🎥 Training Model - Deep Learning and Neural Networks with Python and Pytorch p.4
👁 2 раз ⏳ 1856 сек.
In this deep learning with Python and Pytorch tutorial, we'll be actually training this neural network by learning how to iterate over our data, pass to the model, calculate loss from the result, and then do backpropagation to slowly fit our model to the data.

Text-based tutorials and sample code: https://pythonprogramming.net/training-deep-learning-neural-network-pytorch/

Linode Cloud GPUs $20 credit: https://linode.com/sentdex

Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ

​DCTD: Deep Conditional Target Densities for Accurate Regression

Authors: Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat, Thomas B. Schön

Abstract: While deep learning-based classification is generally addressed using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidence-based regression, which entails predicting a confidence value for each input-target pair (x, y). While this approach has demonstrated impressive results, it requires important task-dependent design choices, and the predicted confidences often lack a natural probabilistic meaning. We address these issues by proposing
https://arxiv.org/abs/1909.12297

🔗 DCTD: Deep Conditional Target Densities for Accurate Regression
While deep learning-based classification is generally addressed using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidence-based regression, which entails predicting a confidence value for each input-target pair (x, y). While this approach has demonstrated impressive results, it requires important task-dependent design choices, and the predicted confidences often lack a natural probabilistic meaning. We address these issues by proposing Deep Conditional Target Densities (DCTD), a novel and general regression method with a clear probabilistic interpretation. DCTD models the conditional target density p(y|x) by using a neural network to directly predict the un-normalized density from (x, y). This model of p(y|x) is trained by minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. We perform comprehensive experiments on four computer vision regression tasks. Our app

​Trueface Tutorials: Convert MXNet Models into High-Performance Inference Frameworks

🔗 Trueface Tutorials: Convert MXNet Models into High-Performance Inference Frameworks
MXNet is a great framework when it comes to prototyping and training models. Developed by The Apache Software Foundation, MXNet offers a…

​Ai: Where To Begin?

🔗 Ai: Where To Begin?
Organizations widely recognize the potential power of artificial intelligence (Ai). They instinctively understand that it feels like we’re…

​Optimising a Machine Learning Model with the Confusion Matrix

🔗 Optimising a Machine Learning Model with the Confusion Matrix
How to tune a classifier dependant on your use case, including a walk through in python

​Objects Counting by Estimating a Density Map With Convolutional Neural Networks

🔗 Objects Counting by Estimating a Density Map With Convolutional Neural Networks
Written by Tomasz Bonus and Tomasz Golan.

​Hey, Can (A)I Get Your Number?

🔗 Hey, Can (A)I Get Your Number?
Using CNNs to help love flourish in the 21st century

Искусственный интеллект простыми словами

✅Как учатся машины | Искусственный интеллект
✅Искусственный интеллект и машинное обучение
✅Искусственный интеллект и нейронные сети
✅Искусственный интеллект в юриспруденции
✅Искусственный интеллект в филологии и журналистике
✅Искусственный интеллект в сельском хозяйстве
✅Искусственный интеллект в бизнесе и финансах
✅Искусственный интеллект в медицине и биологии
✅Искусственный интеллект в педагогике и психологии

#video #ai

🎥 Как учатся машины | Искусственный интеллект
👁 40 раз ⏳ 483 сек.
Искусственный интеллект простыми словами

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👁 5 раз ⏳ 686 сек.
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🎥 Искусственный интеллект в юриспруденции
👁 9 раз ⏳ 378 сек.
Примеры использования искусственного интеллекта в юриспруденции

🎥 Искусственный интеллект в филологии и журналистике
👁 1 раз ⏳ 308 сек.
Примеры использования искусственного интеллекта в журналистике и филологии

🎥 Искусственный интеллект в сельском хозяйстве
👁 4 раз ⏳ 188 сек.
Примеры использования искусственного интеллекта в сельском хозяйстве

🎥 Искусственный интеллект в бизнесе и финансах
👁 1 раз ⏳ 248 сек.
Примеры использования искусственного интеллекта в бизнесе и финансах

🎥 Искусственный интеллект в медицине и биологии
👁 6 раз ⏳ 146 сек.
Примеры использования искусственного интеллекта в медицине и биологии

🎥 Искусственный интеллект в педагогике и психологии
👁 6 раз ⏳ 173 сек.
Примеры использования искусственного интеллекта в педагогике и психологии

​Погружение в свёрточные нейронные сети: передача обучения (transfer learning)
Полный курс на русском языке можно найти по этой ссылке.
Оригинальный курс на английском доступен по этой ссылке.

🔗 Погружение в свёрточные нейронные сети: передача обучения (transfer learning)
Полный курс на русском языке можно найти по этой ссылке. Оригинальный курс на английском доступен по этой ссылке. Содержание Интервью с Себастьяном Труном Введ...

​ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Lan et al. Google
arxiv.org/abs/1909.11942

🔗 ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations, longer training times, and unexpected model degradation. To address these problems, we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large.

​Power BI as a Tool for Business Intelligence

🔗 Power BI as a Tool for Business Intelligence
An article about the advantages of Power BI as a tool for BI.

DeepMind Measures 7 Capabilities Every AI Should Have

video: https://www.youtube.com/watch?v=zrF5_O92ELQ

📝 The paper "Behaviour Suite for Reinforcement Learning"

https://arxiv.org/abs/1908.03568

code https://github.com/deepmind/bsuite

🎥 DeepMind Measures 7 Capabilities Every AI Should Have
👁 1 раз ⏳ 242 сек.
❤️ Thank you so much for your support on Patreon: https://www.patreon.com/TwoMinutePapers

📝 The paper "Behaviour Suite for Reinforcement Learning" is available here:
https://arxiv.org/abs/1908.03568
https://github.com/deepmind/bsuite

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Bruno Brito, Bryan Learn, Christian Ahlin, Christoph Jadanowski, Claudio Fernandes, Daniel Hasega

​LSTM for time series prediction

🔗 LSTM for time series prediction
Training a Long Short Term Memory Neural Network with PyTorch and forecasting Bitcoin trading data

​Why Kaggle Is Not Inclusive and How to Improve It.

🔗 Why Kaggle Is Not Inclusive and How to Improve It.
‘If you want to be good at swimming in pools, that is fine, go for Kaggle. If you want to be good on the open sea, go for Omdena’ —…

​A Non-Confusing Guide to Confusion Matrix

🔗 A Non-Confusing Guide to Confusion Matrix
Uncover how your predictive model can be improved just from analyzing a simple 2x2 matrix

​Is There a Difference Between Open Data and Public Data?

🔗 Is There a Difference Between Open Data and Public Data?
There is a general consensus that when we talk about open data we are referring to any piece of data or content that is free to access…

🎥 Using Docker and Kubernetes to Simplify Machine Learning
👁 1 раз ⏳ 2170 сек.

​How can I trust you?

🔗 How can I trust you?
An intuition and tutorial on trust score

🎥 Learn Intel Ai With Gerald. How to set up Ai environment
👁 1 раз ⏳ 5229 сек.
Learn Ai with #IntelAi #tensorflow #deeplearning #Machine Learning

🎥 How to Become a Deep Learning Expert
👁 1 раз ⏳ 1431 сек.
In this video you will learn how to level up in your deep learning expertise. I share the path I took, and give you my guidelines on how to think about expertise.

You have to recognize that expertise is a sliding scale, rather than a state of being. Even the deep learning pioneers are learning more each day, and are gaining in expertise over time.

The key is to gradually increase your skills in mathematics and implementing cutting edge solutions at the forefront of deep learning. Always be striving for