How to Implement Progressive Growing GAN Models in Keras
In this tutorial, you will discover how to develop progressive growing generative adversarial network models from scratch with Keras.
http://bit.ly/2NaEyIK
In this tutorial, you will discover how to develop progressive growing generative adversarial network models from scratch with Keras.
http://bit.ly/2NaEyIK
Machine Learning Mastery
How to Implement Progressive Growing GAN Models in Keras - Machine Learning Mastery
The progressive growing generative adversarial network is an approach for training a deep convolutional neural network model for generating synthetic images.
It is an extension of the more traditional GAN architecture that involves incrementally growing…
It is an extension of the more traditional GAN architecture that involves incrementally growing…
Parameter optimization in neural networks
Play with three interactive visualizations and develop your intuition for optimizing model parameters.
http://bit.ly/2Z2IQt1
Play with three interactive visualizations and develop your intuition for optimizing model parameters.
http://bit.ly/2Z2IQt1
www.deeplearning.ai
Parameter optimization in neural networks
Data Science Pioneers - Conquering the Next Frontier
Data Science Pioneers is a documentary investigating the future of data science with key data science leaders from across Europe and North America.
Trailer http://bit.ly/2Nl7DRO
http://bit.ly/2NjLCmE
Data Science Pioneers is a documentary investigating the future of data science with key data science leaders from across Europe and North America.
Trailer http://bit.ly/2Nl7DRO
http://bit.ly/2NjLCmE
Vimeo
Data Science Pioneers Excerpt - Triveni Ghandi @Dataiku
This is "Data Science Pioneers Excerpt - Triveni Ghandi @Dataiku" by Dataiku on Vimeo, the home for high quality videos and the people who love them.
Introducing the New Snorkel
Snorkel is a Python library for programmatically building and managing training datasets. In their latest update, the Snorkel Team walks through key new features, new tutorials, and the road ahead.
http://bit.ly/2NhrLV8
Snorkel is a Python library for programmatically building and managing training datasets. In their latest update, the Snorkel Team walks through key new features, new tutorials, and the road ahead.
http://bit.ly/2NhrLV8
74 Summaries of Machine Learning and NLP Research
These short summaries of Machine Learning and NLP research papers cover a wide variety of authors, topics and venues from the past couple of years. Includes key points, diagrams and links for each paper.
http://bit.ly/2D6nTjA
These short summaries of Machine Learning and NLP research papers cover a wide variety of authors, topics and venues from the past couple of years. Includes key points, diagrams and links for each paper.
http://bit.ly/2D6nTjA
Marek Rei
74 Summaries of Machine Learning and NLP Research - Marek Rei
My previous post on summarising 57 research papers turned out to be quite useful for people working in this field, so it is about time…
Hey folks,
Excited to announce that my team at VITech (VITech Lab) has released our first free Deep Learning Container at AWS Marketplace - http://bit.ly/DeepLearningContainer
This is a fully pre-configured docker container with TensorFlow 2.0 and open-source libraries for Machine Learning, including Jupyter Lab & Notebook, Keras, Theano, PyTorch, OpenCV, H2O, CNTK, NVIDIA CUDA, cuDNN, Numpy, Scipy, scikit-learn, XGBoost, etc. Use the container to reduce the time and resources spent on settings, configurations, and installs.
Try it for free now: http://bit.ly/DeepLearningContainer
If you have any issues or questions, feel free to reach out to me!
Excited to announce that my team at VITech (VITech Lab) has released our first free Deep Learning Container at AWS Marketplace - http://bit.ly/DeepLearningContainer
This is a fully pre-configured docker container with TensorFlow 2.0 and open-source libraries for Machine Learning, including Jupyter Lab & Notebook, Keras, Theano, PyTorch, OpenCV, H2O, CNTK, NVIDIA CUDA, cuDNN, Numpy, Scipy, scikit-learn, XGBoost, etc. Use the container to reduce the time and resources spent on settings, configurations, and installs.
Try it for free now: http://bit.ly/DeepLearningContainer
If you have any issues or questions, feel free to reach out to me!
The 2019 AI Index report
The AI Index Report from the HAI group at Stanford is a "starting point for informed conversations about the state of AI." The report is organized into 9 chapters that cover a variety of topics including things like Research and Development, Conferences, Technical Performance, The Economy, Education, Autonomous Systems, Public Perception, Societal Considerations, National Strategies and Global AI Vibrancy.
https://stanford.io/2s1wv9f
The AI Index Report from the HAI group at Stanford is a "starting point for informed conversations about the state of AI." The report is organized into 9 chapters that cover a variety of topics including things like Research and Development, Conferences, Technical Performance, The Economy, Education, Autonomous Systems, Public Perception, Societal Considerations, National Strategies and Global AI Vibrancy.
https://stanford.io/2s1wv9f
Stanford HAI
AI Index 2019
@catalyst_team are happy to announce the release of Catalyst v20.01.3, DL/RL framework for PyTorch
What is in Catalyst:
- Universal train/inference loop.
- Configuration files for model/data hyperparameters.
- Reproducibility - all source code and the environment will be saved.
- Callbacks - reusable train/inference pipeline parts.
- Training stages support.
- Easy customization.
- PyTorch best practices (Ranger optimizer, OneCycle, FP16 and more).
http://bit.ly/38RRevJ
What is in Catalyst:
- Universal train/inference loop.
- Configuration files for model/data hyperparameters.
- Reproducibility - all source code and the environment will be saved.
- Callbacks - reusable train/inference pipeline parts.
- Training stages support.
- Easy customization.
- PyTorch best practices (Ranger optimizer, OneCycle, FP16 and more).
http://bit.ly/38RRevJ
Self-Driving Research in Review: NeurIPS 2019
In this article, Peter Ondruska and Vladimir Iglovikov present an overview of the most relevant papers on self-driving technology from the Workshop on Machine Learning for Autonomous Driving and the NeurIPS conference.
http://bit.ly/36GQHLH
In this article, Peter Ondruska and Vladimir Iglovikov present an overview of the most relevant papers on self-driving technology from the Workshop on Machine Learning for Autonomous Driving and the NeurIPS conference.
http://bit.ly/36GQHLH
Exploratory Data Analysis for NLP: A Complete Guide to Python Tools
In this article, you will learn nearly all major techniques you can use to research your text data. It also includes a complete tour of Python tools that can help you get the job done.
http://bit.ly/31gdXyW
In this article, you will learn nearly all major techniques you can use to research your text data. It also includes a complete tour of Python tools that can help you get the job done.
http://bit.ly/31gdXyW
Introducing Label Studio, a swiss army knife of data labeling
In this article, you will learn about open-source data labeling, annotation, and exploration tool - Label Studio. Label Studio works with texts, images, audios, and HTML documents right out of the box.
http://bit.ly/2RQVBli
In this article, you will learn about open-source data labeling, annotation, and exploration tool - Label Studio. Label Studio works with texts, images, audios, and HTML documents right out of the box.
http://bit.ly/2RQVBli
12 февраля приглашаем всех на второй Open Data Science Meetup в Одессе. На нем вы познакомитесь с аугментацией и семантической сегментацией изображений, а также новыми ML сервисами от AWS.
Будет организована онлайн-трансляция.
Подробности и регистрация: http://bit.ly/31uOJx2
Будет организована онлайн-трансляция.
Подробности и регистрация: http://bit.ly/31uOJx2
Hey, I’d like to restart the digest with a new feature. Now, I can automatically share with you the most interesting links from my blog collection. Please vote for the links to help me pick the best blogs. To add the blogs, fill out the form: http://bit.ly/2IVGq54
PyImageSearch Blog: I want to help you the best I can during COVID-19
http://bit.ly/3b3J2Ka
If you read yesterday’s blog post on COVID-19 detection with Keras and TensorFlow, then you know what this blog post is about: I’ve put together learning resources, both free and paid, for PyImageSearch readers to study CV/DL during the COVID-19…
The post I want to help you the best I can during COVID-19 appeared first on PyImageSearch.
#announcements #announcements #deep-learning #medical
http://bit.ly/3b3J2Ka
If you read yesterday’s blog post on COVID-19 detection with Keras and TensorFlow, then you know what this blog post is about: I’ve put together learning resources, both free and paid, for PyImageSearch readers to study CV/DL during the COVID-19…
The post I want to help you the best I can during COVID-19 appeared first on PyImageSearch.
#announcements #announcements #deep-learning #medical
Towards Data Science: The AI Ecosystem is a MESS
http://bit.ly/39ZT6ng
Why is it impossible to understand what AI companies really do?Continue reading on Towards Data Science »
#data-science #artificial-intelligence #machine-learning
http://bit.ly/39ZT6ng
Why is it impossible to understand what AI companies really do?Continue reading on Towards Data Science »
#data-science #artificial-intelligence #machine-learning
Towards Data Science: How To Make Use Of Loops In Python
http://bit.ly/2x6yB9K
Basic Tutorial on For, While and Nested LoopsContinue reading on Towards Data Science »
#programming #data-science #life #artificial-intelligence #python
http://bit.ly/2x6yB9K
Basic Tutorial on For, While and Nested LoopsContinue reading on Towards Data Science »
#programming #data-science #life #artificial-intelligence #python
Towards Data Science: Integrate AI Into Any Application in Minutes
http://bit.ly/3b5DvT8
Use the Spawner API to build ML into any application, instantly.Continue reading on Towards Data Science »
#artificial-intelligence #api #machine-learning #data-science #programming
http://bit.ly/3b5DvT8
Use the Spawner API to build ML into any application, instantly.Continue reading on Towards Data Science »
#artificial-intelligence #api #machine-learning #data-science #programming
Towards Data Science: Simulate cultural interactions using Go and Python
http://bit.ly/2xPU73f
Nothing shouts culture more than visiting relatives during Chinese New Year. From celebrations and customs to decorations and food (it’s…Continue reading on Towards Data Science »
#golang #culture #agent-based-modeling #python #go
http://bit.ly/2xPU73f
Nothing shouts culture more than visiting relatives during Chinese New Year. From celebrations and customs to decorations and food (it’s…Continue reading on Towards Data Science »
#golang #culture #agent-based-modeling #python #go
Towards Data Science: Kaggle Released CORD-19 — an AI Challenge on the COVID-19
http://bit.ly/33rDpTc
Find out the key tasks that AI can help address in this international crisis.Continue reading on Towards Data Science »
#co-vid-19 #artificial-intelligence #kaggle #machine-learning
http://bit.ly/33rDpTc
Find out the key tasks that AI can help address in this international crisis.Continue reading on Towards Data Science »
#co-vid-19 #artificial-intelligence #kaggle #machine-learning