Transfer learning with Custom Vision
•
0 likes•549 views
Transfer learning allows you to adapt pre-trained machine learning models to fit custom use cases. In this talk, we'll provide an overview of transfer learning, and demo transfer learning in practise using customvision.ai.
Report
Share
Report
Share
Download to read offline
Recommended
Fashion product de-duplication with image similarity and LSH
This document summarizes how a company detects duplicate fashion products across different retailers by using local sensitivity hashing on image descriptors. Key points:
- The company scrapes data from many retailers and designers, with most processes automated including classification, recommendations, and sales.
- Initially they used fuzzy string matching to find duplicates but this did not work well with vague product names like "dress" or "shirt".
- They now detect duplicates by extracting BRISK image descriptors and applying k-means clustering to create "bags of words" from the descriptors to compare image similarity.
- For speed, they apply local sensitivity hashing to the descriptors to filter for similar images before running comparisons, projecting the high
Ai in 45 minutes
The document provides an overview of artificial intelligence and machine learning techniques for image classification using small datasets. It describes how to build a basic convolutional neural network from scratch or fine-tune a pre-trained model like VGG16 to classify images of cats and dogs with only 2000 training examples. Fine-tuning the top layers of VGG16 improved accuracy from 79% using just bottleneck features to 98%, showing how transfer learning can boost performance for small datasets.
Deep Learning in a nutshell
In HopebayTech.com Techfriday event, I share the brief understanding of Deep Learning concept which is learned from some great talks and reading.
Deep learning
This document provides an overview of machine learning and deep learning concepts. It begins with an introduction to machine learning basics, including supervised and unsupervised learning. It then discusses deep learning, why it is useful, and its main components like activation functions, optimizers, and regularization methods. The document explains deep neural network architecture including convolutional neural networks. It provides examples of convolutional and max pooling layers and how they help reduce parameters in neural networks.
ML crash course
Given at the https://www.meetup.com/Build-a-business-on-the-big-tech-themes-of-today/events/243084444/ meetup
A Developer's Guide To Machine Learning
This document provides an overview of machine learning and perspectives from various experts:
- It discusses different types of machine learning problems like classification, regression, and clustering and examples of algorithms used to solve each.
- Experts offer views on neural networks, with one saying they are like a "swiss army knife" and can be used to solve many machine learning problems.
- Other experts discuss the importance of linear algebra and matrix multiplication in machine learning models like neural networks.
- One expert prefers neural networks and singular value decomposition for machine learning tasks.
Artificial intelligence slides beginners
This document provides an overview of machine learning basics, including definitions of machine learning, neural networks, and different types of machine learning such as supervised, unsupervised, and reinforcement learning. It discusses applications of machine learning in areas like healthcare, finance, translation, and gaming. Deep learning and challenges in the field are also summarized. The document is intended as a brief introduction for beginners to understand machine learning concepts.
20170402 Crop Innovation and Business - Amsterdam
This document discusses applying machine learning and artificial intelligence techniques like deep neural networks to problems in genomics and agriculture. It provides examples of using Google Cloud platforms and services for storing and analyzing large genomic datasets, as well as developing models for tasks like variant calling from sequencing data and marker-assisted breeding. The document advocates that Google is well-positioned to handle massive volumes of genomic and agricultural data and help advance the application of AI in these domains.
Recommended
Fashion product de-duplication with image similarity and LSH
This document summarizes how a company detects duplicate fashion products across different retailers by using local sensitivity hashing on image descriptors. Key points:
- The company scrapes data from many retailers and designers, with most processes automated including classification, recommendations, and sales.
- Initially they used fuzzy string matching to find duplicates but this did not work well with vague product names like "dress" or "shirt".
- They now detect duplicates by extracting BRISK image descriptors and applying k-means clustering to create "bags of words" from the descriptors to compare image similarity.
- For speed, they apply local sensitivity hashing to the descriptors to filter for similar images before running comparisons, projecting the high
Ai in 45 minutes
The document provides an overview of artificial intelligence and machine learning techniques for image classification using small datasets. It describes how to build a basic convolutional neural network from scratch or fine-tune a pre-trained model like VGG16 to classify images of cats and dogs with only 2000 training examples. Fine-tuning the top layers of VGG16 improved accuracy from 79% using just bottleneck features to 98%, showing how transfer learning can boost performance for small datasets.
Deep Learning in a nutshell
In HopebayTech.com Techfriday event, I share the brief understanding of Deep Learning concept which is learned from some great talks and reading.
Deep learning
This document provides an overview of machine learning and deep learning concepts. It begins with an introduction to machine learning basics, including supervised and unsupervised learning. It then discusses deep learning, why it is useful, and its main components like activation functions, optimizers, and regularization methods. The document explains deep neural network architecture including convolutional neural networks. It provides examples of convolutional and max pooling layers and how they help reduce parameters in neural networks.
ML crash course
Given at the https://www.meetup.com/Build-a-business-on-the-big-tech-themes-of-today/events/243084444/ meetup
A Developer's Guide To Machine Learning
This document provides an overview of machine learning and perspectives from various experts:
- It discusses different types of machine learning problems like classification, regression, and clustering and examples of algorithms used to solve each.
- Experts offer views on neural networks, with one saying they are like a "swiss army knife" and can be used to solve many machine learning problems.
- Other experts discuss the importance of linear algebra and matrix multiplication in machine learning models like neural networks.
- One expert prefers neural networks and singular value decomposition for machine learning tasks.
Artificial intelligence slides beginners
This document provides an overview of machine learning basics, including definitions of machine learning, neural networks, and different types of machine learning such as supervised, unsupervised, and reinforcement learning. It discusses applications of machine learning in areas like healthcare, finance, translation, and gaming. Deep learning and challenges in the field are also summarized. The document is intended as a brief introduction for beginners to understand machine learning concepts.
20170402 Crop Innovation and Business - Amsterdam
This document discusses applying machine learning and artificial intelligence techniques like deep neural networks to problems in genomics and agriculture. It provides examples of using Google Cloud platforms and services for storing and analyzing large genomic datasets, as well as developing models for tasks like variant calling from sequencing data and marker-assisted breeding. The document advocates that Google is well-positioned to handle massive volumes of genomic and agricultural data and help advance the application of AI in these domains.
B4UConference_machine learning_deeplearning
Agenda
Understanding Machine Learning
Machine Learning Algorithms & Application
Demo Machine Learning
Why Deep Learning?
Deep Learning Algorithms & Application
Demo Deep Learning
[Revised] Intro to CNN
Vincent gives an introductory presentation on convolutional neural networks (CNNs) for image recognition. He covers:
1) The principles of CNNs including convolution, ReLU activation, and max pooling for extracting features from images.
2) How CNN stacks are used along with a fully connected layer to generate predictions from feature maps.
3) Techniques for avoiding overfitting like data augmentation, dropout, and transfer learning by leveraging pretrained models.
How can we train with few data
The document discusses approaches for using deep learning with small datasets, including transfer learning techniques like fine-tuning pre-trained models, multi-task learning, and metric learning approaches for few-shot and zero-shot learning problems. It also covers domain adaptation techniques when labels are not available, as well as anomaly detection for skewed label distributions. Traditional models like SVM are suggested as initial approaches, with deep learning techniques applied if those are not satisfactory.
Everything You Need to Know About Computer Vision
https://www.datatobiz.com/blog/computer-vision-guide/
To most, they consist of pixels only, but digital images, like any other form of content, can be mined for data by computers. Further, they can also be analyzed afterward. Use image processing methods, including computers, to retrieve the information from still photographs, and even videos. Here we are going to discuss everything you must know about computer vision.
There are two forms-Machine Vision, which is this tech’s more “traditional” type, and Computer Vision (CV), a digital world offshoot. While the first is mostly for industrial use, as an example are cameras on a conveyor belt in an industrial plant, the second is to teach computers to extract and understand “hidden” data inside digital images and videos.
Facebook this August said it was open-sourcing its work to improve its Computer Visiontechnology software for users further. This image was posted by FB Research scientist Piotr Dollar to explain the difference between human and computer vision.
Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take big leaps in recent years, and in some tasks related to detection and labeling of objects has been able to surpass humans.
One of the driving factors behind computer vision development is the amount of data we produce now, which will then get used to educate and develop computer vision.
Distributed Models Over Distributed Data with MLflow, Pyspark, and Pandas
Does more data always improve ML models? Is it better to use distributed ML instead of single node ML?
In this talk I will show that while more data often improves DL models in high variance problem spaces (with semi or unstructured data) such as NLP, image, video more data does not significantly improve high bias problem spaces where traditional ML is more appropriate. Additionally, even in the deep learning domain, single node models can still outperform distributed models via transfer learning.
Data scientists have pain points running many models in parallel automating the experimental set up. Getting others (especially analysts) within an organization to use their models Databricks solves these problems using pandas udfs, ml runtime and MLflow.
Fine-tuning Pre-Trained Models for Generative AI Applications
This article provides an in-depth exploration of fine-tuning pre-trained models, including the process, benefits, and more.
II-SDV 2017: The Next Era: Deep Learning for Biomedical Research
Deep learning is hot, making waves, delivering results, and is somewhat of a buzzword today. There is a desire to apply deep learning to anything that is digital. Unlike the brain, these artificial neural networks have a very strict predefined structure. The brain is made up of neurons that talk to each other via electrical and chemical signals. We do not differentiate between these two types of signals in artificial neural networks. They are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Another buzzword that was used for the last few years across all industries is “big data”. In biomedical and health sciences, both unstructured and structured information constitute "big data". On the one hand deep learning needs lot of data whereas “big data" has value only when it generates actionable insight. Given this, these two areas are destined to be married. The couple is made for each other. The time is ripe now for a synergistic association that will benefit the pharmaceutical companies. It may be only a short time before we have vice presidents of machine learning or deep learning in pharmaceutical and biotechnology companies. This presentation will review the prominent deep learning methods and discuss these techniques for their usefulness in biomedical and health informatics.
Designing a neural network architecture for image recognition
The document discusses the design of a basic neural network architecture for image recognition. It begins by outlining a simple design with dense layers but notes this does not work well for images. Convolutional layers are introduced to help detect patterns regardless of location. Max pooling and dropout layers are also discussed to make the network more efficient and robust. The document provides examples of how these various layer types work and combines them into a basic convolutional block that can be stacked for more complex images.
Transfer Learning: Breve introducción a modelos pre-entrenados.
Introducción al uso de pre-trained models para Redes Convolucionales.
Video: https://youtu.be/dQr7I5_jEpM
Andrew NG machine learning
This document discusses best practices for setting up development and test sets for machine learning models. It recommends that the dev and test sets:
1) Should reflect the actual data distribution you want your model to perform well on, rather than just being a random split of your training data.
2) Should come from the same data distribution. Having mismatched dev and test sets makes progress harder to measure.
3) The dev set should be large enough, typically thousands to tens of thousands of examples, to detect small performance differences as models are improved. The test set size depends on desired confidence in overall performance.
Classification case study + intro to cnn
Vincent Tatan presents an introduction to convolutional neural networks (CNNs) for image recognition. The document discusses key CNN concepts like convolution, ReLU activation, and max pooling. It provides an example of using a CNN to classify cats versus dogs images, demonstrating overfitting issues and techniques like dropout and data augmentation to address them. Transfer learning is introduced as a way to leverage models pre-trained on large datasets. Code examples and resources are shared to demonstrate CNN implementations in practice.
Python Machine Learning January 2018 - Ho Chi Minh City
This document provides an overview of machine learning concepts using Python. It defines machine learning as the study of how computers can learn without being explicitly programmed. It discusses different machine learning algorithms like supervised vs unsupervised learning, classification vs regression, and clustering vs association. It also gives examples of machine learning applications and recommends Python as a good programming language for machine learning due to useful libraries like NumPy, Pandas, and SciPy. Finally, it outlines future uses of machine learning and provides resources for further learning.
GDSC Unsri - From Zero to Hero with TensorFlow.pptx
Ever heard of Flutter x Tensorflow? This session is made to help you understand how to integrate Flutter with Tensorflow and guide you through the essential steps. Whether you're new to Flutter x Tensorflow or seeking to improve your skills, this event will provide you with the knowledge and tools to master your skills in Flutter x Tensorflow. Come join us in the "Flutter x Tensorflow Workshop" by GDSC UNSRI! Don't miss out on this opportunity to enhance your proficiency! 🚀
Week3-Deep Neural Network (DNN).pptx
Deep neural networks learn hierarchical representations of data through multiple layers of feature extraction. Lower layers identify low-level features like edges while higher layers integrate these into more complex patterns and objects. Deep learning models are trained on large labeled datasets by presenting examples, calculating errors, and adjusting weights to minimize errors over many iterations. Deep learning has achieved human-level performance on tasks like image recognition due to its ability to leverage large amounts of training data and learn representations automatically rather than relying on manually designed features.
Key Insights Of Using Deep Learning To Analyze Healthcare Data | Workshop Fro...
In this presentation, I present how to properly discover, analyze and find trends in various types of healthcare data in order to utilize machine learning algorithms to predict future trends in the data. This presentation directly discusses the implications of data analysis in predicting benign and malignant cancers but the same techniques in this presentation can be applied to any other types of data in the real world.
For a more in-depth presentation, please watch the video presentation of this slideshow linked here: https://youtu.be/gXSl2iWcJ00
Machine learning with TensorFlow
This document discusses machine learning and TensorFlow. It begins with an introduction to modern machine intelligence and its real-world impact through examples like medical imaging. Next, it provides an overview of machine learning, moving to discuss TensorFlow and Google's use of machine learning in products like Translate, Photos, and Gmail. The document promotes free machine learning resources and ends with contact information for the presenter.
Automation, intelligence and knowledge modelling
Automation, intelligence and knowledge modelling,
My talk at http://web11.org/
Numerous talks, news articles and blog posts have been written about impact of recent advances in technology to our society. To a layman, it is all mix of "good news/bad news" show: from improvements in transport, agriculture or health, to jobs disappearing, or wealth inequality, just to name a few. But to techies like myself, the real question is somehow different: How far we can go?.
Machine learning || Introduction || Main Components || Examples || Techniques
This document discusses machine learning. It defines machine learning as designing algorithms that extract valuable information from data. It states that machine learning is useful for problems where existing solutions require fine-tuning or many rules, complex problems traditional methods can't solve, and environments that are changing. Examples of machine learning tasks are given, such as image classification using convolutional neural networks, detecting tumors using convolutional neural networks, automatically classifying news articles using natural language processing, and creating chatbots or personal assistants using natural language processing and question-answering modules.
100_Days_of_Data_Science
The document provides guidance for days 90-100 of a 100 Days of Data Science Challenge, suggesting focusing on reviewing and revising one's progress during this time. It recommends reviewing goals and objectives, reflecting on strengths and challenges, reviewing completed project work, seeking feedback, and revising one's project plan based on learnings and feedback to stay motivated and on track, and identify areas for continued focus and improvement.
Deep learning: Cutting through the Myths and Hype
The field of Artificial Intelligence (AI) has been revitalized in this decade, primarily due to the large-scale application of Deep Learning (DL) and other Machine Learning (ML) algorithms. This has been most evident in applications like computer vision, natural language processing, and game bots. However, extraordinary successes within a short period of time have also had the unintended consequence of causing a sharp difference of opinion in research and industrial communities regarding the capabilities and limitations of deep learning. A few questions you might have heard being asked (or asked yourself) include:
a. We don’t know how Deep Neural Networks make decisions, so can we trust them?
b. Can Deep Learning deal with highly non-linear continuous systems with millions of variables?
c. Can Deep Learning solve the Artificial General Intelligence problem?
The goal of this seminar is to provide a 1000-feet view of Deep Learning and hopefully answer the questions above. The seminar will touch upon the evolution, current state of the art, and peculiarities of Deep Learning, and share thoughts on using Deep Learning as a tool for developing power system solutions.
AWS Machine Learning & Google Cloud Machine Learning
This document provides an overview and comparison of machine learning services from AWS and Google Cloud. It begins with introductions of the speaker and agenda. It then provides background on machine learning and the three main types (supervised, unsupervised, reinforcement learning). It discusses how cloud services can provide on-demand compute for machine learning. It gives a breakdown of specific machine learning services from Google Cloud (such as Cloud ML Engine, Vision, Translation) and AWS (such as Machine Learning, Lex, Rekognition). It provides an example of pricing cloud infrastructure. Finally, it demonstrates building a multi-class classifier on the Iris dataset using logistic regression with both Google Cloud ML Engine and AWS Machine Learning.
Practical AI for Business: Bandit Algorithms
This document provides an overview of bandit algorithms and their applications. It begins by explaining the multi-armed bandit problem and some basic algorithms like epsilon-greedy to solve it. It then discusses more advanced techniques like Thompson sampling and their benefits over naive approaches. Finally, it outlines several real-world uses of bandit algorithms, including UI optimization, recommendation systems, and multiplayer games. Bandit algorithms provide a powerful way to optimize outcomes in situations where rewards are not immediately revealed.
More Related Content
Similar to Transfer learning with Custom Vision
B4UConference_machine learning_deeplearning
Agenda
Understanding Machine Learning
Machine Learning Algorithms & Application
Demo Machine Learning
Why Deep Learning?
Deep Learning Algorithms & Application
Demo Deep Learning
[Revised] Intro to CNN
Vincent gives an introductory presentation on convolutional neural networks (CNNs) for image recognition. He covers:
1) The principles of CNNs including convolution, ReLU activation, and max pooling for extracting features from images.
2) How CNN stacks are used along with a fully connected layer to generate predictions from feature maps.
3) Techniques for avoiding overfitting like data augmentation, dropout, and transfer learning by leveraging pretrained models.
How can we train with few data
The document discusses approaches for using deep learning with small datasets, including transfer learning techniques like fine-tuning pre-trained models, multi-task learning, and metric learning approaches for few-shot and zero-shot learning problems. It also covers domain adaptation techniques when labels are not available, as well as anomaly detection for skewed label distributions. Traditional models like SVM are suggested as initial approaches, with deep learning techniques applied if those are not satisfactory.
Everything You Need to Know About Computer Vision
https://www.datatobiz.com/blog/computer-vision-guide/
To most, they consist of pixels only, but digital images, like any other form of content, can be mined for data by computers. Further, they can also be analyzed afterward. Use image processing methods, including computers, to retrieve the information from still photographs, and even videos. Here we are going to discuss everything you must know about computer vision.
There are two forms-Machine Vision, which is this tech’s more “traditional” type, and Computer Vision (CV), a digital world offshoot. While the first is mostly for industrial use, as an example are cameras on a conveyor belt in an industrial plant, the second is to teach computers to extract and understand “hidden” data inside digital images and videos.
Facebook this August said it was open-sourcing its work to improve its Computer Visiontechnology software for users further. This image was posted by FB Research scientist Piotr Dollar to explain the difference between human and computer vision.
Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take big leaps in recent years, and in some tasks related to detection and labeling of objects has been able to surpass humans.
One of the driving factors behind computer vision development is the amount of data we produce now, which will then get used to educate and develop computer vision.
Distributed Models Over Distributed Data with MLflow, Pyspark, and Pandas
Does more data always improve ML models? Is it better to use distributed ML instead of single node ML?
In this talk I will show that while more data often improves DL models in high variance problem spaces (with semi or unstructured data) such as NLP, image, video more data does not significantly improve high bias problem spaces where traditional ML is more appropriate. Additionally, even in the deep learning domain, single node models can still outperform distributed models via transfer learning.
Data scientists have pain points running many models in parallel automating the experimental set up. Getting others (especially analysts) within an organization to use their models Databricks solves these problems using pandas udfs, ml runtime and MLflow.
Fine-tuning Pre-Trained Models for Generative AI Applications
This article provides an in-depth exploration of fine-tuning pre-trained models, including the process, benefits, and more.
II-SDV 2017: The Next Era: Deep Learning for Biomedical Research
Deep learning is hot, making waves, delivering results, and is somewhat of a buzzword today. There is a desire to apply deep learning to anything that is digital. Unlike the brain, these artificial neural networks have a very strict predefined structure. The brain is made up of neurons that talk to each other via electrical and chemical signals. We do not differentiate between these two types of signals in artificial neural networks. They are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Another buzzword that was used for the last few years across all industries is “big data”. In biomedical and health sciences, both unstructured and structured information constitute "big data". On the one hand deep learning needs lot of data whereas “big data" has value only when it generates actionable insight. Given this, these two areas are destined to be married. The couple is made for each other. The time is ripe now for a synergistic association that will benefit the pharmaceutical companies. It may be only a short time before we have vice presidents of machine learning or deep learning in pharmaceutical and biotechnology companies. This presentation will review the prominent deep learning methods and discuss these techniques for their usefulness in biomedical and health informatics.
Designing a neural network architecture for image recognition
The document discusses the design of a basic neural network architecture for image recognition. It begins by outlining a simple design with dense layers but notes this does not work well for images. Convolutional layers are introduced to help detect patterns regardless of location. Max pooling and dropout layers are also discussed to make the network more efficient and robust. The document provides examples of how these various layer types work and combines them into a basic convolutional block that can be stacked for more complex images.
Transfer Learning: Breve introducción a modelos pre-entrenados.
Introducción al uso de pre-trained models para Redes Convolucionales.
Video: https://youtu.be/dQr7I5_jEpM
Andrew NG machine learning
This document discusses best practices for setting up development and test sets for machine learning models. It recommends that the dev and test sets:
1) Should reflect the actual data distribution you want your model to perform well on, rather than just being a random split of your training data.
2) Should come from the same data distribution. Having mismatched dev and test sets makes progress harder to measure.
3) The dev set should be large enough, typically thousands to tens of thousands of examples, to detect small performance differences as models are improved. The test set size depends on desired confidence in overall performance.
Classification case study + intro to cnn
Vincent Tatan presents an introduction to convolutional neural networks (CNNs) for image recognition. The document discusses key CNN concepts like convolution, ReLU activation, and max pooling. It provides an example of using a CNN to classify cats versus dogs images, demonstrating overfitting issues and techniques like dropout and data augmentation to address them. Transfer learning is introduced as a way to leverage models pre-trained on large datasets. Code examples and resources are shared to demonstrate CNN implementations in practice.
Python Machine Learning January 2018 - Ho Chi Minh City
This document provides an overview of machine learning concepts using Python. It defines machine learning as the study of how computers can learn without being explicitly programmed. It discusses different machine learning algorithms like supervised vs unsupervised learning, classification vs regression, and clustering vs association. It also gives examples of machine learning applications and recommends Python as a good programming language for machine learning due to useful libraries like NumPy, Pandas, and SciPy. Finally, it outlines future uses of machine learning and provides resources for further learning.
GDSC Unsri - From Zero to Hero with TensorFlow.pptx
Ever heard of Flutter x Tensorflow? This session is made to help you understand how to integrate Flutter with Tensorflow and guide you through the essential steps. Whether you're new to Flutter x Tensorflow or seeking to improve your skills, this event will provide you with the knowledge and tools to master your skills in Flutter x Tensorflow. Come join us in the "Flutter x Tensorflow Workshop" by GDSC UNSRI! Don't miss out on this opportunity to enhance your proficiency! 🚀
Week3-Deep Neural Network (DNN).pptx
Deep neural networks learn hierarchical representations of data through multiple layers of feature extraction. Lower layers identify low-level features like edges while higher layers integrate these into more complex patterns and objects. Deep learning models are trained on large labeled datasets by presenting examples, calculating errors, and adjusting weights to minimize errors over many iterations. Deep learning has achieved human-level performance on tasks like image recognition due to its ability to leverage large amounts of training data and learn representations automatically rather than relying on manually designed features.
Key Insights Of Using Deep Learning To Analyze Healthcare Data | Workshop Fro...
In this presentation, I present how to properly discover, analyze and find trends in various types of healthcare data in order to utilize machine learning algorithms to predict future trends in the data. This presentation directly discusses the implications of data analysis in predicting benign and malignant cancers but the same techniques in this presentation can be applied to any other types of data in the real world.
For a more in-depth presentation, please watch the video presentation of this slideshow linked here: https://youtu.be/gXSl2iWcJ00
Machine learning with TensorFlow
This document discusses machine learning and TensorFlow. It begins with an introduction to modern machine intelligence and its real-world impact through examples like medical imaging. Next, it provides an overview of machine learning, moving to discuss TensorFlow and Google's use of machine learning in products like Translate, Photos, and Gmail. The document promotes free machine learning resources and ends with contact information for the presenter.
Automation, intelligence and knowledge modelling
Automation, intelligence and knowledge modelling,
My talk at http://web11.org/
Numerous talks, news articles and blog posts have been written about impact of recent advances in technology to our society. To a layman, it is all mix of "good news/bad news" show: from improvements in transport, agriculture or health, to jobs disappearing, or wealth inequality, just to name a few. But to techies like myself, the real question is somehow different: How far we can go?.
Machine learning || Introduction || Main Components || Examples || Techniques
This document discusses machine learning. It defines machine learning as designing algorithms that extract valuable information from data. It states that machine learning is useful for problems where existing solutions require fine-tuning or many rules, complex problems traditional methods can't solve, and environments that are changing. Examples of machine learning tasks are given, such as image classification using convolutional neural networks, detecting tumors using convolutional neural networks, automatically classifying news articles using natural language processing, and creating chatbots or personal assistants using natural language processing and question-answering modules.
100_Days_of_Data_Science
The document provides guidance for days 90-100 of a 100 Days of Data Science Challenge, suggesting focusing on reviewing and revising one's progress during this time. It recommends reviewing goals and objectives, reflecting on strengths and challenges, reviewing completed project work, seeking feedback, and revising one's project plan based on learnings and feedback to stay motivated and on track, and identify areas for continued focus and improvement.
Deep learning: Cutting through the Myths and Hype
The field of Artificial Intelligence (AI) has been revitalized in this decade, primarily due to the large-scale application of Deep Learning (DL) and other Machine Learning (ML) algorithms. This has been most evident in applications like computer vision, natural language processing, and game bots. However, extraordinary successes within a short period of time have also had the unintended consequence of causing a sharp difference of opinion in research and industrial communities regarding the capabilities and limitations of deep learning. A few questions you might have heard being asked (or asked yourself) include:
a. We don’t know how Deep Neural Networks make decisions, so can we trust them?
b. Can Deep Learning deal with highly non-linear continuous systems with millions of variables?
c. Can Deep Learning solve the Artificial General Intelligence problem?
The goal of this seminar is to provide a 1000-feet view of Deep Learning and hopefully answer the questions above. The seminar will touch upon the evolution, current state of the art, and peculiarities of Deep Learning, and share thoughts on using Deep Learning as a tool for developing power system solutions.
Similar to Transfer learning with Custom Vision (20)
Distributed Models Over Distributed Data with MLflow, Pyspark, and Pandas
Distributed Models Over Distributed Data with MLflow, Pyspark, and Pandas
Fine-tuning Pre-Trained Models for Generative AI Applications
Fine-tuning Pre-Trained Models for Generative AI Applications
II-SDV 2017: The Next Era: Deep Learning for Biomedical Research
II-SDV 2017: The Next Era: Deep Learning for Biomedical Research
Designing a neural network architecture for image recognition
Designing a neural network architecture for image recognition
Transfer Learning: Breve introducción a modelos pre-entrenados.
Transfer Learning: Breve introducción a modelos pre-entrenados.
Python Machine Learning January 2018 - Ho Chi Minh City
Python Machine Learning January 2018 - Ho Chi Minh City
GDSC Unsri - From Zero to Hero with TensorFlow.pptx
GDSC Unsri - From Zero to Hero with TensorFlow.pptx
Key Insights Of Using Deep Learning To Analyze Healthcare Data | Workshop Fro...
Key Insights Of Using Deep Learning To Analyze Healthcare Data | Workshop Fro...
Machine learning || Introduction || Main Components || Examples || Techniques
Machine learning || Introduction || Main Components || Examples || Techniques
More from SC5.io
AWS Machine Learning & Google Cloud Machine Learning
This document provides an overview and comparison of machine learning services from AWS and Google Cloud. It begins with introductions of the speaker and agenda. It then provides background on machine learning and the three main types (supervised, unsupervised, reinforcement learning). It discusses how cloud services can provide on-demand compute for machine learning. It gives a breakdown of specific machine learning services from Google Cloud (such as Cloud ML Engine, Vision, Translation) and AWS (such as Machine Learning, Lex, Rekognition). It provides an example of pricing cloud infrastructure. Finally, it demonstrates building a multi-class classifier on the Iris dataset using logistic regression with both Google Cloud ML Engine and AWS Machine Learning.
Practical AI for Business: Bandit Algorithms
This document provides an overview of bandit algorithms and their applications. It begins by explaining the multi-armed bandit problem and some basic algorithms like epsilon-greedy to solve it. It then discusses more advanced techniques like Thompson sampling and their benefits over naive approaches. Finally, it outlines several real-world uses of bandit algorithms, including UI optimization, recommendation systems, and multiplayer games. Bandit algorithms provide a powerful way to optimize outcomes in situations where rewards are not immediately revealed.
Decision trees & random forests
The document discusses decision trees and random forests machine learning algorithms. It explains that decision trees are simple algorithms that can be used for classification and regression problems. Random forests improve on decision trees by constructing multiple trees on randomly sampled data and averaging their predictions, which helps prevent overfitting issues. The document provides examples of how decision trees are constructed and how random forests make predictions through voting or averaging tree predictions.
Bandit Algorithms
This document discusses bandit algorithms and their applications. It begins with an overview of multi-armed bandit problems and explores various algorithms to solve them, including naive, epsilon-greedy, and Thompson sampling algorithms. Thompson sampling is shown to have logarithmic regret compared to linear regret for other algorithms. The document then discusses contextual bandits, which incorporate context into the problem to select the best arm given context, and adversarial contextual bandits, where payoffs may change. Real-world applications of bandits include medical treatments, testing, optimization, and recommendations. Contextual bandits can be implemented using APIs, databases, and services. Bandit algorithms can solve many problems modeled as games.
Machine Learning Using Cloud Services
This document provides an overview of machine learning and how cloud services can help with machine learning projects. It defines machine learning and describes the main types (supervised, unsupervised, reinforcement learning). It then discusses how the cloud helps with the main parts of a machine learning workflow: fetching and preparing data using cloud data warehousing, training models using GPUs and cloud-based computation, and deploying models using serverless functions and APIs. It also mentions some pre-built AI services like IBM Watson and Amazon AI.
Angular.js Primer in Aalto University
AngularJS training provides an overview of key AngularJS concepts and best practices for building Angular applications. The document introduces the trainer, Lauri Svan, and discusses AngularJS fundamentals like two-way data binding, dependency injection, templates, controllers and directives. It also outlines the typical structure of an Angular app, including modules, services and routing. Form validation, custom directives and asynchronous validation with ngModelOptions are also covered to demonstrate common Angular patterns and techniques.
Miten design-muutosjohtaminen hyödyttää yrityksiä?
Miten design-muutosjohtamisella voidaan tuoda suunnitteluajatus koko yrityksen toimintaan ja rakentaa menestyksekkäämpiä palvelumalleja?
SC5:n Head of Designin Juho Paasosen esitys ICTexpossa 23.4.2015.
Securing the client side web
While application security will always be an application space problem that's ultimately up to programmers to solve, new techniques in modern browsers can help mitigate vulnerability surface area when bugs enter the playing field unnoticed. Besides the obvious transport level security provided by HTTPS, CSP and Sandboxed Iframes provide solid mechanisms for setting rules to help the browser help you.
Engineering HTML5 Applications for Better Performance
One second page loads, 100ms UI response time and 60fps animations - even if today's browsers are super fast, meeting these performance goals can be tricky, particularly for mobile browsers.
This presentation outlines some tools & techniques to help to design web apps with performance in mind.
2013 10-02-backbone-robots-aarhus
SC5 and A-Lehdet just launched a responsive and adaptive website for Tuulilasi, the Finnish media authority for tech reviews of cars and other gadgets. This website relies heavily on search engine traffic, so we needed to support server side rendering. On the other hand we really wanted to use a single page app, but we didn't want to duplicate efforts.
This talk shows why the ability to share code between the browser and the server is the best thing since sliced bread. We leveraged this possibility to develop a platform that allows us to run the same application in both environments, ultimately achieving a single page app that also caters to robots.
2013 10-02-html5-performance-aarhus
Engineering HTML5 Applications for Better Performance - a presentation held by Lauri Svan / SC5 at Goto Aarhus International Software Development Conference.
Traditional web services are considered to be slow. The new APIs introduced by HTML5 applications do not automatically perform any better, but provide means for writing them in the same way as native applications.
We suggest to engineer the HTML5 applications the same ways as your native applications. That is, measure them using the same performance metrics, and solving some of the performance bottlenecks in the same ways as you would do for a native app.
2013 04-02-server-side-backbone
This document discusses using a proxy server to render single page applications for search engines and legacy browsers. It introduces the concept of a server-side backbone that runs the same backbone application code on both the client and server. This allows rendering the initial HTML on the server to avoid problems with robots and speeds up loading for users. It also discusses some of the challenges in implementing this approach like emulating the DOM and browser APIs on the server. Overall it presents server-side rendering as a way to solve crawlability and legacy browser support problems for single page apps.
Building single page applications
A Lecture given in Aalto University course "Design of WWW Services".
Single page app is already several years old web application paradigm that is now gaining traction due to the interest towards HTML5 and particularly cross-platform mobile (web) applications. The presentation overviews the single page application paradigm and compares it with other web app paradigms.
The presentation uses Backbone.js as the sample and gives practical tips on how to best structure Backbone.js applications. It contains an extensive set of tips and links in the notes section.
The reader is adviced to download the presentation for better readability of the notes.
More from SC5.io (13)
AWS Machine Learning & Google Cloud Machine Learning
AWS Machine Learning & Google Cloud Machine Learning
Miten design-muutosjohtaminen hyödyttää yrityksiä?
Miten design-muutosjohtaminen hyödyttää yrityksiä?
Engineering HTML5 Applications for Better Performance
Engineering HTML5 Applications for Better Performance
Recently uploaded
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
State of Artificial intelligence Report 2023
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
学校原件一模一样【微信：741003700 】《(英国UCA毕业证书)创意艺术大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
毕业原版【微信:41543339】【(牛布毕业证书)牛津布鲁克斯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Recently uploaded (20)
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
Global Situational Awareness of A.I. and where its headed
Global Situational Awareness of A.I. and where its headed
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
Transfer learning with Custom Vision
- 1. TRANSFER LEARNING Max Pagels, Data Science Specialist @maxpagels, linkedin.com/in/maxpagels/
- 2. MACHINE LEARNING NEEDS DATA TO WORK The more, the better. But what if you don’t have a lot of data to work with, and no time to collect more? Is ML still an option?
- 3. IN SOME CASES, YES. For general regression and classification problems such as face detection and image recognition, there are lots (but still not enough) of freely available datasets online that you can use to train your own models.
- 4. MNIST 70,000 28x28px labelled images of handwritten digits 0-9
- 5. FASHION MNIST 70,000 28x28px labelled images of clothing items (ten classes)
- 6. VGG FACE DATASET Portrait images of 2,622 identities
- 7. CIFAR-10 60,000 32x32px colour images belonging to one of ten classes (airplane, automobile, cat, deer etc.)
- 8. CIFAR-100 60,000 32x32px colour images belonging to one of a hundred different classes
- 9. GTD Data from more than 170,000 terrorist attacks worldwide, 1970-2016
- 10. FOOD 101 101,000 images of 101 types of food items
- 11. YOUTUBE-8M Over 7 million YouTube videos across 4716 classes
- 12. LOTS MORE ON KAGGLE https://www.kaggle.com/datasets
- 13. INSTEAD OF TRAINING MODELS YOURSELF, YOU CAN ALSO USE PRE-TRAINED ONES If you use a well known dataset such as MNIST or CIFAR-10, chances are someone’s already trained a neural network, random forest, or some other learning algorithm on it with high accuracy.
- 14. BUT WHAT IF WE HAVE A SPECIFIC PROBLEM FOR WHICH NO PRE-TRAINED MODEL EXISTS? Can we somehow leverage the information learned by some pre-trained model and adapt it to our specific use case?
- 15. EXAMPLE Say we want to build a food classifier that reliably determine if an image contains one of two Finnish dishes: fried baltic herring or fried perch? A model pre-trained on Food 101 has learned a lot about what a fish looks like, but the dataset doesn’t include images of fried baltic herring or fried perch. Could we use the wisdom the model has learned about fish in order to make building our custom model easier?
- 16. YES, AND IT’S CALLED TRANSFER LEARNING The basic idea is to take a pre-trained model that has already learning useful representations and tailor it to a custom use case.
- 17. TRANSFER LEARNING, IN A NUTSHELL Say you have a pre-trained neural network that you would like to adapt to your use case. To do transfer learning: 1. Chop off the neurons in the last layer of the model 2. Replace neurons with new ones, one neuron per class you want to predict (or just one neuron in total if doing regression) 3. Finish training using a dataset of labelled images specific to your problem Why would you want to do this?
- 18. ONE MAJOR REASON You need less labelled data. A lot less. Usually, 50-100 labelled examples is more than enough.
- 19. HOW DOES ONE IMPLEMENT TRANSFER LEARNING? You can do transfer learning quite easily in code using Keras, MXNet, TensorFlow, CNTK etc: download pre-trained model, freeze weights, remove last layer, add new layer. There are, however, ready-made, zero-coding-required cloud services that can do it for you.
- 20. CUSTOMVISION.AI DEMO Transfer learning as a service
- 21. THANK YOU! Special thanks to Ari Bornstein from Microsoft for the inspiration! QUESTIONS?