Neural Networks | Нейронные сети
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​Нейросети и глубокое обучение: онлайн-учебник, глава 1

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Michael NielsenПеред вами – перевод свободной онлайн-книги Майкла Нильсена «Neural Networks and Deep Learning», распространяемой под лицензией Creative Commons Attribution-NonCommercial 3.0 Unported License. Мотивацией к его созданию послужил успешный опыт перевода учебника по программированию, "Выразительный JavaScript". Книга по нейросетям тоже достаточно популярна, на неё активно ссылаются авторы англоязычных статей. Её переводов я не нашёл, за исключением перевода начала первой главы с сокращениями.

Желающие отблагодарить автора книги могут сделать это на её официальной странице, переводом через PayPal или биткоин. Для поддержки переводчика на Хабре есть форма «поддержать автора».
https://habr.com/ru/post/456738/

🔗 Нейросети и глубокое обучение: онлайн-учебник, глава 1
Примечание Перед вами – перевод свободной онлайн-книги Майкла Нильсена «Neural Networks and Deep Learning», распространяемой под лицензией Creative Commons Attri...
🎥 All Hail The Mighty Translatotron!
👁 2 раз 358 сек.
❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers

My talk and the full panel discussion at the NATO conference (I start at around the 12:30 minute mark):
▶️ https://www.facebook.com/StratComCOE/videos/698737203889068/

📝 The paper "Direct speech-to-speech translation with a sequence-to-sequence model" and the voice samples are available here:
https://arxiv.org/abs/1904.06037
https://google-research.github.io/lingvo-lab/translatotron/#conversational

🙏 We would like to thank
Artificial Intelligence In Healthcare | Examples Of AI In Healthcare

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://www.youtube.com/watch?v=j6EB9HO6acE

🎥 Artificial Intelligence In Healthcare | Examples Of AI In Healthcare | Edureka
👁 1 раз 1752 сек.
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
Artificial Intelligence in Healthcare is revolutionizing the medical industry by providing a helping hand. This Edureka session will help you understand the positive impact of Artificial Intelligence in the healthcare domain along with practical implementation in Python. The following topics are covered in this session:

1. What Is Artificial Intelligence?
2. AI in healthcare
3.
🎥 Berlin Buzzwords 2019: Lester Solbakken–Scaling ONNX and TensorFlow model evaluation in search
👁 1 раз 1317 сек.
With the advances in deep learning and the corresponding increase in machine learning frameworks in recent years, a new class of software has emerged: model servers. These promise, among other things, performance and scalability. There is however a large class of applications where such model servers are inadequate. For instance, search and recommendation applications must efficiently evaluate models over potentially many thousands of data points as part of handling a query. In such cases the amount of data
🎥 AWS Educate – Innovation and Education Lightning Talks
👁 1 раз 3515 сек.
Four 8 minute powerful talks from leading higher education educators on topics related to AWS Educate, Education, Innovation, and Cyber, Voice AI, ML, or Deep Learning. After the talks, a moderator will have a 15 minute panel discussion related to the topics discussed.
​MRI Tissue Magnetism Quantification through Total Field Inversion with Deep Neural Networks
https://arxiv.org/abs/1904.07105

🔗 MRI Tissue Magnetism Quantification through Total Field Inversion with Deep Neural Networks
Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to infer estimates of local tissue magnetism (magnetic susceptibility), which has been shown useful to provide novel image contrast and as biomarkers of abnormal tissue. QSM requires addressing a challenging post-processing problem: filtering of image phase estimates and inversion of the phase to susceptibility relationship. A wide variety of quantification errors, robustness limitations, and artifacts plague QSM algorithms. To overcome these limitations, a robust deep-learning-based single-step QSM reconstruction approach is proposed and demonstrated. This neural network was trained using magnetostatic physics simulations based on in-vivo data sources. Random perturbations were added to the physics simulations to provide sufficient input-label pairs for the training purposes. The network was quantitatively tested using gold-standard in-silico labeled datasets against established QSM total field inversion approaches. In addition, the algorith
​Modern Deep Learning Techniques Applied to Natural Language Processing by Authors

🔗 Modern Deep Learning Techniques Applied to Natural Language Processing by Authors
🎥 Intel: Leading the deep learning accelerator solutions
👁 1 раз 1926 сек.
In this talk, we will discuss how Intel is leading the industry in developing AI accelerators and how new AI hardware like our Intel Nervana Neural Network Processors, paired with open-source software, will allow developers to harness the power of AI for any application.

Subscribe for more videos like this: http://hpe.to/6007Beguh

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​Open-sourcing PyRobot to accelerate AI robotics research

🔗 Open-sourcing PyRobot to accelerate AI robotics research
Facebook AI is open-sourcing PyRobot, a lightweight, high-level interface that lets AI researchers get up and running with robotics experiments in just hours, with no specialized robotics expertise.
​How to Implement GAN Hacks to Train Stable Generative Adversarial Networks
https://machinelearningmastery.com/how-to-code-generative-adversarial-network-hacks/

🔗 How to Implement GAN Hacks to Train Stable Generative Adversarial Networks
Generative Adversarial Networks, or GANs, are challenging to train. This is because the architecture involves both a generator and a discriminator model that compete in a zero-sum game. It means that improvements to one model come at the cost of a degrading of performance in the other model. The result is a very unstable training …
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