📌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 ...
⚡️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) Data Engineer at Wikimedia Foundation (Remote)
https://bit.ly/3dj3jOV
2) Staff Data Scientist at Instacart (San Francisco, CA - Remote)
https://bit.ly/3pnb8c5
3) Sr. Data Engineer at HashiCorp (United States - Remote)
https://bit.ly/3EoUIWS
4) Product Data Scientist at Mozilla (Remote US, Remote Canada)
https://bit.ly/3IjModu
5) Senior Data Engineer at Twitch (United States - Remote)
https://bit.ly/3oow972
📌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) Data Engineer at Wikimedia Foundation (Remote)
https://bit.ly/3dj3jOV
2) Staff Data Scientist at Instacart (San Francisco, CA - Remote)
https://bit.ly/3pnb8c5
3) Sr. Data Engineer at HashiCorp (United States - Remote)
https://bit.ly/3EoUIWS
4) Product Data Scientist at Mozilla (Remote US, Remote Canada)
https://bit.ly/3IjModu
5) Senior Data Engineer at Twitch (United States - Remote)
https://bit.ly/3oow972
📌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!
💡Parameter Exploration at Lyft
In this article, you'll learn about parameter exploration practices at Lyft, including the ups and downs of the methods they agreed on, to drive data-driven decision making at scale.
https://lft.to/3omq62E
In this article, you'll learn about parameter exploration practices at Lyft, including the ups and downs of the methods they agreed on, to drive data-driven decision making at scale.
https://lft.to/3omq62E
Medium
Parameter Exploration at Lyft
What is Parameter Exploration
🔥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/3xT9CCe
But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3xT9CCe
📚TorchGeo: deep learning with geospatial data
TorchGeo is a Python library for integrating geospatial data into the PyTorch deep learning ecosystem that enables deep learning for remote sensing applications.
https://bit.ly/3oroG7e
TorchGeo is a Python library for integrating geospatial data into the PyTorch deep learning ecosystem that enables deep learning for remote sensing applications.
https://bit.ly/3oroG7e
⚡️Data Phoenix team wishes you an amazing week! Today, we want to present to you Dr. Gregory Piatetsky-Shapiro, a fabled president of KDnuggets. Gregory is famous as an expert in Business Analytics, Data Mining, and Data Science, and one of the biggest influencers in the fields of data and AI.
He's a co-founder of Knowledge Discovery and Data mining conferences, co-founder, and past chair of SIGKDD, a professional organization for Knowledge Discovery and Data Mining. Gregory has over 60 publications and edited several books and collections on data mining and knowledge discovery.
He's led data mining and consulting groups at GTE Laboratories, Knowledge Stream Partners, and Xchange. His experience in developing solutions for CRM, customer attrition, cross-sell, and segmentation for some of the leading banks, insurance, and telecommunications companies is truly unrivaled. He's also worked on data analysis of the clinical trials, microarray, and proteomic data.
https://bit.ly/3dpskrS
He's a co-founder of Knowledge Discovery and Data mining conferences, co-founder, and past chair of SIGKDD, a professional organization for Knowledge Discovery and Data Mining. Gregory has over 60 publications and edited several books and collections on data mining and knowledge discovery.
He's led data mining and consulting groups at GTE Laboratories, Knowledge Stream Partners, and Xchange. His experience in developing solutions for CRM, customer attrition, cross-sell, and segmentation for some of the leading banks, insurance, and telecommunications companies is truly unrivaled. He's also worked on data analysis of the clinical trials, microarray, and proteomic data.
https://bit.ly/3dpskrS
📌Root Causing Data Failures
Handling data is not an easy task. In this post, you'll find out how Anomalo, a data quality platform, can help you find the root cause of data quality issues automatically.
https://bit.ly/3DtP7wY
Handling data is not an easy task. In this post, you'll find out how Anomalo, a data quality platform, can help you find the root cause of data quality issues automatically.
https://bit.ly/3DtP7wY
Anomalo
Anomalo | Root Causing Data Failures
Finding the root cause of data quality issues automatically.
📚LILA: Language-Informed Latent Actions
Language-Informed Latent Actions (LILA) is a framework for learning natural language interfaces in the context of human-robot collaboration under the shared autonomy paradigm.
https://bit.ly/31yRDVE
Language-Informed Latent Actions (LILA) is a framework for learning natural language interfaces in the context of human-robot collaboration under the shared autonomy paradigm.
https://bit.ly/31yRDVE
💡Training an object detector from scratch in PyTorch
In this tutorial, you'll learn how to train a custom object detector from scratch using PyTorch. Note that this lesson is part 2 of a 3-part series on advanced PyTorch techniques.
https://bit.ly/3EBIbQ0
In this tutorial, you'll learn how to train a custom object detector from scratch using PyTorch. Note that this lesson is part 2 of a 3-part series on advanced PyTorch techniques.
https://bit.ly/3EBIbQ0
PyImageSearch
Training an object detector from scratch in PyTorch - PyImageSearch
Learn to train an object detector using PyTorch and Python. The perfect guide for someone looking to try PyTorch for the first time or new to object detection.
📚Machine-in-the-Loop Rewriting for Creative Image Captioning
In this paper, the authors propose a rewriting model that modifies specified spans of text within the user's original draft to introduce denoscriptive and figurative elements locally in the text.
https://bit.ly/31QdyYI
In this paper, the authors propose a rewriting model that modifies specified spans of text within the user's original draft to introduce denoscriptive and figurative elements locally in the text.
https://bit.ly/31QdyYI
🔥Hello friends!
We hope that your week is going well so 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/3lOxZfJ
We hope that your week is going well so 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/3lOxZfJ
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.
📌Orchestrate a Data Science Project in Python With Prefect
This step-by-step guide will teach you how you can use Prefect to optimize your DS workflow in a few lines of Python code, to increase efficiency in the long run.
https://bit.ly/33jdFwv
This step-by-step guide will teach you how you can use Prefect to optimize your DS workflow in a few lines of Python code, to increase efficiency in the long run.
https://bit.ly/33jdFwv
Medium
Orchestrate a Data Science Project in Python With Prefect
Optimize Your Data Science Workflow in a Few Lines of Code
⚡️Hello everyone!
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/3dBnO9R
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/3dBnO9R
Data Phoenix
Data Phoenix Digest - ISSUE 35
AI trends report, AWS re:Invent 2021, how to handle ML model drift in production, XGBoost vs LightGBM, DCGAN for Fashion-MNIST, Adobe Research at ICCV 2021, NVIDIA TAO, HyperStyle, EditGAN, Florence, videos, books, jobs, and more ...
⚡️Hello everyone!
We hope that your weekend is going great!
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 CV Engineer at SoftServe (Odesa, Lviv, Kyiv, Remote)
https://bit.ly/3GqJdii
2) Summer Internship, Data Scientist at Spotify (London)
https://bit.ly/3pOC9p2
3) Machine Learning Engineer at Lyft (Kyiv)
https://bit.ly/31QBACS
4) Sr. Data Scientist at GoPro (San Mateo, Carlsbad)
https://bit.ly/31LhASc
5) Data Scientist at Snap (Odesa, Kyiv, Remote)
https://bit.ly/3lUQgb2
For other available positions click on the link 👉🏻
https://bit.ly/3yeuW5v
We hope that your weekend is going great!
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 CV Engineer at SoftServe (Odesa, Lviv, Kyiv, Remote)
https://bit.ly/3GqJdii
2) Summer Internship, Data Scientist at Spotify (London)
https://bit.ly/3pOC9p2
3) Machine Learning Engineer at Lyft (Kyiv)
https://bit.ly/31QBACS
4) Sr. Data Scientist at GoPro (San Mateo, Carlsbad)
https://bit.ly/31LhASc
5) Data Scientist at Snap (Odesa, Kyiv, Remote)
https://bit.ly/3lUQgb2
For other available positions click on the link 👉🏻
https://bit.ly/3yeuW5v
📚How to handle ML model drift in production
In this Q&A, you'll learn what to do if you have a model in production, and the data is drifting. Eight awesome and detailed tips to help you solve the problem in the bud.
https://bit.ly/3DJRX1i
In this Q&A, you'll learn what to do if you have a model in production, and the data is drifting. Eight awesome and detailed tips to help you solve the problem in the bud.
https://bit.ly/3DJRX1i
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.
🔥Hello friends!
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/3pNsMpA
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/3pNsMpA
📌HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
In this paper, Yuval Alaluf et al. propose HyperStyle, a hypernetwork that learns to modulate StyleGAN's weights to faithfully express a given image in editable regions of the latent space.
https://bit.ly/3oLRhUE
In this paper, Yuval Alaluf et al. propose HyperStyle, a hypernetwork that learns to modulate StyleGAN's weights to faithfully express a given image in editable regions of the latent space.
https://bit.ly/3oLRhUE