🎥 Deep Learning Full Course - 7 Hours | Deep Learning Tutorial | Edureka
👁 3 раз ⏳ 21746 сек.
👁 3 раз ⏳ 21746 сек.
** AI & Deep Learning with TensorFlow: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This Edureka Deep Learning Full Course video will help you understand and learn Deep Learning & Tensorflow in detail. This Deep Learning Tutorial is ideal for both beginners as well as professionals who want to master Deep Learning Algorithms. Below are the topics covered in this Deep Learning tutorial video:
3:11 What is Deep Learning
3:55 Why Artificial Intelligence?
5:48 What is AI?
6:53 Applications of AI
8Vk
Deep Learning Full Course - 7 Hours | Deep Learning Tutorial | Edureka
** AI & Deep Learning with TensorFlow: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This Edureka Deep Learning Full Course video will help you understand and learn Deep Learning & Tensorflow in detail. This Deep Learning Tutorial is ideal for…
This Edureka Deep Learning Full Course video will help you understand and learn Deep Learning & Tensorflow in detail. This Deep Learning Tutorial is ideal for…
Vijay Kumar: Flying Robots | Artificial Intelligence (AI) Podcast
🔗 Vijay Kumar: Flying Robots | Artificial Intelligence (AI) Podcast
Vijay Kumar is one of the top roboticists in the world, professor at the University of Pennsylvania, Dean of Penn Engineering, former director of GRASP lab, or the General Robotics, Automation, Sensing and Perception Laboratory at Penn that was established back in 1979, 40 years ago. Vijay is perhaps best known for his work in multi-robot systems (or robot swarms) and micro aerial vehicles, robots that elegantly cooperate in flight under all the uncertainty and challenges that real-world conditions present.
🔗 Vijay Kumar: Flying Robots | Artificial Intelligence (AI) Podcast
Vijay Kumar is one of the top roboticists in the world, professor at the University of Pennsylvania, Dean of Penn Engineering, former director of GRASP lab, or the General Robotics, Automation, Sensing and Perception Laboratory at Penn that was established back in 1979, 40 years ago. Vijay is perhaps best known for his work in multi-robot systems (or robot swarms) and micro aerial vehicles, robots that elegantly cooperate in flight under all the uncertainty and challenges that real-world conditions present.
YouTube
Vijay Kumar: Flying Robots | Lex Fridman Podcast #37
Practical guide to Attention mechanism for NLU tasks
🔗 Practical guide to Attention mechanism for NLU tasks
Tested hands-on strategies to tackle attention for improving sequence to sequence models
🔗 Practical guide to Attention mechanism for NLU tasks
Tested hands-on strategies to tackle attention for improving sequence to sequence models
Medium
Practical guide to Attention mechanism for NLU tasks
Tested hands-on strategies to tackle attention for improving sequence to sequence models
https://towardsdatascience.com/from-econometrics-to-machine-learning-ee182f3a45d7?source=collection_home---4------5----------------------
🔗 From Econometrics to Machine Learning
Why econometrics should be part of your skills
🔗 From Econometrics to Machine Learning
Why econometrics should be part of your skills
Medium
From Econometrics to Machine Learning
Why econometrics should be part of your skills
Conditional Love: The Rise of Renormalization Techniques for Conditioning Neural Networks
🔗 Conditional Love: The Rise of Renormalization Techniques for Conditioning Neural Networks
Conditional renormalization is an oft-unsung technique powering many recent ML successes; how does it work and where did the idea come…
🔗 Conditional Love: The Rise of Renormalization Techniques for Conditioning Neural Networks
Conditional renormalization is an oft-unsung technique powering many recent ML successes; how does it work and where did the idea come…
Medium
Conditional Love: The Rise of Renormalization Techniques for Conditioning Neural Networks
Conditional renormalization is an oft-unsung technique powering many recent ML successes; how does it work and where did the idea come…
Minimizing the Societal Cost of Credit Card Fraud with Limited and Imbalanced Data.
http://arxiv.org/abs/1909.01486
🔗 Minimizing the Societal Cost of Credit Card Fraud with Limited and Imbalanced Data
Machine learning has automated much of financial fraud detection, notifying firms of, or even blocking, questionable transactions instantly. However, data imbalance starves traditionally trained models of the content necessary to detect fraud. This study examines three separate factors of credit card fraud detection via machine learning. First, it assesses the potential for different sampling methods, undersampling and Synthetic Minority Oversampling Technique (SMOTE), to improve algorithm performance in data-starved environments. Additionally, five industry-practical machine learning algorithms are evaluated on total fraud cost savings in addition to traditional statistical metrics. Finally, an ensemble of individual models is trained with a genetic algorithm to attempt to generate higher cost efficiency than its components. Monte Carlo performance distributions discerned random undersampling outperformed SMOTE in lowering fraud costs, and that an ensemble was unable to outperform its individual parts. Most
http://arxiv.org/abs/1909.01486
🔗 Minimizing the Societal Cost of Credit Card Fraud with Limited and Imbalanced Data
Machine learning has automated much of financial fraud detection, notifying firms of, or even blocking, questionable transactions instantly. However, data imbalance starves traditionally trained models of the content necessary to detect fraud. This study examines three separate factors of credit card fraud detection via machine learning. First, it assesses the potential for different sampling methods, undersampling and Synthetic Minority Oversampling Technique (SMOTE), to improve algorithm performance in data-starved environments. Additionally, five industry-practical machine learning algorithms are evaluated on total fraud cost savings in addition to traditional statistical metrics. Finally, an ensemble of individual models is trained with a genetic algorithm to attempt to generate higher cost efficiency than its components. Monte Carlo performance distributions discerned random undersampling outperformed SMOTE in lowering fraud costs, and that an ensemble was unable to outperform its individual parts. Most
arXiv.org
Minimizing the Societal Cost of Credit Card Fraud with Limited and...
Machine learning has automated much of financial fraud detection, notifying
firms of, or even blocking, questionable transactions instantly. However, data
imbalance starves traditionally trained...
firms of, or even blocking, questionable transactions instantly. However, data
imbalance starves traditionally trained...
Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Condition
https://research.fb.com/publications/findings-of-the-wmt-2019-shared-task-on-parallel-corpus-filtering-for-low-resource-conditions/
https://research.fb.com/wp-content/uploads/2019/09/Findings-of-the-WMT-2019-Shared-Task-on-Parallel-Corpus-Filtering-for-Low-Resource-Conditions.pdf?
🔗 Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions
Following the WMT 2018 Shared Task on Parallel Corpus Filtering (Koehn et al., 2018), we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2% and 10% of the highest-quality data to be used to train machine translation systems. This year, the task tackled the low resource condition of Nepali– English and Sinhala–English. Eleven participants from companies, national research labs, and universities participated in this task.
https://research.fb.com/publications/findings-of-the-wmt-2019-shared-task-on-parallel-corpus-filtering-for-low-resource-conditions/
https://research.fb.com/wp-content/uploads/2019/09/Findings-of-the-WMT-2019-Shared-Task-on-Parallel-Corpus-Filtering-for-Low-Resource-Conditions.pdf?
🔗 Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions
Following the WMT 2018 Shared Task on Parallel Corpus Filtering (Koehn et al., 2018), we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2% and 10% of the highest-quality data to be used to train machine translation systems. This year, the task tackled the low resource condition of Nepali– English and Sinhala–English. Eleven participants from companies, national research labs, and universities participated in this task.
Facebook Research
Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions - Facebook Research
Following the WMT 2018 Shared Task on Parallel Corpus Filtering (Koehn et al., 2018), we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2% and 10%…
Академия искусственного интеллекта
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Урок 1. Часть 1. Искусственный интеллект сегодня
Урок 1. Часть 2. Истоки ИИ 1950-1990
Урок 1. Часть 3. Недавние вехи ИИ
Урок 1. Часть 4. Новейшие разработки ИИ
Урок 1. Часть 5. Резюме
Урок 2. Часть 1. Введение в машинное обучение
Урок 2. Часть 2. Обучение с учителем
Урок 2. Часть 3. Модели машинного обучения
Урок 2. Часть 4. Пример задачи машинного обучения
Урок 2. Часть 5. Итоги
🎥 Урок 1. Часть 1. Искусственный интеллект сегодня (Академия искусственного интеллекта)
👁 1 раз ⏳ 158 сек.
🎥 Урок 1. Часть 2. Истоки ИИ 1950-1990 (Академия искусственного интеллекта)
👁 1 раз ⏳ 298 сек.
🎥 Урок 1. Часть 3. Недавние вехи ИИ (Академия искусственного интеллекта)
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🎥 Урок 1. Часть 4. Новейшие разработки ИИ (Академия искусственного интеллекта)
👁 1 раз ⏳ 234 сек.
🎥 Урок 1. Часть 5. Резюме (Академия искусственного интеллекта)
👁 1 раз ⏳ 186 сек.
🎥 Урок 2. Часть 1. Введение в машинное обучение (Академия искусственного интеллекта)
👁 1 раз ⏳ 195 сек.
🎥 Урок 2. Часть 2. Обучение с учителем (Академия искусственного интеллекта)
👁 1 раз ⏳ 372 сек.
🎥 Урок 2. Часть 3. Модели машинного обучения (Академия искусственного интеллекта)
👁 1 раз ⏳ 317 сек.
🎥 Урок 2. Часть 4. Пример задачи машинного обучения (Академия искусственного интеллекта)
👁 1 раз ⏳ 265 сек.
🎥 Урок 2. Часть 5. Итоги (Академия искусственного интеллекта)
👁 1 раз ⏳ 239 сек.
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Урок 1. Часть 1. Искусственный интеллект сегодня
Урок 1. Часть 2. Истоки ИИ 1950-1990
Урок 1. Часть 3. Недавние вехи ИИ
Урок 1. Часть 4. Новейшие разработки ИИ
Урок 1. Часть 5. Резюме
Урок 2. Часть 1. Введение в машинное обучение
Урок 2. Часть 2. Обучение с учителем
Урок 2. Часть 3. Модели машинного обучения
Урок 2. Часть 4. Пример задачи машинного обучения
Урок 2. Часть 5. Итоги
🎥 Урок 1. Часть 1. Искусственный интеллект сегодня (Академия искусственного интеллекта)
👁 1 раз ⏳ 158 сек.
Академия искусственного интеллекта для школьников 7-11 классов при поддержке Сбербанка и Благотворительного фонда "Вклад в будущее".
Больше уроков...🎥 Урок 1. Часть 2. Истоки ИИ 1950-1990 (Академия искусственного интеллекта)
👁 1 раз ⏳ 298 сек.
Академия искусственного интеллекта для школьников 7-11 классов при поддержке Сбербанка и Благотворительного фонда "Вклад в будущее".
Больше уроков...🎥 Урок 1. Часть 3. Недавние вехи ИИ (Академия искусственного интеллекта)
👁 1 раз ⏳ 398 сек.
Академия искусственного интеллекта для школьников 7-11 классов при поддержке Сбербанка и Благотворительного фонда "Вклад в будущее".
Больше уроков...🎥 Урок 1. Часть 4. Новейшие разработки ИИ (Академия искусственного интеллекта)
👁 1 раз ⏳ 234 сек.
Академия искусственного интеллекта для школьников 7-11 классов при поддержке Сбербанка и Благотворительного фонда "Вклад в будущее".
Больше уроков...🎥 Урок 1. Часть 5. Резюме (Академия искусственного интеллекта)
👁 1 раз ⏳ 186 сек.
Академия искусственного интеллекта для школьников 7-11 классов при поддержке Сбербанка и Благотворительного фонда "Вклад в будущее".
Больше уроков...🎥 Урок 2. Часть 1. Введение в машинное обучение (Академия искусственного интеллекта)
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Академия искусственного интеллекта для школьников 7-11 классов при поддержке Сбербанка и Благотворительного фонда "Вклад в будущее".
Больше уроков...🎥 Урок 2. Часть 2. Обучение с учителем (Академия искусственного интеллекта)
👁 1 раз ⏳ 372 сек.
Академия искусственного интеллекта для школьников 7-11 классов при поддержке Сбербанка и Благотворительного фонда "Вклад в будущее".
Больше уроков...🎥 Урок 2. Часть 3. Модели машинного обучения (Академия искусственного интеллекта)
👁 1 раз ⏳ 317 сек.
Академия искусственного интеллекта для школьников 7-11 классов при поддержке Сбербанка и Благотворительного фонда "Вклад в будущее".
Больше уроков...🎥 Урок 2. Часть 4. Пример задачи машинного обучения (Академия искусственного интеллекта)
👁 1 раз ⏳ 265 сек.
Академия искусственного интеллекта для школьников 7-11 классов при поддержке Сбербанка и Благотворительного фонда "Вклад в будущее".
Больше уроков...🎥 Урок 2. Часть 5. Итоги (Академия искусственного интеллекта)
👁 1 раз ⏳ 239 сек.
Академия искусственного интеллекта для школьников 7-11 классов при поддержке Сбербанка и Благотворительного фонда "Вклад в будущее".
Больше уроков...Vk
Урок 1. Часть 1. Искусственный интеллект сегодня (Академия искусственного интеллекта)
Академия искусственного интеллекта для школьников 7-11 классов при поддержке Сбербанка и Благотворительного фонда "Вклад в будущее". Больше уроков...
Adversarial Examples — Rethinking the Definition
🔗 Adversarial Examples — Rethinking the Definition
Adversarial examples are a large obstacle for a variety of machine learning systems to overcome. Their existence shows the tendency of…
🔗 Adversarial Examples — Rethinking the Definition
Adversarial examples are a large obstacle for a variety of machine learning systems to overcome. Their existence shows the tendency of…
Medium
Adversarial Examples — Rethinking the Definition
Adversarial examples are a large obstacle for a variety of machine learning systems to overcome. Their existence shows the tendency of…
🎥 Машинное обучение. Семинар 1. Fun with Embeddings
👁 3 раз ⏳ 1543 сек.
👁 3 раз ⏳ 1543 сек.
Семинар от 06.09.2019
Семинарист: Николай Карпачев
Ссылка на репозиторий: https://github.com/ml-mipt/ml-mipt/tree/part2_week01/part2/week01_word_embeddings
Снимал: Михаил Кревский
Монтировал: Артём СапожниковVk
Машинное обучение. Семинар 1. Fun with Embeddings
Семинар от 06.09.2019
Семинарист: Николай Карпачев
Ссылка на репозиторий: https://github.com/ml-mipt/ml-mipt/tree/part2_week01/part2/week01_word_embeddings
Снимал: Михаил Кревский
Монтировал: Артём Сапожников
Семинарист: Николай Карпачев
Ссылка на репозиторий: https://github.com/ml-mipt/ml-mipt/tree/part2_week01/part2/week01_word_embeddings
Снимал: Михаил Кревский
Монтировал: Артём Сапожников
A Keras Meta Model Served
🔗 A Keras Meta Model Served
Wrapping Keras Models into the Tensorflow Ecosystem
🔗 A Keras Meta Model Served
Wrapping Keras Models into the Tensorflow Ecosystem
Medium
A Keras Meta Model Served
Wrapping Keras Models into the Tensorflow Ecosystem
Facebook и Microsoft запускают конкурс по обнаружению deepfake
🔗 Facebook и Microsoft запускают конкурс по обнаружению deepfake
Facebook совместно с Microsoft и коалицией «Партнёрства по искусственному интеллекту во благо людей и общества» (PAI), а также научными работниками из нескольк...
🔗 Facebook и Microsoft запускают конкурс по обнаружению deepfake
Facebook совместно с Microsoft и коалицией «Партнёрства по искусственному интеллекту во благо людей и общества» (PAI), а также научными работниками из нескольк...
Habr
Facebook и Microsoft запускают конкурс по обнаружению deepfake
Facebook совместно с Microsoft и коалицией «Партнёрства по искусственному интеллекту во благо людей и общества» (PAI), а также научными работниками из нескольких университетов анонсировала конкурс...
К концу 2020 года все кредиты в Сбербанке будет одобрять искусственный интеллект
🔗 К концу 2020 года все кредиты в Сбербанке будет одобрять искусственный интеллект
К концу 2020 года 100% решений о выдаче кредитов физическим лицам в Сбербанке будет принимать искусственный интеллект. Об этом сообщил СМИ первый зампред правлен...
🔗 К концу 2020 года все кредиты в Сбербанке будет одобрять искусственный интеллект
К концу 2020 года 100% решений о выдаче кредитов физическим лицам в Сбербанке будет принимать искусственный интеллект. Об этом сообщил СМИ первый зампред правлен...
Хабр
К концу 2020 года все кредиты в Сбербанке будет одобрять искусственный интеллект
К концу 2020 года 100% решений о выдаче кредитов физическим лицам в Сбербанке будет принимать искусственный интеллект. Об этом сообщил СМИ первый зампред правления Сбербанка Александр...
Assessing the Quality of Long-Form Synthesized Speech
http://ai.googleblog.com/2019/09/assessing-quality-of-long-form.html
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
🔗 Assessing the Quality of Long-Form Synthesized Speech
Posted by Tom Kenter, Google Research, London Automatically generated speech is everywhere, from directions being read out aloud while y...
http://ai.googleblog.com/2019/09/assessing-quality-of-long-form.html
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
🔗 Assessing the Quality of Long-Form Synthesized Speech
Posted by Tom Kenter, Google Research, London Automatically generated speech is everywhere, from directions being read out aloud while y...
research.google
Assessing the Quality of Long-Form Synthesized Speech
Posted by Tom Kenter, Google Research, London Automatically generated speech is everywhere, from directions being read out aloud while you are dr...
🎥 Applied Deep Learning - Rosanne Liu on AI Research (2019)
👁 1 раз ⏳ 2824 сек.
👁 1 раз ⏳ 2824 сек.
Rosanne Liu is a Senior Research Scientist at Uber AI labs. She is currently working on the multiple fronts where machine learning and neural networks are mysterious. She shares her experiences working on ML projects as well as what she’s learned along the way.
This lecture was a part of the Applied Deep Learning Fellowship held at the Weights and Biases Headquarters in the spring of 2019.
For more tutorials: https://www.wandb.com/classes
To learn more about Weights & Biases: https://www.wandb.com/Vk
Applied Deep Learning - Rosanne Liu on AI Research (2019)
Rosanne Liu is a Senior Research Scientist at Uber AI labs. She is currently working on the multiple fronts where machine learning and neural networks are mysterious. She shares her experiences working on ML projects as well as what she’s learned along the…
🎥 SAS Demo | Deep Learning with Python (DLPy) and SAS Viya for Computer Vision
👁 1 раз ⏳ 2363 сек.
👁 1 раз ⏳ 2363 сек.
In this SAS demo, you'll learn about the SAS Deep Learning Python API, or DLPy for short. This series will focus on the newest computer vision models supported by DLPy. DLPy enables data scientists familiar with Python to take advantage of the deep learning and computer vision features in SAS Viya.
DLPy is available at – https://github.com/sassoftware/python-dlpy
These section may be watch in any order.
00:00 - Introduction to the Deep Learning with Python (DLPy) and SAS Viya for Computer Vision videoVk
SAS Demo | Deep Learning with Python (DLPy) and SAS Viya for Computer Vision
In this SAS demo, you'll learn about the SAS Deep Learning Python API, or DLPy for short. This series will focus on the newest computer vision models supported by DLPy. DLPy enables data scientists familiar with Python to take advantage of the deep learning…
🎥 Edureka Deep Learning Webinar | Deep Learning Tutorial For Beginners | Edureka Masterclass
👁 1 раз ⏳ 5554 сек.
👁 1 раз ⏳ 5554 сек.
(Edureka Meetup Community: http://bit.ly/2DQO5PL)
Join our Meetup community and get access to 100+ tech webinars/ month for FREE: http://bit.ly/2DQO5PL
Topics to be covered in this session:
1. What Is Artificial Intelligence?
2. Introduction To Deep Learning
3. How Does A Neural Network Work?
4. Hands-On
Know more about Edureka Meetup Community: http://bit.ly/2TypYMv
Subscribe to our Edureka YouTube channel to get video updates: https://goo.gl/6ohpTV
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Edureka Deep Learning Webinar | Deep Learning Tutorial For Beginners | Edureka Masterclass
(Edureka Meetup Community: http://bit.ly/2DQO5PL)
Join our Meetup community and get access to 100+ tech webinars/ month for FREE: http://bit.ly/2DQO5PL
Topics to be covered in this session:
1. What Is Artificial Intelligence?
2. Introduction To Deep Learning…
Join our Meetup community and get access to 100+ tech webinars/ month for FREE: http://bit.ly/2DQO5PL
Topics to be covered in this session:
1. What Is Artificial Intelligence?
2. Introduction To Deep Learning…
Creating Impact
🔗 Creating Impact
In some large tech companies, Data Scientists are evaluated by how much impact they make in the company. For example, if a data science…
🔗 Creating Impact
In some large tech companies, Data Scientists are evaluated by how much impact they make in the company. For example, if a data science…
Medium
Creating Impact
In some large tech companies, Data Scientists are evaluated by how much impact they make in the company. For example, if a data science…
Attribute Relevance Analysis in Python — IV and WoE
🔗 Attribute Relevance Analysis in Python — IV and WoE
Recently I’ve written about Recursive Feature Elimination — one of many feature selection techniques I use most often. Today I will speak…
🔗 Attribute Relevance Analysis in Python — IV and WoE
Recently I’ve written about Recursive Feature Elimination — one of many feature selection techniques I use most often. Today I will speak…
Medium
Attribute Relevance Analysis in Python — IV and WoE
Recently I’ve written about Recursive Feature Elimination — one of many feature selection techniques I use most often. Today I will speak…