📌Solving Math Word Problems
Learn about a system trained by the OpenAI team that solves grade school math problems with twice the accuracy of a fine-tuned GPT-3 model. It solves ~90% as many problems as real kids.
https://bit.ly/30KA5oX
Learn about a system trained by the OpenAI team that solves grade school math problems with twice the accuracy of a fine-tuned GPT-3 model. It solves ~90% as many problems as real kids.
https://bit.ly/30KA5oX
Openai
Solving math word problems
We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% as many problems as real kids: a small sample of 9-12 year olds scored 60% on a test from our dataset, while our…
🔥Data Phoenix wishes you lovely Sunday! We hope is going great and you are ready for the upcoming week!
But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3HQC4cG
But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3HQC4cG
📚EditGAN: High-Precision Semantic Image Editing
EditGAN is a novel method for high quality, high precision semantic image editing, allowing users to edit images by modifying their highly detailed part segmentation masks.
https://bit.ly/3cvlAIu
EditGAN is a novel method for high quality, high precision semantic image editing, allowing users to edit images by modifying their highly detailed part segmentation masks.
https://bit.ly/3cvlAIu
⚡️Hello everyone! How are you feeling about starting a new week? Today, let us introduce you Bernard Marr — a world-renowned futurist, influencer, and thought leader in business and technology. Passionate about using technology for the good of humanity, he shares his vision with over 2 million social media followers and 1 million newsletter subscribers. He was ranked by LinkedIn as one of the top 5 business influencers in the world and the No.1 influencer in the UK.
Today, Bernard Marr is one of the world’s most highly respected experts when it comes to future trends, strategy, business performance, digital transformation, and the intelligent use of data and AI in business. He has worked with and advised many of the world’s best-known organizations, such as Amazon, Google, Microsoft, Astra Zeneca, The Bank of England, BP, NVIDIA, Cisco, DHL, IBM, HPE, Ericsson, Jaguar Land Rover, Mars, The Ministry of Defense, NATO, The Home Office, NHS, Oracle, T-Mobile, Toyota, The Royal Air Force, Shell, The United Nations, Walgreens Alliance Boots, and Walmart.
He is the author of 20 books and hundreds of high profile reports and articles, including the international best-sellers. His books have been translated into over 20 languages and have been repeatedly featured as the Amazon No.1 bestselling book. He has earned the CMI Management Book of the Year award, the Axiom book award, and the WHSmith best business book award.
Today, Bernard also enjoys teaching for Oxford University, Warwick Business School, the Irish Management Institute, and ICAEW. On top of that, Bernard serves as a non-executive director on the board of businesses and has a seat on the dean’s council for Lancaster University Management School.
https://bit.ly/3qXZAOF
Today, Bernard Marr is one of the world’s most highly respected experts when it comes to future trends, strategy, business performance, digital transformation, and the intelligent use of data and AI in business. He has worked with and advised many of the world’s best-known organizations, such as Amazon, Google, Microsoft, Astra Zeneca, The Bank of England, BP, NVIDIA, Cisco, DHL, IBM, HPE, Ericsson, Jaguar Land Rover, Mars, The Ministry of Defense, NATO, The Home Office, NHS, Oracle, T-Mobile, Toyota, The Royal Air Force, Shell, The United Nations, Walgreens Alliance Boots, and Walmart.
He is the author of 20 books and hundreds of high profile reports and articles, including the international best-sellers. His books have been translated into over 20 languages and have been repeatedly featured as the Amazon No.1 bestselling book. He has earned the CMI Management Book of the Year award, the Axiom book award, and the WHSmith best business book award.
Today, Bernard also enjoys teaching for Oxford University, Warwick Business School, the Irish Management Institute, and ICAEW. On top of that, Bernard serves as a non-executive director on the board of businesses and has a seat on the dean’s council for Lancaster University Management School.
https://bit.ly/3qXZAOF
Bernard Marr
Bernard Marr | Future . Business . Success
Influencer
Bernard Marr is one of the world’s most successful social media influencers at the intersection of business and technology. View More
Keynote Speaker
Bernard Marr is a popular keynote choice because of his vast practical experience…
Bernard Marr is one of the world’s most successful social media influencers at the intersection of business and technology. View More
Keynote Speaker
Bernard Marr is a popular keynote choice because of his vast practical experience…
💡Hugging Face Transformer Inference Under 1 Millisecond Latency
Hugging Face has released “Infinity’’, a server product that performs inference at enterprise scale. It can perform Transformer inference at 1 millisecond latency on the GPU.
https://bit.ly/3xb5Qnj
Hugging Face has released “Infinity’’, a server product that performs inference at enterprise scale. It can perform Transformer inference at 1 millisecond latency on the GPU.
https://bit.ly/3xb5Qnj
Medium
Hugging Face Transformer Inference Under 1 Millisecond Latency
Go to production with Microsoft and Nvidia open source tooling
📚 On the Frequency Bias of Generative Models
In this paper, the authors provide insights on measures against high-frequency artifacts and what makes them effective, with focus on a frequency bias.
https://bit.ly/3l1m51k
In this paper, the authors provide insights on measures against high-frequency artifacts and what makes them effective, with focus on a frequency bias.
https://bit.ly/3l1m51k
📌Design patterns for Machine Learning Pipelines
ML pipeline design keeps evolving. In this article, you'll learn how these design patterns changed, what processes they went through, and their future direction.
https://bit.ly/32nIRKp
ML pipeline design keeps evolving. In this article, you'll learn how these design patterns changed, what processes they went through, and their future direction.
https://bit.ly/32nIRKp
KDnuggets
Design Patterns for Machine Learning Pipelines
ML pipeline design has undergone several evolutions in the past decade with advances in memory and processor performance, storage systems, and the increasing scale of data sets. We describe how these design patterns changed, what processes they went through…
📚DScribe: Library of Denoscriptors for Machine Learning in Materials Science
DScribe is a software package for ML that provides "denoscriptors" for atomistic materials simulations, to accelerate and simplify the application of ML for atomistic property prediction.
https://bit.ly/3cKVMIe
DScribe is a software package for ML that provides "denoscriptors" for atomistic materials simulations, to accelerate and simplify the application of ML for atomistic property prediction.
https://bit.ly/3cKVMIe
🔥Hello friends! We hope that your week is going well this far. Data Phoenix team wants to remind you about our weekly newsletter which is coming, as always, tomorrow! Fill in your email and get instant access to all the AI/ML goodies in one go. Looking forward to having you as one of our amazing subscribers!
https://bit.ly/3oYT6g7
https://bit.ly/3oYT6g7
Data Phoenix
Data Phoenix is your best friend in learning and growing in the data world! We publish digest, organize events and help expand the frontiers of your knowledge in ML, CV, NLP, and other aspects of AI.
💡ORDAINED: The Python Project Template
Creating Python packages can be annoying. Learn about a project boilerplate template for Python packages that can be used instead of copying a directory tree and doing find and replace.
https://bit.ly/3xsJGxg'
Creating Python packages can be annoying. Learn about a project boilerplate template for Python packages that can be used instead of copying a directory tree and doing find and replace.
https://bit.ly/3xsJGxg'
TODO ...
🙏 ORDAINED: The Python Project Template
Creating a Cookiecutter template for generating opinionated Python project boilerplate.
📌How to Handle ML Model Drift in Production
Data drift is an everyday challenge in Data Science and Machine Learning. In this introductory overview, you'll learn about major steps you can take to handle it more efficiently.
https://bit.ly/3E12yFT
Data drift is an everyday challenge in Data Science and Machine Learning. In this introductory overview, you'll learn about major steps you can take to handle it more efficiently.
https://bit.ly/3E12yFT
Evidentlyai
"My data drifted. What's next?" How to handle ML model drift in production.
What can you do once you detect data drift for a production ML model? Here is an introductory overview of the possible steps.
⚡️Data Phoenix team is ready to present our weekly issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3CWZlpB
https://bit.ly/3CWZlpB
Data Phoenix
Data Phoenix Digest - ISSUE 33
Instant access to OpenAI’s API. The perks of AI and automation. What Is the difference between outlier detection and data drift detection? PyTorch C++ API for use on mobile platforms. Compositional transformers for scene generation. CleanRL, Causal-BALD,…
We hope your Sunday is going great and you are ready for the upcoming week!
But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3nZuC6V
But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3nZuC6V
⚡️Hello friends!
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) Senior/Middle CV/ML Engineer at Apostera (Odesa, Kyiv, Remote)
https://bit.ly/3d0WOQs
2) Junior CV/ML Engineer at Apostera (Odesa, Kyiv, Remote)
https://bit.ly/3d0WOQs
3) Senior Data Scientist/ML Engineer at Xenoss (Odesa, Kyiv, Remote)
https://bit.ly/3I0pszG
4) Middle+/Senior Data scientist at AUTODOC (Odesa)
https://bit.ly/3IaIZxw
5) Machine Learning Architect at SoftServe (Odesa, Kyiv, Remote)
https://bit.ly/3lcoPcE
Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) Senior/Middle CV/ML Engineer at Apostera (Odesa, Kyiv, Remote)
https://bit.ly/3d0WOQs
2) Junior CV/ML Engineer at Apostera (Odesa, Kyiv, Remote)
https://bit.ly/3d0WOQs
3) Senior Data Scientist/ML Engineer at Xenoss (Odesa, Kyiv, Remote)
https://bit.ly/3I0pszG
4) Middle+/Senior Data scientist at AUTODOC (Odesa)
https://bit.ly/3IaIZxw
5) Machine Learning Architect at SoftServe (Odesa, Kyiv, Remote)
https://bit.ly/3lcoPcE
Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
⚡️Hello friends! Let’s start our Monday with Francesca Lazzeri, an experienced data scientist, economist, and machine learning practitioner with over 15 years of experience in academic research.
Francesca is the author of “Machine Learning for Time Series Forecasting with Python”. She has published numerous articles and papers in technology journals.
In addition to that, she is a Professor of Machine Learning at Columbia University and a Principal Data Scientist Manager at Microsoft, where she leads a team of data scientists focusing on the data science and machine learning applications in such use cases as customer retention, fraud detection, and experimentation.
She was a research fellow at Harvard University in the Technology and Operations Management Unit. Also, she is an Advisory Board Member for the European Union and for the WiDS initiative, a machine learning mentor at the Massachusetts Institute of Technology.
Let us know, who should be next?
https://bit.ly/3rjcUNJ
Francesca is the author of “Machine Learning for Time Series Forecasting with Python”. She has published numerous articles and papers in technology journals.
In addition to that, she is a Professor of Machine Learning at Columbia University and a Principal Data Scientist Manager at Microsoft, where she leads a team of data scientists focusing on the data science and machine learning applications in such use cases as customer retention, fraud detection, and experimentation.
She was a research fellow at Harvard University in the Technology and Operations Management Unit. Also, she is an Advisory Board Member for the European Union and for the WiDS initiative, a machine learning mentor at the Massachusetts Institute of Technology.
Let us know, who should be next?
https://bit.ly/3rjcUNJ
💡Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
In this paper, Liming Jiang et al. introduce a novel strategy called Adaptive Pseudo Augmentation (APA) that encourages healthy competition between the generator and the discriminator.
https://bit.ly/317tbdL
In this paper, Liming Jiang et al. introduce a novel strategy called Adaptive Pseudo Augmentation (APA) that encourages healthy competition between the generator and the discriminator.
https://bit.ly/317tbdL
📌Get Started: DCGAN for Fashion-MNIST
In this tutorial for beginners, you'll implement a typical DCGAN with TensorFlow 2 and Keras, based on a basic GAN paper and a Colab notebook.
https://bit.ly/3xGGxK3
In this tutorial for beginners, you'll implement a typical DCGAN with TensorFlow 2 and Keras, based on a basic GAN paper and a Colab notebook.
https://bit.ly/3xGGxK3
PyImageSearch
Get Started: DCGAN for Fashion-MNIST - PyImageSearch
Get started learning GANs by implementing a DCGAN with TensorFlow 2 / Keras to generate Fashion-MNIST like gray-scale images.
💡Compositional Transformers for Scene Generation
The authors introduce the GANformer2 model, an iterative object-oriented transformer that is capable of highly efficient generative modeling.
https://bit.ly/2ZM0gvr
The authors introduce the GANformer2 model, an iterative object-oriented transformer that is capable of highly efficient generative modeling.
https://bit.ly/2ZM0gvr
📌Using CNN for financial time series prediction
In this tutorial, you'll learn how a CNN model can be built for prediction in financial time series, from creating 2D convolutional layers to monitoring the performance of model training.
https://bit.ly/3divnC5
In this tutorial, you'll learn how a CNN model can be built for prediction in financial time series, from creating 2D convolutional layers to monitoring the performance of model training.
https://bit.ly/3divnC5
Machine Learning Mastery
Using CNN for financial time series prediction - Machine Learning Mastery
Convolutional neural networks have their roots in image processing. It was first published in LeNet to recognize the MNIST handwritten digits. However, convolutional neural networks are not limited to handling images.
In this tutorial, we are going to…
In this tutorial, we are going to…
🔥Hi guys! We hope that you had an amazing week!
Data Phoenix team wants to remind you about our weekly newsletter which is coming really soon! Fill in your email so you don't miss a thing and get instant access to all the AI/ML goodies in one go. Looking forward to having you as one of our amazing subscribers!
https://bit.ly/31tfar6
Data Phoenix team wants to remind you about our weekly newsletter which is coming really soon! Fill in your email so you don't miss a thing and get instant access to all the AI/ML goodies in one go. Looking forward to having you as one of our amazing subscribers!
https://bit.ly/31tfar6
Data Phoenix
Data Phoenix is your best friend in learning and growing in the data world! We publish digest, organize events and help expand the frontiers of your knowledge in ML, CV, NLP, and other aspects of AI.
⚡️New issue of the digest is LIVE!
Data Phoenix team is ready to present our weekly newsletter! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/31uwxHK
Data Phoenix team is ready to present our weekly newsletter! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/31uwxHK
Data Phoenix
Data Phoenix Digest - ISSUE 34
The launch of PyTorch Live, training a DCGAN in PyTorch, text classification with BERT in PyTorch, transformers from scratch, Machine-in-the-Loop rewriting for creative image captioning, TorchGeo, LILA, OpenPrompt, WaveFake, books, jobs, and more ...