With 180+ papers mentioning
Transformers and its predecessors, it was high time to put out a real paper that people could cite.
https://arxiv.org/abs/1910.03771
🔗 Transformers: State-of-the-art Natural Language Processing
Recent advances in modern Natural Language Processing (NLP) research have been dominated by the combination of Transfer Learning methods with large-scale Transformer language models. With them came a paradigm shift in NLP with the starting point for training a model on a downstream task moving from a blank specific model to a general-purpose pretrained architecture. Still, creating these general-purpose models remains an expensive and time-consuming process restricting the use of these methods to a small sub-set of the wider NLP community. In this paper, we present Transformers, a library for state-of-the-art NLP, making these developments available to the community by gathering state-of-the-art general-purpose pretrained models under a unified API together with an ecosystem of libraries, examples, tutorials and scripts targeting many downstream NLP tasks. Transformers features carefully crafted model implementations and high-performance pretrained weights for two main deep learning frameworks, PyTorch and Te
Transformers and its predecessors, it was high time to put out a real paper that people could cite.
https://arxiv.org/abs/1910.03771
🔗 Transformers: State-of-the-art Natural Language Processing
Recent advances in modern Natural Language Processing (NLP) research have been dominated by the combination of Transfer Learning methods with large-scale Transformer language models. With them came a paradigm shift in NLP with the starting point for training a model on a downstream task moving from a blank specific model to a general-purpose pretrained architecture. Still, creating these general-purpose models remains an expensive and time-consuming process restricting the use of these methods to a small sub-set of the wider NLP community. In this paper, we present Transformers, a library for state-of-the-art NLP, making these developments available to the community by gathering state-of-the-art general-purpose pretrained models under a unified API together with an ecosystem of libraries, examples, tutorials and scripts targeting many downstream NLP tasks. Transformers features carefully crafted model implementations and high-performance pretrained weights for two main deep learning frameworks, PyTorch and Te
arXiv.org
HuggingFace's Transformers: State-of-the-art Natural Language...
Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity...
🎥 Machine Learning Overview | MLAIT | DSC-LPU
👁 1 раз ⏳ 995 сек.
👁 1 раз ⏳ 995 сек.
#MachineLearning #DSC #MLAIT
Resources: https://github.com/patidarparas13/Machine-Learning-Tutorials
Twitter: @patidarparas13,@mlait1908,@dsclpu
LinkedIn : https://linkedin.com/in/patidarparas13
Thank You!Vk
Machine Learning Overview | MLAIT | DSC-LPU
#MachineLearning #DSC #MLAIT
Resources: https://github.com/patidarparas13/Machine-Learning-Tutorials
Twitter: @patidarparas13,@mlait1908,@dsclpu
LinkedIn : https://linkedin.com/in/patidarparas13
Thank You!
Resources: https://github.com/patidarparas13/Machine-Learning-Tutorials
Twitter: @patidarparas13,@mlait1908,@dsclpu
LinkedIn : https://linkedin.com/in/patidarparas13
Thank You!
Getting Started With Machine Learning, Part 3: Writing Your First Machine Learning Program
🔗 Getting Started With Machine Learning, Part 3: Writing Your First Machine Learning Program
A simple example using sci-kit learn
🔗 Getting Started With Machine Learning, Part 3: Writing Your First Machine Learning Program
A simple example using sci-kit learn
Medium
Getting Started With Machine Learning, Part 3: Writing Your First Machine Learning Program
A simple example using sci-kit learn
How Many Samples Do I Need in My Test?
🔗 How Many Samples Do I Need in My Test?
What you need to know to calculate a minimum test size for a binomial test — and how to calculate it
🔗 How Many Samples Do I Need in My Test?
What you need to know to calculate a minimum test size for a binomial test — and how to calculate it
Medium
How Many Samples Do I Need in My Test?
What you need to know to calculate a minimum test size for a binomial test — and how to calculate it
Feature Extraction Techniques
🔗 Feature Extraction Techniques
An end to end guide on how to reduce a dataset dimensionality using Feature Extraction Techniques such as: PCA, ICA, LDA, LLE, t-SNE and…
🔗 Feature Extraction Techniques
An end to end guide on how to reduce a dataset dimensionality using Feature Extraction Techniques such as: PCA, ICA, LDA, LLE, t-SNE and…
Medium
Feature Extraction Techniques
An end to end guide on how to reduce a dataset dimensionality using Feature Extraction Techniques such as: PCA, ICA, LDA, LLE, t-SNE and…
📹Artificial caricature
Agents learn to draw simplified (artistic?) portraits via trial and error.
Project website: https://learning-to-paint.github.io
ArXiV: https://arxiv.org/abs/1910.01007
🔗 Unsupervised Doodling and Painting with Improved SPIRAL
Agents learn to draw simplified (artistic?) portraits via trial and error.
Project website: https://learning-to-paint.github.io
ArXiV: https://arxiv.org/abs/1910.01007
🔗 Unsupervised Doodling and Painting with Improved SPIRAL
arXiv.org
Unsupervised Doodling and Painting with Improved SPIRAL
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is trained with...
Creating a Weapon Detector in 5 simple steps
🔗 Creating a Weapon Detector in 5 simple steps
Object detection using mask-RCNN on custom dataset
🔗 Creating a Weapon Detector in 5 simple steps
Object detection using mask-RCNN on custom dataset
Medium
Creating a Weapon Detector in 5 simple steps
Object detection using mask-RCNN on custom dataset
🎥 Deep Learning Weekly (VIII) - Two Stage Object Detection - Алмаз Зиноллаев
👁 1 раз ⏳ 2626 сек.
👁 1 раз ⏳ 2626 сек.
Алмаз Зиноллаев последовательно рассказал, как итеративно развивался и усложнялся пайплайн, посвященный задаче детекции объектов на изображении
Presentation: https://yadi.sk/i/97o5GkLOSK0lUgVk
Deep Learning Weekly (VIII) - Two Stage Object Detection - Алмаз Зиноллаев
Алмаз Зиноллаев последовательно рассказал, как итеративно развивался и усложнялся пайплайн, посвященный задаче детекции объектов на изображении
Presentation: https://yadi.sk/i/97o5GkLOSK0lUg
Presentation: https://yadi.sk/i/97o5GkLOSK0lUg
Kaggle Live Coding: Automating report generation | Kaggle
🔗 Kaggle Live Coding: Automating report generation | Kaggle
This week Rachael will continue to work on her forum clustering project (https://www.kaggle.com/rebeccaturner/forum-post-embeddings-clustering-1-0). Now that we've got our clusters, we need to generate better reports for them! SUBSCRIBE: https://www.youtube.com/c/kaggle?sub_... About Kaggle: Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and do your data science work. Kaggle's platform is the fastest way to get started on a new data science project. Spi
🔗 Kaggle Live Coding: Automating report generation | Kaggle
This week Rachael will continue to work on her forum clustering project (https://www.kaggle.com/rebeccaturner/forum-post-embeddings-clustering-1-0). Now that we've got our clusters, we need to generate better reports for them! SUBSCRIBE: https://www.youtube.com/c/kaggle?sub_... About Kaggle: Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and do your data science work. Kaggle's platform is the fastest way to get started on a new data science project. Spi
YouTube
Kaggle Live Coding: Automating report generation | Kaggle
This week Rachael will continue to work on her forum clustering project (https://www.kaggle.com/rebeccaturner/forum-post-embeddings-clustering-1-0). Now that...
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
🎥 On the GPU - Deep Learning and Neural Networks with Python and Pytorch p.7
👁 2 раз ⏳ 1923 сек.
🎥 On the GPU - Deep Learning and Neural Networks with Python and Pytorch p.7
👁 2 раз ⏳ 1923 сек.
Text-based tutorials and sample code: https://pythonprogramming.net/gpu-deep-learning-neural-network-pytorch/
How to use cloud GPUs: https://pythonprogramming.net/cloud-gpu-compare-and-setup-linode-rtx-6000/
Linode Cloud GPUs $20 credit: https://linode.com/sentdex
Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join
Discord: https://discord.gg/sentdex
Support the content: https://pythonprogramming.net/support-donate/
Twitter: https://twitter.com/sentdex
Instagram: https://insExploring Massively Multilingual, Massive Neural Machine Translation
http://ai.googleblog.com/2019/10/exploring-massively-multilingual.html
article: https://arxiv.org/pdf/1907.05019.pdf
🔗 Exploring Massively Multilingual, Massive Neural Machine Translation
Posted by Ankur Bapna, Software Engineer and Orhan Firat, Research Scientist, Google Research “... perhaps the way [of translation] is t...
http://ai.googleblog.com/2019/10/exploring-massively-multilingual.html
article: https://arxiv.org/pdf/1907.05019.pdf
🔗 Exploring Massively Multilingual, Massive Neural Machine Translation
Posted by Ankur Bapna, Software Engineer and Orhan Firat, Research Scientist, Google Research “... perhaps the way [of translation] is t...
research.google
Exploring Massively Multilingual, Massive Neural Machine Translation
Posted by Ankur Bapna, Software Engineer and Orhan Firat, Research Scientist, Google Research “... perhaps the way [of translation] is to descend...
Top 5 Metrics for Evaluating Your Deep Learning Program's GPU Performance - Exxact
https://blog.exxactcorp.com/top-5-metrics-for-evaluating-your-deep-learning-programs-gpu-performance/
🔗 Top 5 Metrics for Evaluating Your Deep Learning Program's GPU Performance - Exxact
In this blog article, we discuss the top 5 metrics for evaluating your deep learning program's GPU performance. You don't want to miss this one!
https://blog.exxactcorp.com/top-5-metrics-for-evaluating-your-deep-learning-programs-gpu-performance/
🔗 Top 5 Metrics for Evaluating Your Deep Learning Program's GPU Performance - Exxact
In this blog article, we discuss the top 5 metrics for evaluating your deep learning program's GPU performance. You don't want to miss this one!
Exxact
Top 5 Metrics for Evaluating Your Deep Learning Program's GPU Performance - Exxact
In this blog article, we discuss the top 5 metrics for evaluating your deep learning program's GPU performance. You don't want to miss this one!
Detection and Classification of Blood Cells with Deep Learning (Part 2 — Training and Evaluation)
🔗 Detection and Classification of Blood Cells with Deep Learning (Part 2 — Training and Evaluation)
Tackling the BCCD Dataset with Tensorflow Object Detection API
🔗 Detection and Classification of Blood Cells with Deep Learning (Part 2 — Training and Evaluation)
Tackling the BCCD Dataset with Tensorflow Object Detection API
Medium
Detection and Classification of Blood Cells with Deep Learning (Part 2 — Training and Evaluation)
Tackling the BCCD Dataset with Tensorflow Object Detection API
Line Detection: Make an Autonomous Car see Road Lines
🔗 Line Detection: Make an Autonomous Car see Road Lines
Step by step you you can turn a video stream into a line detector via Computer Vision
🔗 Line Detection: Make an Autonomous Car see Road Lines
Step by step you you can turn a video stream into a line detector via Computer Vision
Medium
Line Detection: Make an Autonomous Car see Road Lines
Step by step you you can turn a video stream into a line detector via Computer Vision
🎥 [VDT19] Applied machine learning: a few lessons I learned the hard way by Alessandro Giusti
👁 1 раз ⏳ 2681 сек.
👁 1 раз ⏳ 2681 сек.
Machine Learning is easy; solving problems with machine learning is hard. In the last 10 years, I approached dozens of real-world problems with machine learning and deep learning, with varying degrees of success. In the process I made many mistakes and eventually learned how to avoid them. I noticed that many of these mistakes are pretty common among novice practitioners, so this talk might save you some time.Vk
[VDT19] Applied machine learning: a few lessons I learned the hard way by Alessandro Giusti
Machine Learning is easy; solving problems with machine learning is hard. In the last 10 years, I approached dozens of real-world problems with machine learning and deep learning, with varying degrees of success. In the process I made many mistakes and eventually…
Free Book: Deep Learning and Computer Vision with CNNs
https://www.datasciencecentral.com/profiles/blogs/free-book-deep-learning-and-computer-vision-with-cnns
🔗 Free Book: Deep Learning and Computer Vision with CNNs
By Dan Howarth and Ajit Jaokar, October 2019. 42 pages. CNN stands for Convolutional Neural Networks. Part 1 will introduce the core concepts of Deep Learning.…
https://www.datasciencecentral.com/profiles/blogs/free-book-deep-learning-and-computer-vision-with-cnns
🔗 Free Book: Deep Learning and Computer Vision with CNNs
By Dan Howarth and Ajit Jaokar, October 2019. 42 pages. CNN stands for Convolutional Neural Networks. Part 1 will introduce the core concepts of Deep Learning.…
Data Science Central
Free Book: Deep Learning and Computer Vision with CNNs
By Dan Howarth and Ajit Jaokar, October 2019. 58 pages. CNN stands for Convolutional Neural Networks. Part 1 will introduce the core concepts of Deep Learning. We will also start coding straightaway with Tensorflow 2.0. In part 2, we use another dataset –…
PyTorch 1.3 — What’s new?
🔗 PyTorch 1.3 — What’s new?
Support for Android and iOS, Named Tensor, TPU Support, Quantization and more.
🔗 PyTorch 1.3 — What’s new?
Support for Android and iOS, Named Tensor, TPU Support, Quantization and more.
Medium
PyTorch 1.3 — What’s new?
Support for Android and iOS, Named Tensor, TPU Support, Quantization and more.
AttoNets, A New AI That is Faster & Efficient For Edge Computing
https://www.marktechpost.com/2019/10/11/attonets-a-new-ai-that-is-faster-efficient-for-edge-computing/
🔗 AttoNets, A New AI That is Faster & Efficient For Edge Computing | MarkTechPost
AttoNets, A New AI That is Faster & Efficient For Edge Computing. An AI team at the University of Waterloo, Canada, developed a new type of compact family of deep neural networks (AttoNets),
https://www.marktechpost.com/2019/10/11/attonets-a-new-ai-that-is-faster-efficient-for-edge-computing/
🔗 AttoNets, A New AI That is Faster & Efficient For Edge Computing | MarkTechPost
AttoNets, A New AI That is Faster & Efficient For Edge Computing. An AI team at the University of Waterloo, Canada, developed a new type of compact family of deep neural networks (AttoNets),
MarkTechPost
AttoNets, A New AI That is Faster & Efficient For Edge Computing
An AI team at the University of Waterloo, Canada, developed a new type of compact family of deep neural networks (AttoNets), which can even run on smartphones, tablets, and other mobile devices. The main problem with available neural networks is they require…
Грокаем PyTorch
Привет, Хабр!
У нас в продаже появилась долгожданная книга о библиотеке PyTorch.
Поскольку весь необходимый базовый материал о PyTorch вы узнаете из этой книги, мы напоминаем о пользе процесса под названием «grokking» или «углубленное постижение» той темы, которую вы хотите усвоить. В сегодняшней публикации мы расскажем, как Кай Арулкумаран (Kai Arulkumaran) грокнул PyTorch (без картинок). Добро пожаловать под кат.
🔗 Грокаем PyTorch
Привет, Хабр! У нас в продаже появилась долгожданная книга о библиотеке PyTorch. Поскольку весь необходимый базовый материал о PyTorch вы узнаете из этой кни...
Привет, Хабр!
У нас в продаже появилась долгожданная книга о библиотеке PyTorch.
Поскольку весь необходимый базовый материал о PyTorch вы узнаете из этой книги, мы напоминаем о пользе процесса под названием «grokking» или «углубленное постижение» той темы, которую вы хотите усвоить. В сегодняшней публикации мы расскажем, как Кай Арулкумаран (Kai Arulkumaran) грокнул PyTorch (без картинок). Добро пожаловать под кат.
🔗 Грокаем PyTorch
Привет, Хабр! У нас в продаже появилась долгожданная книга о библиотеке PyTorch. Поскольку весь необходимый базовый материал о PyTorch вы узнаете из этой кни...
Хабр
Грокаем PyTorch
Привет, Хабр! У нас в предзаказе появилась долгожданная книга о библиотеке PyTorch . Поскольку весь необходимый базовый материал о PyTorch вы узнаете из этой книги, мы напоминаем о пользе процесса под...