​AI Creates Near Perfect Images Of People, Dogs and More

🔗 AI Creates Near Perfect Images Of People, Dogs and More
❤️ Check out Weights & Biases here and sign up for a free demo: https://www.wandb.com/papers 📝 The paper "Generating Diverse High-Fidelity Images with VQ-VAE-2" and its supplementary materials are available here: https://arxiv.org/abs/1906.00446 https://drive.google.com/file/d/1H2nr_Cu7OK18tRemsWn_6o5DGMNYentM/view Our latent-space material synthesis paper is available here: https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ 🙏 We would like to thank our generous Patreon supporters who

​Awesome Fraud Detection Research Papers

https://github.com/benedekrozemberczki/awesome-fraud-detection-papers

🔗 benedekrozemberczki/awesome-fraud-detection-papers
A curated list of data mining papers about fraud detection. - benedekrozemberczki/awesome-fraud-detection-papers

State of Data Science & Machine Learning - Peter Wang
https://www.youtube.com/watch?v=S3FpT4xMyn4

🎥 State of Data Science & Machine Learning - Peter Wang
👁 1 раз ⏳ 2292 сек.
As machine learning and AI become adopted at an increasing rate, businesses and practitioners face new types of challenges. At the heart of many of these lies an uncomfortable truth: that data science is not merely a new kind of technical specialty, but rather that it represents an opportunity for deep business transformation. In this talk, Peter speaks to this concept that Data Science isn’t just a “job”, it’s actually a democratization of empiricism. Furthermore, the idea of “democratization” is intertwin

​Simple and multiple linear regression with Python
Linear regression is a linear approach to model the relationship between a dependent variable
https://towardsdatascience.com/simple-and-multiple-linear-regression-with-python-c9ab422ec29c?source=collection_home---4------1-----------------------

🔗 Simple and multiple linear regression with Python
Linear regression is a linear approach to model the relationship between a dependent variable (target variable) and one (simple…

​How to Train Your Quadcopter
The beginner’s guide to teaching a quadcopter to fly (with code!)
https://towardsdatascience.com/how-to-train-your-quadcopter-adventures-in-machine-learning-algorithms-e6ee5033fd61?source=collection_home---4------0-----------------------

🔗 How to Train Your Quadcopter
The beginner’s guide to teaching a quadcopter to fly (with code!)

​The theory you need to know before you start an NLP project
An overview of the most common natural language processing and machine learning techniques needed to start tackling any project involving
https://towardsdatascience.com/the-theory-you-need-to-know-before-you-start-an-nlp-project-1890f5bbb793?source=collection_home---4------0-----------------------

🔗 The theory you need to know before you start an NLP project
An overview of the most common natural language processing and machine learning techniques needed to start tackling any project involving…

OpenCV Python Tutorial For Beginners 28 - Hough Line Transform Theory
https://www.youtube.com/watch?v=7m-RVJ6ABsY

🎥 OpenCV Python Tutorial For Beginners 28 - Hough Line Transform Theory
👁 1 раз ⏳ 759 сек.
In this video on OpenCV Python Tutorial For Beginners, we are going to understand the concept of the Hough Transform and Hough Line Transform Theory.

OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C++, and Python.
it is Open Source and free. opencv is easy to use and install.

Starting with an overview of what the course will be covering, we move on to discussing morphological

Linear Regression - Introduction to Machine Learning using Python and Scikit Learn Chapter 6 1
https://www.youtube.com/watch?v=3Q6gzUPecLE

🎥 Linear Regression - Introduction to Machine Learning using Python and Scikit Learn Chapter 6 1
👁 1 раз ⏳ 663 сек.
Welcome to the video series on Introduction to Machine Learning with Scikit-Learn.

This video contains Chapter - 6.1. In this chapter, I've explained our first Machine Learning algorithm called Linear Regression using just five data points for easy understanding

This video describes what is Linear Regression and how we can use the same using Scikit-learn. In context of this algorithm, I've also explained the unified machine learning algorithm and how generic interface can be used for almost all ML algori

​Visualizing Keras Models
I love how simple and clear Keras makes it to build neural networks. With wandb, you can visualize your network’s performance
https://towardsdatascience.com/visualizing-keras-models-49d591931209?source=topic_page---------42------------------1

🔗 Lightweight Visualization of Keras Models
I love how simple and clear Keras makes it to build neural networks. With wandb, you can visualize your network’s performance and…

​Deep convolutional neural networks for uncertainty propagation in random fields

Authors: Xihaier Luo, Ahsan Kareem

Abstract: …manageable dimensions and then the condensed representation is used for surrogate modeling. In this study, we present a new solution scheme for this type of problems based on a deep learning approach. The proposed surrogate is based on a particular network architecture, i.e. the convolutional neural networks.
https://arxiv.org/abs/1907.11198

🔗 Deep convolutional neural networks for uncertainty propagation in random fields
The development of a reliable and robust surrogate model is often constrained by the dimensionality of the problem. For a system with high-dimensional inputs/outputs (I/O), conventional approaches usually use a low-dimensional manifold to describe the high-dimensional system, where the I/O data is first reduced to more manageable dimensions and then the condensed representation is used for surrogate modeling. In this study, we present a new solution scheme for this type of problems based on a deep learning approach. The proposed surrogate is based on a particular network architecture, i.e. the convolutional neural networks. The surrogate architecture is designed in a hierarchical style containing three different levels of model structures, advancing the efficiency and effectiveness of the model in the aspect of training and deploying. To assess the model performance, we carry out uncertainty quantification in a continuum mechanics benchmark problem. Numerical results suggest the proposed model is capable of d

Высшая математика. Дифференциальные уравнения

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
✅Обыкновенные дифференциальные уравнения. Основные понятия
✅Дифференциальные уравнения 1-го порядка. Основные понятия
✅Дифференциальные уравнения с разделяющимися переменными, ч.1
✅Дифференциальные уравнения с разделяющимися переменными, ч.2
✅Дифференциальные уравнения с разделяющимися переменными, ч.3
✅Дифференциальные уравнения, не содержащие явно искомой функции, ч.1
✅Дифференциальные уравнения, не содержащие явно искомой функции, ч.2
✅Дифференциальные уравнения, не содержащие явно искомой функции, ч.3
✅Дифференциальные уравнения, не содержащие явно независимой переменной, ч.1
✅Дифференциальные уравнения, не содержащие явно независимой переменной, ч.2

🎥 Обыкновенные дифференциальные уравнения. Основные понятия. Высшая математика.
👁 1 раз ⏳ 652 сек.
Обыкновенные дифференциальные уравнения. Основные понятия. Высшая математика.

🎥 Дифференциальные уравнения 1-го порядка. Основные понятия. Высшая математика.
👁 1 раз ⏳ 1025 сек.
Дифференциальные уравнения 1-го порядка. Основные понятия. Высшая математика.

🎥 Дифференциальные уравнения с разделяющимися переменными (часть 1). Высшая математика.
👁 1 раз ⏳ 1586 сек.
Дифференциальные уравнения с разделяющимися переменными (часть 1). Высшая математика.

🎥 Дифференциальные уравнения с разделяющимися переменными (часть 2). Высшая математика.
👁 1 раз ⏳ 807 сек.
Дифференциальные уравнения с разделяющимися переменными (часть 2). Высшая математика.

🎥 Дифференциальные уравнения, не содержащие явно искомой функции (часть 1). Высшая математика.
👁 1 раз ⏳ 980 сек.
Дифференциальные уравнения, не содержащие явно искомой функции (часть 1). Высшая математика.

🎥 Дифференциальные уравнения, не содержащие явно искомой функции (часть 2). Высшая математика.
👁 1 раз ⏳ 564 сек.
Дифференциальные уравнения, не содержащие явно искомой функции (часть 2). Высшая математика.

🎥 Дифференциальные уравнения, не содержащие явно искомой функции (часть 3). Высшая математика.
👁 1 раз ⏳ 649 сек.
Дифференциальные уравнения, не содержащие явно искомой функции (часть 3). Высшая математика.

🎥 Дифференциальные уравнения с разделяющимися переменными (часть 3). Высшая математика.
👁 1 раз ⏳ 987 сек.


🎥 Дифференциальные уравнения, не содержащие явно независимой переменной (часть 1). Высшая математика.
👁 1 раз ⏳ 1483 сек.
Дифференциальные уравнения, не содержащие явно независимой переменной (часть 1). Высшая математика.

🎥 Дифференциальные уравнения, не содержащие явно независимой переменной (часть 2). Высшая математика.
👁 1 раз ⏳ 743 сек.
Дифференциальные уравнения, не содержащие явно независимой переменной (часть 2). Высшая математика.

​Could advances in AI technology re-shape music as we know it?
Music has been big business for a long time, but these days it’s not just record companies, concert promoters and (occasionally)

🔗 Could advances in AI technology re-shape music as we know it?
Music has been big business for a long time, but these days it’s not just record companies, concert promoters and (occasionally) artists…

​Computers Can’t Tell If You’re Happy When You Smile
Emotion recognition is a $20 billion industry, but a new study says the most popular method is deeply flawed

🔗 Computers Can’t Tell If You’re Happy When You Smile
Emotion recognition is a $20 billion industry, but a new study says the most popular method is deeply flawed

​New State of the Art in Semantic Segmentation https://arxiv.org/abs/1907.05740

🔗 Gated-SCNN: Gated Shape CNNs for Semantic Segmentation
Current state-of-the-art methods for image segmentation form a dense image representation where the color, shape and texture information are all processed together inside a deep CNN. This however may not be ideal as they contain very different type of information relevant for recognition. Here, we propose a new two-stream CNN architecture for semantic segmentation that explicitly wires shape information as a separate processing branch, i.e. shape stream, that processes information in parallel to the classical stream. Key to this architecture is a new type of gates that connect the intermediate layers of the two streams. Specifically, we use the higher-level activations in the classical stream to gate the lower-level activations in the shape stream, effectively removing noise and helping the shape stream to only focus on processing the relevant boundary-related information. This enables us to use a very shallow architecture for the shape stream that operates on the image-level resolution. Our experiments show that this leads to a highly effective architecture that produces sharper predictions around object boundaries and significantly boosts performance on thinner and smaller objects. Our method achieves state-of-the-art performance on the Cityscapes benchmark, in terms of both mask (mIoU) and boundary (F-score) quality, improving by 2% and 4% over strong baselines.

Deep Q-Network Training Code - Reinforcement Learning Code Project
https://www.youtube.com/watch?v=ewRw996uevM

🎥 Deep Q-Network Training Code - Reinforcement Learning Code Project
👁 3 раз ⏳ 1186 сек.
Welcome back to this series on reinforcement learning! In this episode we’ll be bringing together all the classes and functions we’ve developed so far, and incorporating them into our main program to train our deep Q-network for the cart and pole environment. We’ll see the training process live as we watch our agent’s ability to balance the pole on the cart increase as it learns.

💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥

👀 OUR VLOG:
🔗 https://www.youtube.com/channel/UC9cBIteC3u7Ee6bzeOcl_Og

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#Telegram #Бот #OpenCV
Telegram бот с компьютерным зрением на Python. Система охраны из ноутбука.
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://www.youtube.com/watch?v=z8o2hfEbMsw

🎥 Telegram бот с компьютерным зрением на Python. Система охраны из ноутбука.
👁 1 раз ⏳ 624 сек.
Сегодня мы рассмотрим применение компьютерного зрения, а в частности библиотеки OpenCV в связке с Telegram ботом намисанным на языке Python. Для реализации построения бюджетной охранной системы из простого ноутбука.
#Telegram #Бот #OpenCV #Python

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​Instance Selection: The myth behind Data Sampling
One of the most common and most challenging issues in any Big Data system

🔗 Instance Selection: The myth behind Data Sampling
One of the most common and most challenging issues in any Big Data system is to select stratified samples in a way that it’s…

​Reinforcement Learning: let’s teach a taxi-cab how to drive
Reinforcement Learning is a subfield of Machine Learning whose tasks differ from ‘standard’ ways of learning. Indeed

https://towardsdatascience.com/reinforcement-learning-lets-teach-a-taxi-cab-how-to-drive-4fd1a0d00529?source=collection_home---4------1-----------------------

🔗 Reinforcement Learning: let’s teach a taxi-cab how to drive
Reinforcement Learning is a subfield of Machine Learning whose tasks differ from ‘standard’ ways of learning. Indeed, rather than being…

​A Data Scientist’s Approach to Visual Audio Comparison
https://towardsdatascience.com/a-data-scientists-approach-to-visual-audio-comparison-fa15a5d3dcef

🔗 A Data Scientist’s Approach to Visual Audio Comparison
In this article, I demonstrate some custom ways I created to visually compare the frequency domains of multiple audio files. These tools…

​A Deep Dive into NLP with PyTorch.
how to implement more advanced architectures and apply it to real world datasets.

https://docs.google.com/presentation/d/1zyuwCx7knqnP-LJswlDfWSmk5FhFgFmYJGqdEZn8yhc/edit#slide=id.g33c734b530_0_656

Github link:

https://github.com/scoutbee/pytorch-nlp-notebooks

🔗 PyData London 2019-07-12
A Deep Dive into NLP with PyTorch PyData London Jeffrey Hsu & Susannah Klaneček 2019-07-12