Train your dragons! by Shilpa Karkera
•
2 likes•586 views
This document discusses training a dragon to aim and fire at moving objects using machine learning algorithms. It describes using techniques like convolutional neural networks, classification, and reinforcement learning like actor-critic algorithms to teach the dragon to accurately detect targets and improve its aiming and firing abilities over time as it learns. The document is about training the dragon to evolve its skills and make better decisions.
Report
Share
Report
Share
Download to read offline
Recommended
Jki Introduction 2010
Johnson Knight International is a global executive search and selection firm focused on the energy and natural resources sector. They have offices in over 28 locations worldwide and provide services including executive search, interim management, and assessment testing. Their client base includes large oil and gas companies and they specialize in sourcing senior level candidates earning over €120k.
Eecs 441 Uber Preso
Uber provides ride-hailing services through apps like UberX as well as services like UberEATS and UberPUPPIES for food and pet delivery. In the first half of 2015, Uber had gross bookings of $3.63 billion but net revenue of only $663.2 million due to high expenses of $987.2 million, resulting in a loss of $324 million. Founded in 2009, Uber has expanded globally but faced regulatory pressure in major markets and now focuses on growth in India, China, and Southeast Asia, competing with companies like Lyft, Ola, Didi, and Grab while pursuing an ultimate user-experience through competitive pricing and additional services.
Programming for Financial Strategies
This document summarizes an internship at Lama Capital Management focused on programming financial strategies. The internship involved:
1) Using JavaScript to plot return vs frequency curves from calculations in Python to link frontend and backend websites.
2) Scraping historical data from websites using Python scripts to develop trading strategies and test them on daily data over 15 days.
3) Implementing strategies like ACD and RSI in Python, including defining triggers and optimizing parameters like ATR, entry/exit points, and profit booking levels to maximize win rates.
4) Programming in Python to retrieve real-time market data, generate buy/sell signals, and place live trades through APIs.
TransmogrifAI - Automate Machine Learning Workflow with the power of Scala an...
TransmogrifAI - Automate Machine Learning Workflow with the power of Scala and Spark at massive scale.
Scala.IO, CPE - France
Automate ml workflow_transmogrif_ai-_chetan_khatri_berlin-scala
TransmogrifAI is an open source library for automating machine learning workflows built on Scala and Spark. It helps automate tasks like feature engineering, selection, model selection, and hyperparameter tuning. This reduces machine learning development time from months to hours. TransmogrifAI enforces type safety and modularity to build reusable, production-ready models. It was created by Salesforce to make machine learning more accessible to developers without a PhD in machine learning.
How we learned to rank search results big data meetup
Big Data Meetup Argentina Presentation About an implementation of Learning to Rank @OLXGroup Argentina
Introduction of Artificial Intelligence and Machine Learning
A Workshop to introduce Artificial Intelligence and Machine Learning for beginners. It starts with basics , terminologies and concepts for machine learning, compares with deep learning and artificial Intelligence. Highlights the ML and AI offerings like Jupyter Notebook, Azure ML , Amazon Sagemaker, Tensorflow etc.
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & PyTorch with B...
We all know what they say – the bigger the data, the better. But when the data gets really big, how do you mine it and what deep learning framework to use? This talk will survey, with a developer’s perspective, three of the most popular deep learning frameworks—TensorFlow, Keras, and PyTorch—as well as when to use their distributed implementations.
We’ll compare code samples from each framework and discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data) as well as help you answer questions such as:
As a developer how do I pick the right deep learning framework?
Do I want to develop my own model or should I employ an existing one?
How do I strike a trade-off between productivity and control through low-level APIs?
What language should I choose?
In this session, we will explore how to build a deep learning application with Tensorflow, Keras, or PyTorch in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you.
Recommended
Jki Introduction 2010
Johnson Knight International is a global executive search and selection firm focused on the energy and natural resources sector. They have offices in over 28 locations worldwide and provide services including executive search, interim management, and assessment testing. Their client base includes large oil and gas companies and they specialize in sourcing senior level candidates earning over €120k.
Eecs 441 Uber Preso
Uber provides ride-hailing services through apps like UberX as well as services like UberEATS and UberPUPPIES for food and pet delivery. In the first half of 2015, Uber had gross bookings of $3.63 billion but net revenue of only $663.2 million due to high expenses of $987.2 million, resulting in a loss of $324 million. Founded in 2009, Uber has expanded globally but faced regulatory pressure in major markets and now focuses on growth in India, China, and Southeast Asia, competing with companies like Lyft, Ola, Didi, and Grab while pursuing an ultimate user-experience through competitive pricing and additional services.
Programming for Financial Strategies
This document summarizes an internship at Lama Capital Management focused on programming financial strategies. The internship involved:
1) Using JavaScript to plot return vs frequency curves from calculations in Python to link frontend and backend websites.
2) Scraping historical data from websites using Python scripts to develop trading strategies and test them on daily data over 15 days.
3) Implementing strategies like ACD and RSI in Python, including defining triggers and optimizing parameters like ATR, entry/exit points, and profit booking levels to maximize win rates.
4) Programming in Python to retrieve real-time market data, generate buy/sell signals, and place live trades through APIs.
TransmogrifAI - Automate Machine Learning Workflow with the power of Scala an...
TransmogrifAI - Automate Machine Learning Workflow with the power of Scala and Spark at massive scale.
Scala.IO, CPE - France
Automate ml workflow_transmogrif_ai-_chetan_khatri_berlin-scala
TransmogrifAI is an open source library for automating machine learning workflows built on Scala and Spark. It helps automate tasks like feature engineering, selection, model selection, and hyperparameter tuning. This reduces machine learning development time from months to hours. TransmogrifAI enforces type safety and modularity to build reusable, production-ready models. It was created by Salesforce to make machine learning more accessible to developers without a PhD in machine learning.
How we learned to rank search results big data meetup
Big Data Meetup Argentina Presentation About an implementation of Learning to Rank @OLXGroup Argentina
Introduction of Artificial Intelligence and Machine Learning
A Workshop to introduce Artificial Intelligence and Machine Learning for beginners. It starts with basics , terminologies and concepts for machine learning, compares with deep learning and artificial Intelligence. Highlights the ML and AI offerings like Jupyter Notebook, Azure ML , Amazon Sagemaker, Tensorflow etc.
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & PyTorch with B...
We all know what they say – the bigger the data, the better. But when the data gets really big, how do you mine it and what deep learning framework to use? This talk will survey, with a developer’s perspective, three of the most popular deep learning frameworks—TensorFlow, Keras, and PyTorch—as well as when to use their distributed implementations.
We’ll compare code samples from each framework and discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data) as well as help you answer questions such as:
As a developer how do I pick the right deep learning framework?
Do I want to develop my own model or should I employ an existing one?
How do I strike a trade-off between productivity and control through low-level APIs?
What language should I choose?
In this session, we will explore how to build a deep learning application with Tensorflow, Keras, or PyTorch in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you.
Chinaccelerator - Bigger, Badder, and now in Shanghai
As Chinaccelerator breaks onto the Shanghai scene to continue domination of the technology startup world in China, it was time to re-visit what we do, why we're so special, and who's involved. Learn how to "startup like a boss", "do more faster", and make a value-producing, investable, scalable company that will stand the test of time (Hint: it's about having the right 'entrephilosophy')
Machine learning & Time Series Analysis
This document discusses machine learning techniques for time series analysis and financial applications. It introduces common machine learning models like neural networks, convolutional neural networks, and LSTM networks. It also covers important concepts like feature engineering, labeling financial data, model evaluation techniques like cross validation, and analyzing feature importance. The document provides an overview of machine learning fundamentals and challenges in applying these techniques to time series and trading applications.
Machine learning & Time Series Analysis , Finlab CTO 韓承佑
This document discusses machine learning techniques for time series analysis and financial applications. It introduces common machine learning models like neural networks, convolutional neural networks, and LSTM networks. It also covers challenges like labeling financial data, overfitting, and backtesting strategies. Feature engineering techniques are proposed like triple barrier labeling and continuous trading signals. The document concludes with discussing future work like multi-asset modeling, strategy development and live trading.
SRECon23 Cognitive Apprenticeship in Action_ Alert Triage Hour of Power
Cognitive apprenticeship is the philosophy that it is more effective to learn in context and real-world situations compared to following a tutorial in a sandboxed environment. In a nutshell it is “learning-through-guided-experience” and shines when teaching problem-solving processes experts use to handle complex tasks like say…investigating an alert by spelunking in production observability and monitoring data. Learn how Alert Triage Hour of Power became a can’t miss meeting of camaraderie and system surprises!
Engineering Highly Maintainable Code: Maintain or Innovate
How long does it take your company to implement a new feature? Fix a bug? Ramp up a new developer? To re-write your code every five to ten years because it turns into spaghetti? At the end of the day, developers can do one of two things: maintain or innovate. In this session, Steve will dive in to code and show you tips, tricks, techniques, and real-world examples to help you better engineer highly maintainable code, keep your software relevant, reduce development costs, increase developer job satisfaction, and stay innovative.
Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...
The study of the human brain is thousands of years old. With the advent of modern electronics, it was natural to try to harness the thinking process. The first step toward artificial neural networks came in 1943 when McCulloch and Pitts defined how neurons might work. They modeled a simple neural network with electrical circuits. Here the proto-AI age started. 70 years later we entered in the second neural age: self-driving cars, mixed-reality, virtual reality and photo-realistic neural contents. Are you ready for the AI r-Evolution?
Easy path to machine learning
The document provides an overview of a presentation about Google Cloud developer tools and an easier path to machine learning. It introduces the speaker and their background and experience. It then outlines the agenda which includes introductions to machine learning and Google Cloud, Google APIs, Cloud ML APIs, and other APIs to consider. It provides examples of using various Cloud ML APIs like Vision, Natural Language, and Speech for tasks like image labeling, text analysis, and speech recognition. The goal is to demonstrate how APIs powered by machine learning can help ease the burden of learning machine learning by allowing users to leverage pre-built models if they can call APIs.
IC Group brochure
The IC Group is a recruitment partner specializing in the technology industry. It was founded with the goal of becoming an active member of the technology community rather than just an external supplier. This means they are involved in industry events and aim to build lasting relationships with clients and candidates. They offer recruitment services tailored to clients' needs, whether startups requiring flexible payment or large companies needing support for diverse hiring. With specialist recruiters and a global presence, they are able to source rare skills and top talent to access for clients.
Ai 管理人看人工智慧、發展與應用變革
AI
即將從棋盤，走入我們的真實世界
AI 經濟學、AI 管理學的開始
偰子
從機器學習到深度(強化)學習
AI 應用例--數位廣告
（休息）
AI 對產業帶來的變革
AI 與社會科學
Q & A
簡介
現職：大數據顧問、數據科學家
數位廣告、物聯網
熱愛軟體開發與物聯網創新，喜歡新創事業商務模式發展、整合性資料分析與探索性研究。
學歷：中山資管系所、台大商研博士畢
經歷：
東森信息科技 大數據顧問
Vpon 數據科學主管
美商安邁（AMI）新創事業 BD 副總
國立高雄應用科技大學資管系 專任助理教授
龍捲風科技創辦人
創立台灣第一個中文 BBS 網站
Fully Automated QA System For Large Scale Search And Recommendation Engines U...
1) The document describes a fully automated QA system for large scale search and recommendation engines using Spark.
2) It discusses key concepts in information retrieval like precision, recall, and learning to rank as well as challenges in building machine learning models for ranking like obtaining labeled training data.
3) The system architecture involves extracting features from query logs, calculating relevance scores from user click signals, and training machine learning models to improve ranking.
Dublin Ireland Spark Meetup October 15, 2015
This document discusses Apache Spark DataFrames and their use for fraud detection. It provides an overview of DataFrames, how they can be created from various data sources and used for data aggregation, filtering, and querying. Examples are given for creating DataFrames from text files, performing SQL queries, and using DataFrames for profiling, charting, and data quality checks. A variety of fraud detection applications for DataFrames are also outlined.
1시간만에 머신러닝 개념 따라 잡기
The document provides an overview of machine learning concepts and techniques. It begins with definitions of machine learning and common problem types like supervised, unsupervised, and reinforcement learning. Examples of machine learning algorithms for each problem type are given. The document then discusses best practices for machine learning projects, including framing the problem, preparing the data, selecting an appropriate model, and evaluating model performance. Feature engineering techniques for data preprocessing are also covered. The presentation aims to help audiences understand machine learning concepts and how to apply machine learning to real-world problems in one hour.
Sales Acceleration with Marketing Automation
Jen Stretch, Principal Consultant at HomeStretch Marketing presents
"Sales Acceleration with Marketing Automation" at the BMA Carolinas Connect2Education B2B Marketing Workshop
PyTorch Python Tutorial | Deep Learning Using PyTorch | Image Classifier Usin...
( ** Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka PyTorch Tutorial (Blog: https://goo.gl/4zxMfU) will help you in understanding various important basics of PyTorch. It also includes a use-case in which we will create an image classifier that will predict the accuracy of an image data-set using PyTorch.
Below are the topics covered in this tutorial:
1. What is Deep Learning?
2. What are Neural Networks?
3. Libraries available in Python
4. What is PyTorch?
5. Use-Case of PyTorch
6. Summary
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Misha Bilenko, Principal Researcher, Microsoft at MLconf SEA - 5/01/15
Many Shades of Scale: Big Learning Beyond Big Data: In the machine learning research community, much of the attention devoted to ‘big data’ in recent years has been manifested as development of new algorithms and systems for distributed training on many examples. This focus has led to significant advances in the field, from basic but operational implementations on popular platforms to highly sophisticated prototypes in the literature. In the meantime, other aspects of scaling up learning have received relatively little attention, although they are often more pressing in practice. The talk will survey these less-studied facets of big learning: scaling to an extremely large number of features, to many components in predictive pipelines, and to multiple data scientists collaborating on shared experiments.
Data Democratization at Nubank
Nubank is the leading fintech in Latin America. Using bleeding-edge technology, design, and data, the company aims to fight complexity and empower people to take control of their finances. We are disrupting an outdated and bureaucratic system by building a simple, safe and 100% digital environment.
In order to succeed, we need to constantly make better decisions in the speed of insight, and that’s what We aim when building Nubank’s Data Platform. In this talk we want to explore and share the guiding principles and how we created an automated, scalable, declarative and self-service platform that has more than 200 contributors, mostly non-technical, to build 8 thousand distinct datasets, ingesting data from 800 databases, leveraging Apache Spark expressiveness and scalability.
The topics we want to explore are:
– Making data-ingestion a no-brainer when creating new services
– Reducing the cycle time to deploy new Datasets and Machine Learning models to production
– Closing the loop and leverage knowledge processed in the analytical environment to take decisions in production
– Providing the perfect level of abstraction to users
You will get from this talk:
– Our love for ‘The Log’ and how we use it to decouple databases from its schema and distribute the work to keep schemas up to date to the entire team.
– How we made data ingestion so simple using Kafka Streams that teams stopped using databases for analytical data.
– The huge benefits of relying on the DataFrame API to create datasets which made possible having tests end-to-end verifying that the 8000 datasets work without even running a Spark Job and much more.
– The importance of creating the right amount of abstractions and restrictions to have the power to optimize.
Building Content Recommendation Systems using MXNet Gluon
Netflix competition triggered a flurry of research for recommendation engines. This presentation provides a survey of techniques and models for creating a recommender system. The presentation covers Matrix Factorisation, Factorisation Machines, Distributed Factorisation Machines, and DSSM networks as well provide code examples for developing a Matrix Factorisation in Gluon. At the end the presentation provides tips and tricks for large-scale, realtime recommender engines.
Hadoop France meetup Feb2016 : recommendations with spark
Système de recommandations de produits sur un site marchand par Koby KARP, Data Scientist (Equancy) & Hervé MIGNOT, Partner at Equancy
La recommandation reste un outil clé pour la personnalisation des sites marchands et le sujet est loin d’être épuisé. La prise en compte de la particularité d’un marché peut nécessité d’adapter le traitement et les algorithmes utilisés. Après une revue des techniques de recommandations, nous présenterons la démarche spécifique que nous avons adopté. Le système a été développé sous Spark pour la préparation des données et le calcul des modèles de recommandations. Une API simple et son service ont été développé pour délivrer les recommandations aux applications clientes.
Economics of Corporate Learning
The document discusses the evolution of corporate learning from traditional push and pull models to future models that are more integrated with work. It provides examples of different learning patterns used by companies, including organizational wikis, automated FAQs, community networks, professional updates, blogs, and more. These patterns save companies millions of dollars annually and support thousands or even millions of users by providing access to institutional knowledge in areas like know-where, know-how, know-what, and know-who. The overall benefits of these learning investments exceed $100 million while the costs are described as negligible.
Balancing Infrastructure with Optimization and Problem Formulation
- How do we currently think about Data Science?
- Why is infrastructure important to our field?
- Two tools we've built on Sailthru's Data Science team to deal with these problems are "Stolos" and "Relay.Mesos".
Programming the BBC micro:bit with MicroPython by Dunham High School
Talk by the students from Dunham High School (Wang Siyin, Pearlyn Loh, Liu Zixin, Jin Zilong) on programming the micro:bit
Python in big data ecosystem by Nicholas Lu
This document discusses Python in the big data ecosystem using the SMACK stack. SMACK stands for Spark, Kafka, Cassandra, Mesos, and Akka. Spark provides in-memory processing for speed and efficiency. Kafka handles data streaming. Cassandra provides scalable data storage across multiple computers. Mesos enables containerized environments for scalability and management. Akka supports high concurrency. The document outlines how SMACK is useful for mixed volume/velocity data, ETL/ELT processes, and near real-time analytics at scale. It provides examples of using each tool in the stack and discusses when SMACK is applicable.
More Related Content
Similar to Train your dragons! by Shilpa Karkera
Chinaccelerator - Bigger, Badder, and now in Shanghai
As Chinaccelerator breaks onto the Shanghai scene to continue domination of the technology startup world in China, it was time to re-visit what we do, why we're so special, and who's involved. Learn how to "startup like a boss", "do more faster", and make a value-producing, investable, scalable company that will stand the test of time (Hint: it's about having the right 'entrephilosophy')
Machine learning & Time Series Analysis
This document discusses machine learning techniques for time series analysis and financial applications. It introduces common machine learning models like neural networks, convolutional neural networks, and LSTM networks. It also covers important concepts like feature engineering, labeling financial data, model evaluation techniques like cross validation, and analyzing feature importance. The document provides an overview of machine learning fundamentals and challenges in applying these techniques to time series and trading applications.
Machine learning & Time Series Analysis , Finlab CTO 韓承佑
This document discusses machine learning techniques for time series analysis and financial applications. It introduces common machine learning models like neural networks, convolutional neural networks, and LSTM networks. It also covers challenges like labeling financial data, overfitting, and backtesting strategies. Feature engineering techniques are proposed like triple barrier labeling and continuous trading signals. The document concludes with discussing future work like multi-asset modeling, strategy development and live trading.
SRECon23 Cognitive Apprenticeship in Action_ Alert Triage Hour of Power
Cognitive apprenticeship is the philosophy that it is more effective to learn in context and real-world situations compared to following a tutorial in a sandboxed environment. In a nutshell it is “learning-through-guided-experience” and shines when teaching problem-solving processes experts use to handle complex tasks like say…investigating an alert by spelunking in production observability and monitoring data. Learn how Alert Triage Hour of Power became a can’t miss meeting of camaraderie and system surprises!
Engineering Highly Maintainable Code: Maintain or Innovate
How long does it take your company to implement a new feature? Fix a bug? Ramp up a new developer? To re-write your code every five to ten years because it turns into spaghetti? At the end of the day, developers can do one of two things: maintain or innovate. In this session, Steve will dive in to code and show you tips, tricks, techniques, and real-world examples to help you better engineer highly maintainable code, keep your software relevant, reduce development costs, increase developer job satisfaction, and stay innovative.
Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...
The study of the human brain is thousands of years old. With the advent of modern electronics, it was natural to try to harness the thinking process. The first step toward artificial neural networks came in 1943 when McCulloch and Pitts defined how neurons might work. They modeled a simple neural network with electrical circuits. Here the proto-AI age started. 70 years later we entered in the second neural age: self-driving cars, mixed-reality, virtual reality and photo-realistic neural contents. Are you ready for the AI r-Evolution?
Easy path to machine learning
The document provides an overview of a presentation about Google Cloud developer tools and an easier path to machine learning. It introduces the speaker and their background and experience. It then outlines the agenda which includes introductions to machine learning and Google Cloud, Google APIs, Cloud ML APIs, and other APIs to consider. It provides examples of using various Cloud ML APIs like Vision, Natural Language, and Speech for tasks like image labeling, text analysis, and speech recognition. The goal is to demonstrate how APIs powered by machine learning can help ease the burden of learning machine learning by allowing users to leverage pre-built models if they can call APIs.
IC Group brochure
The IC Group is a recruitment partner specializing in the technology industry. It was founded with the goal of becoming an active member of the technology community rather than just an external supplier. This means they are involved in industry events and aim to build lasting relationships with clients and candidates. They offer recruitment services tailored to clients' needs, whether startups requiring flexible payment or large companies needing support for diverse hiring. With specialist recruiters and a global presence, they are able to source rare skills and top talent to access for clients.
Ai 管理人看人工智慧、發展與應用變革
AI
即將從棋盤，走入我們的真實世界
AI 經濟學、AI 管理學的開始
偰子
從機器學習到深度(強化)學習
AI 應用例--數位廣告
（休息）
AI 對產業帶來的變革
AI 與社會科學
Q & A
簡介
現職：大數據顧問、數據科學家
數位廣告、物聯網
熱愛軟體開發與物聯網創新，喜歡新創事業商務模式發展、整合性資料分析與探索性研究。
學歷：中山資管系所、台大商研博士畢
經歷：
東森信息科技 大數據顧問
Vpon 數據科學主管
美商安邁（AMI）新創事業 BD 副總
國立高雄應用科技大學資管系 專任助理教授
龍捲風科技創辦人
創立台灣第一個中文 BBS 網站
Fully Automated QA System For Large Scale Search And Recommendation Engines U...
1) The document describes a fully automated QA system for large scale search and recommendation engines using Spark.
2) It discusses key concepts in information retrieval like precision, recall, and learning to rank as well as challenges in building machine learning models for ranking like obtaining labeled training data.
3) The system architecture involves extracting features from query logs, calculating relevance scores from user click signals, and training machine learning models to improve ranking.
Dublin Ireland Spark Meetup October 15, 2015
This document discusses Apache Spark DataFrames and their use for fraud detection. It provides an overview of DataFrames, how they can be created from various data sources and used for data aggregation, filtering, and querying. Examples are given for creating DataFrames from text files, performing SQL queries, and using DataFrames for profiling, charting, and data quality checks. A variety of fraud detection applications for DataFrames are also outlined.
1시간만에 머신러닝 개념 따라 잡기
The document provides an overview of machine learning concepts and techniques. It begins with definitions of machine learning and common problem types like supervised, unsupervised, and reinforcement learning. Examples of machine learning algorithms for each problem type are given. The document then discusses best practices for machine learning projects, including framing the problem, preparing the data, selecting an appropriate model, and evaluating model performance. Feature engineering techniques for data preprocessing are also covered. The presentation aims to help audiences understand machine learning concepts and how to apply machine learning to real-world problems in one hour.
Sales Acceleration with Marketing Automation
Jen Stretch, Principal Consultant at HomeStretch Marketing presents
"Sales Acceleration with Marketing Automation" at the BMA Carolinas Connect2Education B2B Marketing Workshop
PyTorch Python Tutorial | Deep Learning Using PyTorch | Image Classifier Usin...
( ** Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka PyTorch Tutorial (Blog: https://goo.gl/4zxMfU) will help you in understanding various important basics of PyTorch. It also includes a use-case in which we will create an image classifier that will predict the accuracy of an image data-set using PyTorch.
Below are the topics covered in this tutorial:
1. What is Deep Learning?
2. What are Neural Networks?
3. Libraries available in Python
4. What is PyTorch?
5. Use-Case of PyTorch
6. Summary
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Misha Bilenko, Principal Researcher, Microsoft at MLconf SEA - 5/01/15
Many Shades of Scale: Big Learning Beyond Big Data: In the machine learning research community, much of the attention devoted to ‘big data’ in recent years has been manifested as development of new algorithms and systems for distributed training on many examples. This focus has led to significant advances in the field, from basic but operational implementations on popular platforms to highly sophisticated prototypes in the literature. In the meantime, other aspects of scaling up learning have received relatively little attention, although they are often more pressing in practice. The talk will survey these less-studied facets of big learning: scaling to an extremely large number of features, to many components in predictive pipelines, and to multiple data scientists collaborating on shared experiments.
Data Democratization at Nubank
Nubank is the leading fintech in Latin America. Using bleeding-edge technology, design, and data, the company aims to fight complexity and empower people to take control of their finances. We are disrupting an outdated and bureaucratic system by building a simple, safe and 100% digital environment.
In order to succeed, we need to constantly make better decisions in the speed of insight, and that’s what We aim when building Nubank’s Data Platform. In this talk we want to explore and share the guiding principles and how we created an automated, scalable, declarative and self-service platform that has more than 200 contributors, mostly non-technical, to build 8 thousand distinct datasets, ingesting data from 800 databases, leveraging Apache Spark expressiveness and scalability.
The topics we want to explore are:
– Making data-ingestion a no-brainer when creating new services
– Reducing the cycle time to deploy new Datasets and Machine Learning models to production
– Closing the loop and leverage knowledge processed in the analytical environment to take decisions in production
– Providing the perfect level of abstraction to users
You will get from this talk:
– Our love for ‘The Log’ and how we use it to decouple databases from its schema and distribute the work to keep schemas up to date to the entire team.
– How we made data ingestion so simple using Kafka Streams that teams stopped using databases for analytical data.
– The huge benefits of relying on the DataFrame API to create datasets which made possible having tests end-to-end verifying that the 8000 datasets work without even running a Spark Job and much more.
– The importance of creating the right amount of abstractions and restrictions to have the power to optimize.
Building Content Recommendation Systems using MXNet Gluon
Netflix competition triggered a flurry of research for recommendation engines. This presentation provides a survey of techniques and models for creating a recommender system. The presentation covers Matrix Factorisation, Factorisation Machines, Distributed Factorisation Machines, and DSSM networks as well provide code examples for developing a Matrix Factorisation in Gluon. At the end the presentation provides tips and tricks for large-scale, realtime recommender engines.
Hadoop France meetup Feb2016 : recommendations with spark
Système de recommandations de produits sur un site marchand par Koby KARP, Data Scientist (Equancy) & Hervé MIGNOT, Partner at Equancy
La recommandation reste un outil clé pour la personnalisation des sites marchands et le sujet est loin d’être épuisé. La prise en compte de la particularité d’un marché peut nécessité d’adapter le traitement et les algorithmes utilisés. Après une revue des techniques de recommandations, nous présenterons la démarche spécifique que nous avons adopté. Le système a été développé sous Spark pour la préparation des données et le calcul des modèles de recommandations. Une API simple et son service ont été développé pour délivrer les recommandations aux applications clientes.
Economics of Corporate Learning
The document discusses the evolution of corporate learning from traditional push and pull models to future models that are more integrated with work. It provides examples of different learning patterns used by companies, including organizational wikis, automated FAQs, community networks, professional updates, blogs, and more. These patterns save companies millions of dollars annually and support thousands or even millions of users by providing access to institutional knowledge in areas like know-where, know-how, know-what, and know-who. The overall benefits of these learning investments exceed $100 million while the costs are described as negligible.
Balancing Infrastructure with Optimization and Problem Formulation
- How do we currently think about Data Science?
- Why is infrastructure important to our field?
- Two tools we've built on Sailthru's Data Science team to deal with these problems are "Stolos" and "Relay.Mesos".
Similar to Train your dragons! by Shilpa Karkera (20)
Chinaccelerator - Bigger, Badder, and now in Shanghai
Chinaccelerator - Bigger, Badder, and now in Shanghai
Machine learning & Time Series Analysis , Finlab CTO 韓承佑
Machine learning & Time Series Analysis , Finlab CTO 韓承佑
SRECon23 Cognitive Apprenticeship in Action_ Alert Triage Hour of Power
SRECon23 Cognitive Apprenticeship in Action_ Alert Triage Hour of Power
Engineering Highly Maintainable Code: Maintain or Innovate
Engineering Highly Maintainable Code: Maintain or Innovate
Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...
Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...
Fully Automated QA System For Large Scale Search And Recommendation Engines U...
Fully Automated QA System For Large Scale Search And Recommendation Engines U...
PyTorch Python Tutorial | Deep Learning Using PyTorch | Image Classifier Usin...
PyTorch Python Tutorial | Deep Learning Using PyTorch | Image Classifier Usin...
Misha Bilenko, Principal Researcher, Microsoft at MLconf SEA - 5/01/15
Misha Bilenko, Principal Researcher, Microsoft at MLconf SEA - 5/01/15
Building Content Recommendation Systems using MXNet Gluon
Building Content Recommendation Systems using MXNet Gluon
Hadoop France meetup Feb2016 : recommendations with spark
Hadoop France meetup Feb2016 : recommendations with spark
Balancing Infrastructure with Optimization and Problem Formulation
Balancing Infrastructure with Optimization and Problem Formulation
More from PYCON MY PLT
Programming the BBC micro:bit with MicroPython by Dunham High School
Talk by the students from Dunham High School (Wang Siyin, Pearlyn Loh, Liu Zixin, Jin Zilong) on programming the micro:bit
Python in big data ecosystem by Nicholas Lu
This document discusses Python in the big data ecosystem using the SMACK stack. SMACK stands for Spark, Kafka, Cassandra, Mesos, and Akka. Spark provides in-memory processing for speed and efficiency. Kafka handles data streaming. Cassandra provides scalable data storage across multiple computers. Mesos enables containerized environments for scalability and management. Akka supports high concurrency. The document outlines how SMACK is useful for mixed volume/velocity data, ETL/ELT processes, and near real-time analytics at scale. It provides examples of using each tool in the stack and discusses when SMACK is applicable.
Python testing like a pro by Keith Yang
This document provides an overview of software testing strategies and tools for Python projects. It recommends:
1. Using tools like IPython, linters, yapf, pytest, and hypothesis to find errors early through static and dynamic testing. This allows issues to be identified and addressed before deployment.
2. Enabling easy debugging in production with tools like Sentry, IPython, and alog to monitor and diagnose runtime errors.
3. Preparing for failures through defensive programming techniques and testing backup plans, as "no tested backup plan equals no backup." Thorough testing during development helps maximize uptime and minimize surprises once software is deployed.
The programmer's mind by Jessica McKellar
This document discusses how programming changes one's mindset and gives programmers a sense of agency over systems. It emphasizes that programmers have the ability to understand and modify systems based on explicit principles of free software. The document argues that this programmer mindset of being able to change rules and systems translates to an activist mindset of fighting for access, diversity, and representation in both physical and digital spaces.
Using machine learning to try and predict taxi availability by Narahari Allam...
This document discusses using machine learning to predict taxi availability in Singapore. It will cover fetching open taxi data from Singapore's data.gov.sg API, preprocessing the data, and exploring multiple machine learning algorithms to predict availability. The talk assumes basic Python and machine learning knowledge and focuses on implementation rather than mathematical details. Various algorithms will be tested to compare their results and improve predictions, with the goal being a practical introduction rather than highly accurate predictions or complex methods. Code and materials are available online for others to replicate the analysis.
Data mining news articles by Amir Othman for PyCon APAC 2017
This document discusses a project to collect and mine news articles from various sources including German and Malaysian news sites. The goals are to build a corpus for natural language processing tasks, preserve a piece of digital history as news sites change over time, and cross-validate information across sources. The process involves scraping links to old articles, using RSS/Twitter feeds to retrieve new articles, cleaning and extracting structured data from the articles, and performing analysis like named entity recognition, topic modeling, and similarity comparisons. The collected and analyzed data will be made available through REST APIs and used to create automated timelines applications.
More from PYCON MY PLT (6)
Programming the BBC micro:bit with MicroPython by Dunham High School
Programming the BBC micro:bit with MicroPython by Dunham High School
Using machine learning to try and predict taxi availability by Narahari Allam...
Using machine learning to try and predict taxi availability by Narahari Allam...
Data mining news articles by Amir Othman for PyCon APAC 2017
Data mining news articles by Amir Othman for PyCon APAC 2017
Recently uploaded
Graspan: A Big Data System for Big Code Analysis
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Vitthal Shirke Java Microservices Resume.pdf
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
WhatsApp offers simple, reliable, and private messaging and calling services for free worldwide. With end-to-end encryption, your personal messages and calls are secure, ensuring only you and the recipient can access them. Enjoy voice and video calls to stay connected with loved ones or colleagues. Express yourself using stickers, GIFs, or by sharing moments on Status. WhatsApp Business enables global customer outreach, facilitating sales growth and relationship building through showcasing products and services. Stay connected effortlessly with group chats for planning outings with friends or staying updated on family conversations.
What is Augmented Reality Image Tracking
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
Hand Rolled Applicative User Validation Code Kata
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
openEuler Case Study - The Journey to Supply Chain Security
openEuler Case Study - The Journey to Supply Chain Security
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
DDS-Security 1.2 - What's New? Stronger security for long-running systems
DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
GraphSummit Paris - The art of the possible with Graph Technology
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GreenCode-A-VSCode-Plugin--Dario-Jurisic
Presentation about a VSCode plugin from Dario Jurisic at the GSD Community Stage meetup
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
Presentation from Ghazal Aakel and Eric Jochum at the GSD Community Stage Meetup on June 6th, 2024
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
#AIFusionBuddyUserExperience
#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
#AIFusionBuddyInstallation,
#AIFusionBuddyRefundPolicy,
#AIFusionBuddyDemo,
#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Transform Your Communication with Cloud-Based IVR Solutions
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
Recently uploaded (20)
openEuler Case Study - The Journey to Supply Chain Security
openEuler Case Study - The Journey to Supply Chain Security
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
DDS-Security 1.2 - What's New? Stronger security for long-running systems
DDS-Security 1.2 - What's New? Stronger security for long-running systems
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
GraphSummit Paris - The art of the possible with Graph Technology
GraphSummit Paris - The art of the possible with Graph Technology
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Transform Your Communication with Cloud-Based IVR Solutions
Transform Your Communication with Cloud-Based IVR Solutions
Train your dragons! by Shilpa Karkera
- 2. WHY? WHO? HOW? ASH LEFT HIS CHARIZARD HERE TO TRAIN ;-)
- 3. About Me ▶ Shilpa Karkera, CEO & Founder of Myraa Technologies ▶ Blockchain Specialist in Trade Finance | Virtual Reality with HR | Deep Learning Architect ▶ Runs a passionate team of GOHLCIs (Group of High Learning Curve Individuals) ▶ We’ve built Classifiers, Recommendation Bots, Financial Parsers used by over million users. Solving global industrial challenges in India, UK, Singapore, we created a self-revenue generated ecosystem.
- 4. WHAT WE TEACH THE DRAGON TODAY? AIM & FIRE MOVING OBJECTS
- 5. AIM & FIRE ▶ AIM VALUE FUNCTION ▶ Detection algorithm over object identification ▶ Improvisation of positional vector with Value Function ▶ V = r(t) + w*r(t+1) + w*w*r(t+2) + …. ▶ AIM & FIRE VALUE FUNCTION ▶ Detection and action trigger on threshold ▶ Value of a state-action pair ▶ Measuring the optimal policy, after executing s(t) times V( s(t) ) = r(t) + w*V^ ( s(t) ) Q( s(t), a(t) ) = r(t) + w*max Q( s(t+1), a) a
- 6. WHAT THE DRAGON NEEDS TO IMPROVING ON? FIRING WITH ACCURACY OVER THE RIGHT OBJECTS
- 7. Computer Vision DRAGON LEARNS TO AIM • RGB extraction • Positional Vectors • Noise Elimination Image Extraction • Segmentation • Convolutional Neural network Deep Net Training • Labelling • Model Averaging • Bounding Box Regression Context Modelling
- 8. How ? (Training) ➢ http://www.myraatechnologies.com/DB/flowchart-parser.html Code Contributions - Lahari Meruva
- 9. How ? (Prediction) ➢ http://www.myraatechnologies.com/DB/flowchart-parser.html Code Contributions - Lahari Meruva
- 10. Classification TRAINING THE DRAGON BETWEEN RIGHT AND WRONG OBJECTS 1 Data Preparation 2 Vectorizer 3 Feature Extraction 4 Multinomial NaiveBayes
- 11. How ? ➢ http://www.myraatechnologies.com/DB/multiclass_clf.html Code Contributions - Avinash Goje
- 12. Dragon learns to classify Right Target Objects ➢ http://www.myraatechnologies.com/DB/multiclass_clf.html Code Contributions - Avinash Goje
- 13. Learns about moving objects, improves to learn about pseudo random trajectories, recalculates precision of aim & fires Meanwhile…. The Dragon
- 17. WHAT NEXT?? EVOLUTION OF NEW TASKS & DECISIONS
- 19. THANK YOU! ➢ SHILPA KARKERAA ➢ CEO & Founder ➢ Myraa Technologies ➢ shilpa@myraatechnologies.com ➢ https://www.linkedin.com/in/shilpa-karkeraa-4b306854/