversarial-network-training-algorithm-and-loss-functions/
An implementation of the BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer (Interspeech 2019)
Article: https://arxiv.org/pdf/1907.03040.pdf
Github: https://github.com/guanlinchao/bert-dst
Article: https://arxiv.org/pdf/1907.03040.pdf
Github: https://github.com/guanlinchao/bert-dst
GitHub
GitHub - guanlinchao/bert-dst: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations…
BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer - guanlinchao/bert-dst
Learning to learn with quantum neural networks via classical neural networks
https://arxiv.org/abs/1907.05415
https://arxiv.org/abs/1907.05415
arXiv.org
Learning to learn with quantum neural networks via classical...
Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges. One such...
Simple Deep Q Network w/Pytorch: https://youtu.be/UlJzzLYgYoE
Reinforcement Learning Crash Course: https://youtu.be/sOiNMW8k4T0
Policy Gradients w/Tensorflow: https://youtu.be/UT9pQjVhcaU
Deep Q Learning w/Tensorflow https://youtu.be/3Ggq_zoRGP4
Code Your Own RL Environments https://youtu.be/vmrqpHldAQ0
How to Spec a Deep Learning PC: https://youtu.be/xsnVlMWQj8o
Deep Q Learning w/ Pytorch: https://youtu.be/RfNxXlO6BiA
Machine Learning Freelancing https://youtu.be/6M04ZTLE_O4
Code from video: https://github.com/philtabor/Youtube-Code-Repository
Reinforcement Learning Crash Course: https://youtu.be/sOiNMW8k4T0
Policy Gradients w/Tensorflow: https://youtu.be/UT9pQjVhcaU
Deep Q Learning w/Tensorflow https://youtu.be/3Ggq_zoRGP4
Code Your Own RL Environments https://youtu.be/vmrqpHldAQ0
How to Spec a Deep Learning PC: https://youtu.be/xsnVlMWQj8o
Deep Q Learning w/ Pytorch: https://youtu.be/RfNxXlO6BiA
Machine Learning Freelancing https://youtu.be/6M04ZTLE_O4
Code from video: https://github.com/philtabor/Youtube-Code-Repository
How to Implement Wasserstein Loss for Generative Adversarial Networks
https://machinelearningmastery.com/how-to-implement-wasserstein-loss-for-generative-adversarial-networks/
https://machinelearningmastery.com/how-to-implement-wasserstein-loss-for-generative-adversarial-networks/