Speeding Up Reinforcement Learning with a New Physics Simulation Engine
In this article, the researchers at Google AI invite all to check the results of their work and learn how to perform a more qualitative measure of Brax’s physics fidelity by training their own policies in the Brax Training Colab.
https://bit.ly/2UO6d8L
In this article, the researchers at Google AI invite all to check the results of their work and learn how to perform a more qualitative measure of Brax’s physics fidelity by training their own policies in the Brax Training Colab.
https://bit.ly/2UO6d8L
Googleblog
Speeding Up Reinforcement Learning with a New Physics Simulation Engine
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting
In this paper by Eric Heiden et al., you'll learn about DiSECt, the first differentiable simulator for cutting soft materials. Through various experiments, the team evaluates the performance of the simulator, demostrating the potential for optimization in robotic cutting of soft materials.
https://bit.ly/3yicDeA
In this paper by Eric Heiden et al., you'll learn about DiSECt, the first differentiable simulator for cutting soft materials. Through various experiments, the team evaluates the performance of the simulator, demostrating the potential for optimization in robotic cutting of soft materials.
https://bit.ly/3yicDeA
Differentiable Cutting Simulator
DiSECt - Differentiable Cutting Simulator
A differentiable simulator for robotic cutting, enabling efficient inference of simulation parameters, and optimization of cutting motions.
Patrick van der Smagt is director of AI research at Volkswagen Group and the lead of its Machine Learning Research Lab in Munich. The lab focuses on probabilistic deep learning for time series modelling, optimal control, robotics, and quantum machine learning. He is also a faculty member of the LMU Graduate School of Systemic Neurosciences and research professor at Eötvös Loránd University Budapest. He is the founding head of a European industry initiative on certification of ethics in AI applications and member of the AI Council of the State of Bavaria.
Patrick van der Smagt also directed a lab as a professor for machine learning and biomimetic robotics at the Technical University of Munich, while leading the machine learning group at the Fortiss research institute. He founded and headed the Assistive Robotics and Bionics Lab at the DLR Oberpfaffenhofen.
He did his PhD and MSc on neural networks in robotics and vision at several universities in Amsterdam. In addition to publishing numerous papers and patents on machine learning, robotics, and motor control, he has won a number of awards, including the 2013 Helmholtz-Association Erwin Schrödinger Award, the 2014 King-Sun Fu Memorial Award, the 2013 Harvard Medical School/MGH Martin Research Prize, and best-paper awards at machine learning and robotics conferences and journals. He was founding chairman of a non-for-profit organization for Assistive Robotics for tetraplegics and is a co-founder of various technology companies.
Did you know about Patrick van der Smagt?
Patrick van der Smagt also directed a lab as a professor for machine learning and biomimetic robotics at the Technical University of Munich, while leading the machine learning group at the Fortiss research institute. He founded and headed the Assistive Robotics and Bionics Lab at the DLR Oberpfaffenhofen.
He did his PhD and MSc on neural networks in robotics and vision at several universities in Amsterdam. In addition to publishing numerous papers and patents on machine learning, robotics, and motor control, he has won a number of awards, including the 2013 Helmholtz-Association Erwin Schrödinger Award, the 2014 King-Sun Fu Memorial Award, the 2013 Harvard Medical School/MGH Martin Research Prize, and best-paper awards at machine learning and robotics conferences and journals. He was founding chairman of a non-for-profit organization for Assistive Robotics for tetraplegics and is a co-founder of various technology companies.
Did you know about Patrick van der Smagt?
Towards Real-World Blind Face Restoration with Generative Facial Prior
Xintao Wang et al. present a GFP-GAN that leverages a pretrained face GAN for blind face restoration. It is incorporated into the face restoration process via novel channel-split spatial feature transform layers, to achieve a good balance of realness and fidelity.
https://bit.ly/3i3B89M
Xintao Wang et al. present a GFP-GAN that leverages a pretrained face GAN for blind face restoration. It is incorporated into the face restoration process via novel channel-split spatial feature transform layers, to achieve a good balance of realness and fidelity.
https://bit.ly/3i3B89M
Five of the Most Common Data Science Mistakes
Let’s look into some of the most common data science mistakes, to learn from these mistakes and help individuals interested in the field grow faster in their career.
1. Analysis Without a Problem/Plan
Analysis needs a direction and plan to proceed. Data science problems begin with a well-defined objective. Sometimes, data scientists jump directly into analysis and modeling without thinking about the problem they’re trying to solve first.
For data scientists, it's important to answer questions that begin with "why" vs. "what." To answer “why” questions, data scientists need to be clear on what problem they want to solve and what they want to achieve when it's solved.
2. Lack of Data Work and Dirty Data
From 60% to 80% of a data scientist’s time is spent preparing and cleaning data. Though this task is the least enjoyable, it’s an important step. Most ML tasks do depend on clean data; otherwise, it's just a garbage in, garbage out work.
Data annotation is the process of labeling data correctly, and it usually happens in the pre-processing step (if we talk supervised machine learning). Data scientists need as much clean, correctly correctly-annotated data as possible, to be able to train accurate machine learning models.
3. Not Focusing on Analysis (Enough)
Data visualization and analysis are the most interesting parts of being a data scientist. In competitions, some data scientists will jump directly to predictive modeling, but this approach won’t solve any machine learning problems accurately in real world scenarios. Data scientists need to dig deeper into the data to unlock insights.
By spending more time with the data analysis, digging into trends and patterns, and asking questions, we can create beautiful stories out of any data.
4. Assuming Correlation Implies Causation
Correlation does not imply causation. Correlation is a statistical technique that refers to how two variables change together (e.g. if there is a change in variable x, then there will be a change in y). When x increases, y increases, which means that x and y are correlated.
But, it doesn’t always mean that x causes y or y causes x. Sometimes, illogical analysis says x causes y because x and y are correlated, but this isn’t always the case.
5. Not Considering All Useful Datasets While Building the Model
There may be multiple datasets out there that will help you solve a problem, and a good data scientist needs to consider many various datasets to get the most out of each. Sometimes insights are found by exploring and linking data from various datasets. It’s a data scientist’s job to create a logical connection between data items, understand each, and present a proper overview of the data while building a model.
Have you made any of these mistakes?
Let’s look into some of the most common data science mistakes, to learn from these mistakes and help individuals interested in the field grow faster in their career.
1. Analysis Without a Problem/Plan
Analysis needs a direction and plan to proceed. Data science problems begin with a well-defined objective. Sometimes, data scientists jump directly into analysis and modeling without thinking about the problem they’re trying to solve first.
For data scientists, it's important to answer questions that begin with "why" vs. "what." To answer “why” questions, data scientists need to be clear on what problem they want to solve and what they want to achieve when it's solved.
2. Lack of Data Work and Dirty Data
From 60% to 80% of a data scientist’s time is spent preparing and cleaning data. Though this task is the least enjoyable, it’s an important step. Most ML tasks do depend on clean data; otherwise, it's just a garbage in, garbage out work.
Data annotation is the process of labeling data correctly, and it usually happens in the pre-processing step (if we talk supervised machine learning). Data scientists need as much clean, correctly correctly-annotated data as possible, to be able to train accurate machine learning models.
3. Not Focusing on Analysis (Enough)
Data visualization and analysis are the most interesting parts of being a data scientist. In competitions, some data scientists will jump directly to predictive modeling, but this approach won’t solve any machine learning problems accurately in real world scenarios. Data scientists need to dig deeper into the data to unlock insights.
By spending more time with the data analysis, digging into trends and patterns, and asking questions, we can create beautiful stories out of any data.
4. Assuming Correlation Implies Causation
Correlation does not imply causation. Correlation is a statistical technique that refers to how two variables change together (e.g. if there is a change in variable x, then there will be a change in y). When x increases, y increases, which means that x and y are correlated.
But, it doesn’t always mean that x causes y or y causes x. Sometimes, illogical analysis says x causes y because x and y are correlated, but this isn’t always the case.
5. Not Considering All Useful Datasets While Building the Model
There may be multiple datasets out there that will help you solve a problem, and a good data scientist needs to consider many various datasets to get the most out of each. Sometimes insights are found by exploring and linking data from various datasets. It’s a data scientist’s job to create a logical connection between data items, understand each, and present a proper overview of the data while building a model.
Have you made any of these mistakes?
Accurate Prediction of Protein Structures and Interactions Using a Three-Track Neural Network
In this research article, the team explores network architectures with a three-track network. It produces accurate structure predictions, solves the challenges of X-ray crystallography and cryo-EM structure modeling, and provides insights into the functions of proteins of unknown structure.
https://bit.ly/3zDzp0M
In this research article, the team explores network architectures with a three-track network. It produces accurate structure predictions, solves the challenges of X-ray crystallography and cryo-EM structure modeling, and provides insights into the functions of proteins of unknown structure.
https://bit.ly/3zDzp0M
Science
Accurate prediction of protein structures and interactions using a three-track neural network
DeepMind presented remarkably accurate predictions at the recent CASP14 protein structure prediction assessment conference. We explored network architectures incorporating related ideas and obtained the best performance with a three-track network in which…
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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.
A best channel for AI and Python projects with source code.
Join to channel 🌺🌺
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Join to channel 🌺🌺
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Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho
Admin: @HusseinSheikho || @Hussein_Sheikho
TextOCR
TextOCR is a dataset to benchmark text recognition on arbitrary shaped scene-text that features 1M high-quality word annotations on TextVQA images. It allows data scientists to more easily apply end-to-end reasoning to downstream tasks, such as visual question answering or image captioning.
https://bit.ly/37890MA
TextOCR is a dataset to benchmark text recognition on arbitrary shaped scene-text that features 1M high-quality word annotations on TextVQA images. It allows data scientists to more easily apply end-to-end reasoning to downstream tasks, such as visual question answering or image captioning.
https://bit.ly/37890MA
9 августа стартует онлайн-интенсив «Machine Learning. Введение в регрессионный анализ».
За 3 недели разработчики, аналитики и другие специалисты со знанием синтаксиса Python научатся решать задачи по прогнозированию с помощью трех методов и построят первую ML-модель.
https://bit.ly/2UYMzHb
За 3 недели разработчики, аналитики и другие специалисты со знанием синтаксиса Python научатся решать задачи по прогнозированию с помощью трех методов и построят первую ML-модель.
https://bit.ly/2UYMzHb
Why Data Science Is the Future?
Data scientists have been on the radar generating tons of buzz lately. They are badly needed on the market, and the profession itself has immense potential, both money- and contribution-wise. As a data scientist, you can change the world for real, though all you do is work with data. Amazing!
Here are six reasons why data science is the ideal career for the future.
1. Companies Struggle to Manage Their Data
Businesses now have the tools to collect tons of data, but who's going to process and analyze it? Here's when data scientists enter the picture!
2. New Data Privacy Regulations Increase the Need for Data Scientists
In May 2018, the General Data Protection Regulation (GDPR) took effect for countries in the European Union. In 2020, California enacted a similar regulation for data privacy. The GDPR increased the reliance companies have on data scientists, because now they have to focus more on managing and storing their data responsibly.
3. Data Science Is Still Evolving
Careers without growth potential stay stagnant, usually indicating that jobs within those respective fields must drastically change to remain relevant. Data science appears to have abundant opportunities to evolve over the next decade or so. Since it shows no signs of slowing down, that’s good news for people wanting to enter the field.
4. Data Scientists Have In-Demand Skills
Research shows 94 percent of data science graduates have got jobs in the field since 2011. One of the indicators that data science is well-suited for the future is the dramatic increase in data science job postings. Statistics from Indeed.com show a steady increase in the number of data science jobs listed over the years.
5. A Staggering Amount of Data Growth
People generate data daily, but most probably don’t even think about it. According to a study about current and future data growth, 5 billion consumers take part in data interactions daily, and that number will increase to 6 billion by 2025, representing three-quarters of the world’s population.
6. High Likelihood of Career Advancement Opportunities
LinkedIn has recently picked data scientist as its most promising career. One of the reasons it got the top spot was that the average salary in the role — $130,000. LinkedIn’s study also considered the fact that data scientists could get easily promoted, giving the role a career advancement score of nine out of 10.
Data scientists have been on the radar generating tons of buzz lately. They are badly needed on the market, and the profession itself has immense potential, both money- and contribution-wise. As a data scientist, you can change the world for real, though all you do is work with data. Amazing!
Here are six reasons why data science is the ideal career for the future.
1. Companies Struggle to Manage Their Data
Businesses now have the tools to collect tons of data, but who's going to process and analyze it? Here's when data scientists enter the picture!
2. New Data Privacy Regulations Increase the Need for Data Scientists
In May 2018, the General Data Protection Regulation (GDPR) took effect for countries in the European Union. In 2020, California enacted a similar regulation for data privacy. The GDPR increased the reliance companies have on data scientists, because now they have to focus more on managing and storing their data responsibly.
3. Data Science Is Still Evolving
Careers without growth potential stay stagnant, usually indicating that jobs within those respective fields must drastically change to remain relevant. Data science appears to have abundant opportunities to evolve over the next decade or so. Since it shows no signs of slowing down, that’s good news for people wanting to enter the field.
4. Data Scientists Have In-Demand Skills
Research shows 94 percent of data science graduates have got jobs in the field since 2011. One of the indicators that data science is well-suited for the future is the dramatic increase in data science job postings. Statistics from Indeed.com show a steady increase in the number of data science jobs listed over the years.
5. A Staggering Amount of Data Growth
People generate data daily, but most probably don’t even think about it. According to a study about current and future data growth, 5 billion consumers take part in data interactions daily, and that number will increase to 6 billion by 2025, representing three-quarters of the world’s population.
6. High Likelihood of Career Advancement Opportunities
LinkedIn has recently picked data scientist as its most promising career. One of the reasons it got the top spot was that the average salary in the role — $130,000. LinkedIn’s study also considered the fact that data scientists could get easily promoted, giving the role a career advancement score of nine out of 10.
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#DataPhoenix #DataScience #MachineLearning #ArtificialIntelligence #AI #ML #Data #Digest #Newsletter
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.
Project CodeNet
Project CodeNet provides the AI-for-Code research community with a large scale, diverse, and high quality curated dataset to drive innovation in AI techniques. Check out their GitHub page to find the dataset you need.
https://bit.ly/3zRd28l
Project CodeNet provides the AI-for-Code research community with a large scale, diverse, and high quality curated dataset to drive innovation in AI techniques. Check out their GitHub page to find the dataset you need.
https://bit.ly/3zRd28l
GitHub
GitHub - IBM/Project_CodeNet: This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX
This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX - GitHub - IBM/Project_CodeNet: This repository is to support contributions for tools for the Pro...
Hello friends! Data Phoenix team know that some of you are looking for job opportunities now. As your caring friend, we've put together a list of 11 amazing positions available this week Let us know what you think!
1) Machine Learning Engineer, Shelf
https://bit.ly/3icOAIq
2) Data Scientist, Shelf
https://bit.ly/3lkR0GS
3) Experienced ML Engineer, Lun
https://bit.ly/3ffbfCh
4) Machine learning architect, SoftServe
https://bit.ly/2Vn1hY9
5) Lead MLOps engineer, SoftServe
https://bit.ly/3zMqFWc
6) Product Data Scientist, Snap
https://bit.ly/3if9QgE
7) Senior Machine Learning Engineer, Sigma Software
https://bit.ly/2Vjfh5x
8) Senior Machine Learning Scientist, Upwork
https://bit.ly/3C0ozE4
9) Product Manager, Machine Learning, Grammarly
https://bit.ly/3f9k93V
10) Deep Learning Research Engineer (RnD), Reface
https://bit.ly/3rIspNr
11) Computer Vision Research Engineer, SQUAD
https://bit.ly/3rGkHUe
Sourcing top tech talent is always a challenge... Yeap, we've all been there!
Data Phoenix can offer some help. Did you know that you can publish your job opportunities in our digest for free? If you have open positions in Data Science, Machine Learning, Data Engineering, MLOps, etc., don't hesitate to contact us!🤗
Contact email📥: editor@dataphoenix.info
1) Machine Learning Engineer, Shelf
https://bit.ly/3icOAIq
2) Data Scientist, Shelf
https://bit.ly/3lkR0GS
3) Experienced ML Engineer, Lun
https://bit.ly/3ffbfCh
4) Machine learning architect, SoftServe
https://bit.ly/2Vn1hY9
5) Lead MLOps engineer, SoftServe
https://bit.ly/3zMqFWc
6) Product Data Scientist, Snap
https://bit.ly/3if9QgE
7) Senior Machine Learning Engineer, Sigma Software
https://bit.ly/2Vjfh5x
8) Senior Machine Learning Scientist, Upwork
https://bit.ly/3C0ozE4
9) Product Manager, Machine Learning, Grammarly
https://bit.ly/3f9k93V
10) Deep Learning Research Engineer (RnD), Reface
https://bit.ly/3rIspNr
11) Computer Vision Research Engineer, SQUAD
https://bit.ly/3rGkHUe
Sourcing top tech talent is always a challenge... Yeap, we've all been there!
Data Phoenix can offer some help. Did you know that you can publish your job opportunities in our digest for free? If you have open positions in Data Science, Machine Learning, Data Engineering, MLOps, etc., don't hesitate to contact us!🤗
Contact email📥: editor@dataphoenix.info
📌Customer Support Automation Platform at Uber
In this comprehensive article, you'll learn how Uber manages customer support by using it AI/ML-powered automation platform. Dig in to explore the technological belly of their procedures, architecture, and infrastructure for democratizing the customer support policies
https://ubr.to/3j4Qb2h
In this comprehensive article, you'll learn how Uber manages customer support by using it AI/ML-powered automation platform. Dig in to explore the technological belly of their procedures, architecture, and infrastructure for democratizing the customer support policies
https://ubr.to/3j4Qb2h
Uber Engineering Blog
Customer Support Automation Platform at Uber
High Level Overview of the Problem Introduction If you’ve used any online/digital service, chances are that you are familiar with what a typical customer se
What to read during the weekend?🧐
Handbook of Artificial Intelligence!
The Handbook of Artificial Intelligence, Volume I focuses entirely on the progress that has been happening in the field, as well as on various applications of AI. The book elaborates on AI, AI in the literature, problem representation, search methods, and sample search programs. The text then ponders on representation of knowledge, including survey of representation techniques and representation schemes. The manunoscript explores understanding natural languages, as well as machine translation, grammar, parsing, test generation, and natural language processing systems. The book also takes a look at understanding of spoken language, including systems architecture and the ARPA SUR projects. The text is a valuable source of information for computer science experts and researchers interested in pursuing further research in artificial intelligence.
Handbook of Artificial Intelligence!
The Handbook of Artificial Intelligence, Volume I focuses entirely on the progress that has been happening in the field, as well as on various applications of AI. The book elaborates on AI, AI in the literature, problem representation, search methods, and sample search programs. The text then ponders on representation of knowledge, including survey of representation techniques and representation schemes. The manunoscript explores understanding natural languages, as well as machine translation, grammar, parsing, test generation, and natural language processing systems. The book also takes a look at understanding of spoken language, including systems architecture and the ARPA SUR projects. The text is a valuable source of information for computer science experts and researchers interested in pursuing further research in artificial intelligence.