Gary Hope - Machine Learning: It's Not as Hard as you ThinkSaratoga
Gary Hope is currently the Data Platform Technical Specialist within Microsoft South Africa having previously worked for several large organisations including American Express and Siemens Business Solutions.
Slides from talks presented at Mammoth BI in Cape Town on 17 November 2014.
Visit www.mammothbi.co.za for details on the event. Follow @MammothBI on twitter.
The document discusses using deep learning for object recognition. It proposes building an app using the ImageNet dataset that can recognize objects with 80% accuracy without internet or heavy processing. The app would continue learning to improve over time and detect multiple objects in an image, providing probability scores if uncertain. The ImageNet dataset is a large visual dataset used for object classification and detection challenges involving millions of images across hundreds of categories.
Data science is an interdisciplinary field (it consists of more than one branch of study) that uses statistics, computer science, and machine learning algorithms to gain insights from structured and unstructured data. CETPA INFOTECH, an ISO 9001- 2008 certified training company provides Data Science Training Course for students and professionals who want to make their mark in the world of Data Science. Cetpa is the best data science training institute in Delhi NCR.
There are several areas where AI can be applied, including expert systems, natural language processing, neural systems, robotics, and gaming systems. AI is also used in a number of everyday applications such as smart cars, security cameras, fraud detection, news story generation, customer service, video games, predictive purchasing, work automation, smart recommendations, smart homes, virtual assistants, preventing heart attacks, preserving wildlife, search and rescue, and cybersecurity. Machine learning techniques like supervised learning, unsupervised learning, and reinforcement learning are important methods for developing AI systems.
Machine learning applications nurturing growth of various business domainsShrutika Oswal
Machine learning is a science in which machines are becoming smarter and helping humans to make the best decisions based on previous data recommended practices. This technique is not new but is occupying fresh momentum. Machine Learning Algorithm learns from the previous records and analyses the data. Without any human interrupt, it will generate its own recommendation. A machine will add that recommendation as experience in its database and use it for further processing. In short, the machine learns from its own experience and gives you better and better output.
Machine learning is an iterative process as the more data added to machines learn from fresh feeds of data and then independently adapt new features to handle new data without constant human intervention. Machine learning was earlier used to predict what’s happing with the business but now the machine learning algorithm will suggest what action needs be taken by moving our business forward.
This PowerPoint presentation presents the results of a literature survey of machine learning applications nurturing the growth of various business domains. More specifically, it gives a brief introduction of Machine Learning, four major types of Machine Learning, enhancement in various business domains by the use of various machine learning algorithms.
Artificial intelligence is a field of computer science that creates intelligent systems that can act like humans. It involves machine learning algorithms that allow systems to learn from data and make predictions without being explicitly programmed. Business intelligence is a set of processes and technologies that analyzes historical data to provide insights and information to support business decision making. It involves extracting, transforming, and loading data into data warehouses where it can be visualized through reports, dashboards, and data analysis. Machine learning is a key subset of artificial intelligence that uses algorithms to learn from data and make predictions without being explicitly programmed. It is used in applications like recommender systems, speech recognition, and self-driving cars.
Gary Hope - Machine Learning: It's Not as Hard as you ThinkSaratoga
Gary Hope is currently the Data Platform Technical Specialist within Microsoft South Africa having previously worked for several large organisations including American Express and Siemens Business Solutions.
Slides from talks presented at Mammoth BI in Cape Town on 17 November 2014.
Visit www.mammothbi.co.za for details on the event. Follow @MammothBI on twitter.
The document discusses using deep learning for object recognition. It proposes building an app using the ImageNet dataset that can recognize objects with 80% accuracy without internet or heavy processing. The app would continue learning to improve over time and detect multiple objects in an image, providing probability scores if uncertain. The ImageNet dataset is a large visual dataset used for object classification and detection challenges involving millions of images across hundreds of categories.
Data science is an interdisciplinary field (it consists of more than one branch of study) that uses statistics, computer science, and machine learning algorithms to gain insights from structured and unstructured data. CETPA INFOTECH, an ISO 9001- 2008 certified training company provides Data Science Training Course for students and professionals who want to make their mark in the world of Data Science. Cetpa is the best data science training institute in Delhi NCR.
There are several areas where AI can be applied, including expert systems, natural language processing, neural systems, robotics, and gaming systems. AI is also used in a number of everyday applications such as smart cars, security cameras, fraud detection, news story generation, customer service, video games, predictive purchasing, work automation, smart recommendations, smart homes, virtual assistants, preventing heart attacks, preserving wildlife, search and rescue, and cybersecurity. Machine learning techniques like supervised learning, unsupervised learning, and reinforcement learning are important methods for developing AI systems.
Machine learning applications nurturing growth of various business domainsShrutika Oswal
Machine learning is a science in which machines are becoming smarter and helping humans to make the best decisions based on previous data recommended practices. This technique is not new but is occupying fresh momentum. Machine Learning Algorithm learns from the previous records and analyses the data. Without any human interrupt, it will generate its own recommendation. A machine will add that recommendation as experience in its database and use it for further processing. In short, the machine learns from its own experience and gives you better and better output.
Machine learning is an iterative process as the more data added to machines learn from fresh feeds of data and then independently adapt new features to handle new data without constant human intervention. Machine learning was earlier used to predict what’s happing with the business but now the machine learning algorithm will suggest what action needs be taken by moving our business forward.
This PowerPoint presentation presents the results of a literature survey of machine learning applications nurturing the growth of various business domains. More specifically, it gives a brief introduction of Machine Learning, four major types of Machine Learning, enhancement in various business domains by the use of various machine learning algorithms.
Artificial intelligence is a field of computer science that creates intelligent systems that can act like humans. It involves machine learning algorithms that allow systems to learn from data and make predictions without being explicitly programmed. Business intelligence is a set of processes and technologies that analyzes historical data to provide insights and information to support business decision making. It involves extracting, transforming, and loading data into data warehouses where it can be visualized through reports, dashboards, and data analysis. Machine learning is a key subset of artificial intelligence that uses algorithms to learn from data and make predictions without being explicitly programmed. It is used in applications like recommender systems, speech recognition, and self-driving cars.
Artificial Intelligence: Evolution and its Impact on MarketingZenith
In one real-life minute, Google receives over 4 million searches, 2.5 million pieces of content are shared on Facebook, and Pandora users listen to 61 thousand hours of music. The amount of data that is produced in a day is massive that the world has began to turn to artificial intelligence to make use of this data. Read here to learn about the way that artificial intelligence is revolutionizing the use of big data and how this will impact the world of marketing and business.
A complete brief introduction and importance on Data Science, Data Analytics, Business Analytics, Tools used for Analytics, Artificial Intelligence and Machine Learning.
This Presentation is brief Introduction to Data Analytics and carrier in it. It is with respect to the webinar which took place on 6 th March - Link https://www.youtube.com/watch?v=ltPi1680d1s
The document discusses data science, defining it as a multi-disciplinary field that uses scientific methods to extract knowledge and insights from structured and unstructured data. It notes data science employs techniques from fields like statistics, computer science, and information science. The document outlines related fields like statistics, machine learning, and artificial intelligence. It provides examples of data science tasks at companies and lists applications of data science in various domains like security, banking, healthcare, and transportation. Finally, it discusses the importance of data science for understanding customers and its growing role across many sectors.
The document discusses simplifying an analytics strategy. It recommends accelerating data through a hybrid technology environment to enable faster insight and decision making. A bank adopted this approach to more efficiently manage increasing data volumes for customer analytics. It also discusses delegating work to technologies like business intelligence, data discovery, analytics applications, and machine learning to analyze data and produce predictions. A company's existing culture and technologies impact its analytics journey.
This document provides an introduction and overview of data science. It defines data science as the field that uses scientific processes and algorithms to extract knowledge and insights from data. It describes data scientists as applying machine learning to structure and unstructured data to build AI systems. The document outlines typical data science processes and discusses different types of data scientists, including those focused on humans and machines. It explains why data science is important for businesses to increase the value of their data and help with decisions, customers, and processes. Finally, it provides a demo of a data science application.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Deep learning vs ML vs AI vs DS
Machine learning enables computers to learn from data without being explicitly programmed, and can be classified as supervised, unsupervised, or reinforcement learning. Deep learning uses neural networks to learn representations of data, and is a type of machine learning. Artificial intelligence is the overarching concept of machines being able to carry out tasks in a way that mimics human intelligence. Data science involves extracting insights from data through techniques like analytics and modeling, and encompasses the processes of machine and deep learning. While related, each term has a distinct definition regarding the methodology used.
This document provides an overview of AlgoAnalytics, an analytics consultancy company that uses advanced machine learning techniques. The summary is as follows:
(1) AlgoAnalytics provides predictive analytics solutions for retail, healthcare, financial services, and other industries using techniques like deep learning, natural language processing, and computer vision on structured, text, image and sound data.
(2) The CEO and founder, Aniruddha Pant, has over 20 years of experience applying mathematical techniques to business problems. Some of AlgoAnalytics' work includes recommender systems, demand prediction, image analysis, and customer churn prevention for online retail.
(3) Examples of AlgoAnalytics' predictive models shown include an
Machine Learning: Addressing the Disillusionment to Bring Actual Business Ben...Jon Mead
'Machine learning’ is one of those cringy phrases, almost (if not already) taboo in the world of high-tech SaaS. Applying true machine learning to an organization’s product(s), however, can have real benefit for the business, its clients, and the industry as a whole. From credit card fraud investigations to the way that a car is built, machine learning has permeated our everyday life without a common understanding of what it is and how to implement it.
AI in Insurance: How to Automate Insurance Claim Processing with Machine Lear...Skyl.ai
About the webinar
Insurance companies are looking at technology to solve complexity created by presence of cumbersome processes and presence of multiple entities like actuaries, support team and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
What you'll learn
- How Insurance companies are using ML to drive more efficiency and business gain
- Best practices to automate machine learning models
- Demo: A deeper understanding of the end-to-end machine learning workflow for car damage recognition using Skyl.ai
The Next Big Thing - Data Driven Applications by T.M. Ravi, Founder of The HiveThe Hive
This document discusses the rise of data-driven applications and how they are changing businesses. It outlines how applications are being developed across different areas like back office automation, front office productivity, mobile, social, and more. It also discusses how different types of data like machine data, unstructured data, and data from the internet of things are fueling new applications in industries like retail, transportation, healthcare, and manufacturing. Finally, it discusses how data science techniques are being used to build applications that provide insights, predictions, and optimizations to help organizations make better decisions.
This document discusses emerging technology trends in social analytics and predictive analysis. It provides examples of how companies like Ford, IBM, and SportingIndex are using predictive technology and big data analytics in sports to analyze historical data and predict outcomes. Social media analytics tools like ThisMoment and Sprout Social are also discussed that help analyze social marketing efforts and monitor social media conversations. The document emphasizes the importance of data visualization and interactive data formats to effectively communicate insights from large and complex data sets.
Ai in insurance how to automate insurance claim processing with machine lear...Skyl.ai
Explore more at https://skyl.ai/form?p=start-trial
About the webinar
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support team and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
Estudio34 InAppEvent - How to Reach Your App Promotion KPIs with GoogleWilliam Renedo
This document provides a summary of a presentation about growing mobile apps with Google services. It discusses trends showing continued growth in the mobile app market and challenges developers face in user acquisition and retention. Google's Universal App Campaigns and Firebase analytics platform are presented as solutions. UAC allows developers to reach users across Google properties with one campaign and optimize for high lifetime value users. Firebase provides analytics and integrates with other Google tools to help developers better understand users and improve their mobile apps.
Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor flow, IBM watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science role. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python,Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.
https://www.learntek.org/machine-learning-using-spark/
https://www.learntek.org
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
This document discusses simplifying analytics strategies. It recommends pursuing a simpler path to insights by accelerating data through a hybrid technology environment. This allows for fast delivery of analytics to improve service quality. It also recommends delegating work to analytics technologies like business intelligence and data visualization to more easily uncover patterns. Different analytic techniques like data discovery, applications, and machine learning can further help companies gain insights from their data in a simplified manner. The path to insights is unique for each company based on their goals, data, and technologies.
Infosearch offers several digital accelerators and services to help companies with their AI projects, including evolving AI initiatives with ongoing feedback, well-trained computer-aided annotation to label large amounts of data, and prioritizing issues through gamification to keep teams interested and motivated. They have experience providing annotated datasets using various annotation tools and formats for applications such as image recognition and autonomous vehicles. Outsourcing annotation to Infosearch can help businesses set up and run complex AI projects more efficiently.
Ai design sprint - Finance - Wealth managementChinmay Patel
Chinmay Patel presented an AI design sprint methodology. The methodology involves identifying a business problem, gathering and preparing relevant data, training and deploying a model, and maintaining/improving the model over time. As an example, Chinmay discussed how this process was used to build an automated claim resolution bot that can resolve claims within 3 seconds with no paperwork. The methodology was also proposed for a wealth management use case to perform user segmentation using clustering algorithms.
Artificial Intelligence: Evolution and its Impact on MarketingZenith
In one real-life minute, Google receives over 4 million searches, 2.5 million pieces of content are shared on Facebook, and Pandora users listen to 61 thousand hours of music. The amount of data that is produced in a day is massive that the world has began to turn to artificial intelligence to make use of this data. Read here to learn about the way that artificial intelligence is revolutionizing the use of big data and how this will impact the world of marketing and business.
A complete brief introduction and importance on Data Science, Data Analytics, Business Analytics, Tools used for Analytics, Artificial Intelligence and Machine Learning.
This Presentation is brief Introduction to Data Analytics and carrier in it. It is with respect to the webinar which took place on 6 th March - Link https://www.youtube.com/watch?v=ltPi1680d1s
The document discusses data science, defining it as a multi-disciplinary field that uses scientific methods to extract knowledge and insights from structured and unstructured data. It notes data science employs techniques from fields like statistics, computer science, and information science. The document outlines related fields like statistics, machine learning, and artificial intelligence. It provides examples of data science tasks at companies and lists applications of data science in various domains like security, banking, healthcare, and transportation. Finally, it discusses the importance of data science for understanding customers and its growing role across many sectors.
The document discusses simplifying an analytics strategy. It recommends accelerating data through a hybrid technology environment to enable faster insight and decision making. A bank adopted this approach to more efficiently manage increasing data volumes for customer analytics. It also discusses delegating work to technologies like business intelligence, data discovery, analytics applications, and machine learning to analyze data and produce predictions. A company's existing culture and technologies impact its analytics journey.
This document provides an introduction and overview of data science. It defines data science as the field that uses scientific processes and algorithms to extract knowledge and insights from data. It describes data scientists as applying machine learning to structure and unstructured data to build AI systems. The document outlines typical data science processes and discusses different types of data scientists, including those focused on humans and machines. It explains why data science is important for businesses to increase the value of their data and help with decisions, customers, and processes. Finally, it provides a demo of a data science application.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Deep learning vs ML vs AI vs DS
Machine learning enables computers to learn from data without being explicitly programmed, and can be classified as supervised, unsupervised, or reinforcement learning. Deep learning uses neural networks to learn representations of data, and is a type of machine learning. Artificial intelligence is the overarching concept of machines being able to carry out tasks in a way that mimics human intelligence. Data science involves extracting insights from data through techniques like analytics and modeling, and encompasses the processes of machine and deep learning. While related, each term has a distinct definition regarding the methodology used.
This document provides an overview of AlgoAnalytics, an analytics consultancy company that uses advanced machine learning techniques. The summary is as follows:
(1) AlgoAnalytics provides predictive analytics solutions for retail, healthcare, financial services, and other industries using techniques like deep learning, natural language processing, and computer vision on structured, text, image and sound data.
(2) The CEO and founder, Aniruddha Pant, has over 20 years of experience applying mathematical techniques to business problems. Some of AlgoAnalytics' work includes recommender systems, demand prediction, image analysis, and customer churn prevention for online retail.
(3) Examples of AlgoAnalytics' predictive models shown include an
Machine Learning: Addressing the Disillusionment to Bring Actual Business Ben...Jon Mead
'Machine learning’ is one of those cringy phrases, almost (if not already) taboo in the world of high-tech SaaS. Applying true machine learning to an organization’s product(s), however, can have real benefit for the business, its clients, and the industry as a whole. From credit card fraud investigations to the way that a car is built, machine learning has permeated our everyday life without a common understanding of what it is and how to implement it.
AI in Insurance: How to Automate Insurance Claim Processing with Machine Lear...Skyl.ai
About the webinar
Insurance companies are looking at technology to solve complexity created by presence of cumbersome processes and presence of multiple entities like actuaries, support team and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
What you'll learn
- How Insurance companies are using ML to drive more efficiency and business gain
- Best practices to automate machine learning models
- Demo: A deeper understanding of the end-to-end machine learning workflow for car damage recognition using Skyl.ai
The Next Big Thing - Data Driven Applications by T.M. Ravi, Founder of The HiveThe Hive
This document discusses the rise of data-driven applications and how they are changing businesses. It outlines how applications are being developed across different areas like back office automation, front office productivity, mobile, social, and more. It also discusses how different types of data like machine data, unstructured data, and data from the internet of things are fueling new applications in industries like retail, transportation, healthcare, and manufacturing. Finally, it discusses how data science techniques are being used to build applications that provide insights, predictions, and optimizations to help organizations make better decisions.
This document discusses emerging technology trends in social analytics and predictive analysis. It provides examples of how companies like Ford, IBM, and SportingIndex are using predictive technology and big data analytics in sports to analyze historical data and predict outcomes. Social media analytics tools like ThisMoment and Sprout Social are also discussed that help analyze social marketing efforts and monitor social media conversations. The document emphasizes the importance of data visualization and interactive data formats to effectively communicate insights from large and complex data sets.
Ai in insurance how to automate insurance claim processing with machine lear...Skyl.ai
Explore more at https://skyl.ai/form?p=start-trial
About the webinar
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support team and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
Estudio34 InAppEvent - How to Reach Your App Promotion KPIs with GoogleWilliam Renedo
This document provides a summary of a presentation about growing mobile apps with Google services. It discusses trends showing continued growth in the mobile app market and challenges developers face in user acquisition and retention. Google's Universal App Campaigns and Firebase analytics platform are presented as solutions. UAC allows developers to reach users across Google properties with one campaign and optimize for high lifetime value users. Firebase provides analytics and integrates with other Google tools to help developers better understand users and improve their mobile apps.
Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor flow, IBM watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science role. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python,Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.
https://www.learntek.org/machine-learning-using-spark/
https://www.learntek.org
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
This document discusses simplifying analytics strategies. It recommends pursuing a simpler path to insights by accelerating data through a hybrid technology environment. This allows for fast delivery of analytics to improve service quality. It also recommends delegating work to analytics technologies like business intelligence and data visualization to more easily uncover patterns. Different analytic techniques like data discovery, applications, and machine learning can further help companies gain insights from their data in a simplified manner. The path to insights is unique for each company based on their goals, data, and technologies.
Infosearch offers several digital accelerators and services to help companies with their AI projects, including evolving AI initiatives with ongoing feedback, well-trained computer-aided annotation to label large amounts of data, and prioritizing issues through gamification to keep teams interested and motivated. They have experience providing annotated datasets using various annotation tools and formats for applications such as image recognition and autonomous vehicles. Outsourcing annotation to Infosearch can help businesses set up and run complex AI projects more efficiently.
Ai design sprint - Finance - Wealth managementChinmay Patel
Chinmay Patel presented an AI design sprint methodology. The methodology involves identifying a business problem, gathering and preparing relevant data, training and deploying a model, and maintaining/improving the model over time. As an example, Chinmay discussed how this process was used to build an automated claim resolution bot that can resolve claims within 3 seconds with no paperwork. The methodology was also proposed for a wealth management use case to perform user segmentation using clustering algorithms.
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
06-20-2024-AI Camp Meetup-Unstructured Data and Vector DatabasesTimothy Spann
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
2. Data Science
Data science is an interdisciplinary
field that uses scientific methods,
processes, algorithms and systems
to extract knowledge and insights
from data in various forms, both
structured and unstructured, similar
to data mining.
3. Data Science Application
Using data science, companies have become intelligent enough to push & sell products as per
customers purchasing power & interest. Here’s how they are ruling our hearts and minds:
• Internet Search
• Digital Advertisements
(Targeted Advertising and re-targeting)
• Recommender Systems
• Image Recognition
• Speech Recognition
• Gaming
• Price Comparison Websites
• Airline Route Planning
• Fraud and Risk Detection
• Delivery logistics
• Self Driving Cars
• Robots
4. Internet Search: Data science algorithms to deliver the best result for our searched query in fraction of
seconds. Considering the fact that, Google processes more than 20 petabytes of data everyday.
5. Recommender Systems: A lot of companies have fervidly used this engine / system to promote their products
/ suggestions in accordance with user’s interest and relevance of information. Internet giants like Amazon,
Twitter, Google Play, Netflix, Linkedin, imdb and many more uses this system to improve user experience.
6. Image Recognition:
You upload your image with friends on
Facebook and you start getting suggestions to
tag your friends. This automatic tag suggestion
feature uses face recognition algorithm.
Teaching machines to see
Empowering educators and
students
Improving iris recognition
7.
8. Gaming Systems: EA Sports, Zynga, Sony, Nintendo, Activision-Blizzard have led gaming experience to the
next level using data science. Games are now designed using machine learning algorithms which improve /
upgrade themselves as the player moves up to a higher level. A great example is Alpha Go.
9. Delivery logistics: Who says data science has limited applications? Logistic companies like DHL, FedEx, UPS,
Kuhne+Nagel have used data science to improve their operational efficiency.
11. Healthcare Applications:
Managing Medical Records and Other Data
Doing Repetitive Jobs
Virtual Nurses
Drug Creation
Precision Medicine
Health Monitoring