​Abstractive Text Summarization with NLP

🔗 Abstractive Text Summarization with NLP
RNNs, LSTMs, and Word Embeddings For Text Summarization

​Создание детектора социального дистанцирования

В этом туториале вы узнаете, как реализовать детектор социального дистанцирования COVID-19 с использованием OpenCV, глубокого обучения и компьютерного зрения.

https://www.pyimagesearch.com/2020/06/01/opencv-social-distancing-detector/

🔗 OpenCV Social Distancing Detector - PyImageSearch
In this tutorial, you will learn how to implement a COVID-19 social distancing detector using OpenCV, Deep Learning, and Computer Vision.

​Proposing a new effect of learning rate decay — Network Stability

🔗 Proposing a new effect of learning rate decay — Network Stability
Uncovering learning rate as a form of regularisation in stochastic gradient descent

​How to create a C++ project using Ceres Solver?

🔗 How to create a C++ project using Ceres Solver?
Step-by-step procedure to get started with Ceres Solver

​Create your own Word Cloud

🔗 Create your own Word Cloud
Learn to build a very simple word cloud using Python using only a few lines of code!

🎥 Classifying sound using Machine Learning (Artificial Summit February 2020 @ KnowIt)
👁 1 раз ⏳ 3433 сек.
This is a repost of https://www.youtube.com/watch?v=1H63PewtDbo

Presentation slides and notes available at https://github.com/jonnor/machinehearing#classifying-sound-using-machine-learning

​How to Perform Feature Selection for Regression Data - Machine Learning Mastery

🔗 How to Perform Feature Selection for Regression Data - Machine Learning Mastery
Feature selection is the process of identifying and selecting a subset of input variables that are most relevant to the target variable. Perhaps the simplest case of feature selection is the case where there are numerical input variables and a numerical target for regression predictive modeling. This is because the strength of the relationship between each input variable and the target

​How to Perform Feature Selection for Regression Data - Machine Learning Mastery

🔗 How to Perform Feature Selection for Regression Data - Machine Learning Mastery
Feature selection is the process of identifying and selecting a subset of input variables that are most relevant to the target variable. Perhaps the simplest case of feature selection is the case where there are numerical input variables and a numerical target for regression predictive modeling. This is because the strength of the relationship between each input variable and the target

​Learn Algorithmic Trading Build and deploy algorithmic trading systems
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Открыть в Telegram



📝 Learn Algorithmic Trading Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis.. - 💾16 700 530

​How to Deploy TensorFlow Models to the Web

🔗 How to Deploy TensorFlow Models to the Web
Using basic JavaScript to run Python-built models in the browser

​How to Deploy TensorFlow Models to the Web

🔗 How to Deploy TensorFlow Models to the Web
Using basic JavaScript to run Python-built models in the browser

​A Scalable and Cloud-Native Hyperparameter Tuning System

Katib is a Kubernetes-based system for Hyperparameter Tuning and Neural Architecture Search. Katib supports a number of ML frameworks, including TensorFlow, Apache MXNet, PyTorch, XGBoost, and others.

Github: https://github.com/kubeflow/katib

Getting started with Katib: https://www.kubeflow.org/docs/components/hyperparameter-tuning/hyperparameter/

Paper: https://arxiv.org/abs/2006.02085v1
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 kubeflow/katib
Repository for hyperparameter tuning. Contribute to kubeflow/katib development by creating an account on GitHub.

​A Scalable and Cloud-Native Hyperparameter Tuning System

Katib is a Kubernetes-based system for Hyperparameter Tuning and Neural Architecture Search. Katib supports a number of ML frameworks, including TensorFlow, Apache MXNet, PyTorch, XGBoost, and others.

Github: https://github.com/kubeflow/katib

Getting started with Katib: https://www.kubeflow.org/docs/components/hyperparameter-tuning/hyperparameter/

Paper: https://arxiv.org/abs/2006.02085v1
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 kubeflow/katib
Repository for hyperparameter tuning. Contribute to kubeflow/katib development by creating an account on GitHub.

​Интервью с роботом

🔗 Интервью с роботом
С точки зрения робота Сегодня мы продолжаем тему направленного обучения. Этот способ не требует больших данных, использует короткие выборки. Модет быть полезен д...

​Интервью с роботом

🔗 Интервью с роботом
С точки зрения робота Сегодня мы продолжаем тему направленного обучения. Этот способ не требует больших данных, использует короткие выборки. Модет быть полезен д...

​End-to-End Adversarial Text-to-Speech

https://arxiv.org/abs/2006.03575

🔗 End-to-End Adversarial Text-to-Speech
Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which operate directly on character or phoneme input sequences and produce raw speech audio outputs. Our proposed generator is feed-forward and thus efficient for both training and inference, using a differentiable monotonic interpolation scheme to predict the duration of each input token. It learns to produce high fidelity audio through a combination of adversarial feedback and prediction losses constraining the generated audio to roughly match the ground truth in terms of its total duration and mel-spectrogram. To allow the model to capture temporal variation in the generated audio, we employ soft dynamic time warping in the spectrogram-based prediction loss. The resulting model achieves a mean opinion

​End-to-End Adversarial Text-to-Speech

https://arxiv.org/abs/2006.03575

🔗 End-to-End Adversarial Text-to-Speech
Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which operate directly on character or phoneme input sequences and produce raw speech audio outputs. Our proposed generator is feed-forward and thus efficient for both training and inference, using a differentiable monotonic interpolation scheme to predict the duration of each input token. It learns to produce high fidelity audio through a combination of adversarial feedback and prediction losses constraining the generated audio to roughly match the ground truth in terms of its total duration and mel-spectrogram. To allow the model to capture temporal variation in the generated audio, we employ soft dynamic time warping in the spectrogram-based prediction loss. The resulting model achieves a mean opinion

​Recent Advances in Google Translate">
Recent Advances in Google Translate

🔗 Recent Advances in Google Translate
Posted by Isaac Caswell and Bowen Liang, Software Engineers, Google Research Advances in machine learning (ML) have driven improvements ...

​Recent Advances in Google Translate">
Recent Advances in Google Translate

🔗 Recent Advances in Google Translate
Posted by Isaac Caswell and Bowen Liang, Software Engineers, Google Research Advances in machine learning (ML) have driven improvements ...