Feature mining from movies to improve content recommendation at ShowMax streaming service.
Lear more about ShowMax Engineering at https://tech.showmax.com/about/
El narcotráfico genera mayores ingresos que actividades legales para campesinos y subsidia cultivos ilícitos. Los grandes carteles causan problemas económicos e inestabilidad en los países debido al dinero ilegal. La corrupción de autoridades por las ganancias del narcotráfico socava la capacidad de los gobiernos para lograr metas económicas y debilita la seguridad nacional.
An Overview of User Acceptance Testing (UAT)Usersnap
What is User Acceptance Testing? Also known as UAT or UAT testing.
it's basically, a process of verifying that a solution works for the user.
And the key word here, is user. This is crucial, because they’re the people who will use the software on a daily basis. There are many aspects to consider with respect to software functionality. There’s unit testing, functional testing, integration testing, and system testing, amongst many others.
What Is User Acceptance Testing?
I’ll keep it simple; according to Techopedia, UAT (some people call it UAT testing as well) is:
User acceptance testing (UAT) is the last phase of the software testing process. During UAT, actual software users test the software to make sure it can handle required tasks in real-world scenarios, according to specifications. UAT is one of the final and critical software project procedures that must occur before newly developed software is rolled out to the market.
User acceptance testing (UAT), otherwise known as Beta, Application, or End-User Testing, is often considered the last phase in the web development process, the one before final installation of the software on the client site, or final distribution of it.
Real Time Object Dectection using machine learningpratik pratyay
This document discusses the development of a real-time object detection system using computer vision techniques. It aims to recognize and label moving objects in video streams from monitoring cameras with high accuracy and in a short amount of time. The system will use a hybrid model of convolutional neural networks and support vector machines for feature extraction and classification of objects from camera feeds into predefined classes. It is intended to help analyze surveillance video by only flagging clips that contain objects of interest like people or vehicles, reducing wasted storage and review time.
Majella ELOBO EKASSI is a machine learning engineer with 3 years of experience. He has a Master's Degree in Machine Learning and a Bachelor's Degree in Computer Science. He has worked on several projects involving image processing, deep learning, and deploying models for production use. His skills include developing, training, and optimizing machine learning and deep learning models using tools like Python, Caffe, Keras, and TensorFlow.
This document summarizes a project on real-time object detection using computer vision techniques. It discusses using a system that can recognize objects in a video stream from a camera and label them with bounding boxes and labels. It notes that most video surveillance footage is uninteresting unless there are moving objects. The project aims to address this by building an accurate, fast object detection system that can run on resource-constrained devices. It proposes using a hybrid CNN-SVM model trained on a large dataset to recognize objects and discusses the training and detection phases of the system.
Deep neural networks can be used for object detection and segmentation. Convolutional neural networks (CNNs) are specifically designed for image processing tasks. CNNs apply filters across an image in a sliding window manner and use max pooling to reduce dimensionality. Modern CNNs have hundreds of layers and are trained on large datasets like ImageNet for classification. For object detection, CNNs can generate bounding boxes and classify objects within them using approaches like YOLO, SSD, R-CNN, and Mask R-CNN. While requiring large labeled datasets, synthetic data generation can provide pixel-perfect labels to train detection models at a large scale.
This document describes a student project to detect object movement using a webcam. The project uses Python and OpenCV libraries. Key steps include capturing video frames, comparing frames to detect differences indicating movement, and highlighting the moving regions. The project aims to build a low-cost motion detection system for home users. It analyzes image frames to detect movement and displays the regions in separate color, gray, delta, and threshold frames for clear understanding.
El narcotráfico genera mayores ingresos que actividades legales para campesinos y subsidia cultivos ilícitos. Los grandes carteles causan problemas económicos e inestabilidad en los países debido al dinero ilegal. La corrupción de autoridades por las ganancias del narcotráfico socava la capacidad de los gobiernos para lograr metas económicas y debilita la seguridad nacional.
An Overview of User Acceptance Testing (UAT)Usersnap
What is User Acceptance Testing? Also known as UAT or UAT testing.
it's basically, a process of verifying that a solution works for the user.
And the key word here, is user. This is crucial, because they’re the people who will use the software on a daily basis. There are many aspects to consider with respect to software functionality. There’s unit testing, functional testing, integration testing, and system testing, amongst many others.
What Is User Acceptance Testing?
I’ll keep it simple; according to Techopedia, UAT (some people call it UAT testing as well) is:
User acceptance testing (UAT) is the last phase of the software testing process. During UAT, actual software users test the software to make sure it can handle required tasks in real-world scenarios, according to specifications. UAT is one of the final and critical software project procedures that must occur before newly developed software is rolled out to the market.
User acceptance testing (UAT), otherwise known as Beta, Application, or End-User Testing, is often considered the last phase in the web development process, the one before final installation of the software on the client site, or final distribution of it.
Real Time Object Dectection using machine learningpratik pratyay
This document discusses the development of a real-time object detection system using computer vision techniques. It aims to recognize and label moving objects in video streams from monitoring cameras with high accuracy and in a short amount of time. The system will use a hybrid model of convolutional neural networks and support vector machines for feature extraction and classification of objects from camera feeds into predefined classes. It is intended to help analyze surveillance video by only flagging clips that contain objects of interest like people or vehicles, reducing wasted storage and review time.
Majella ELOBO EKASSI is a machine learning engineer with 3 years of experience. He has a Master's Degree in Machine Learning and a Bachelor's Degree in Computer Science. He has worked on several projects involving image processing, deep learning, and deploying models for production use. His skills include developing, training, and optimizing machine learning and deep learning models using tools like Python, Caffe, Keras, and TensorFlow.
This document summarizes a project on real-time object detection using computer vision techniques. It discusses using a system that can recognize objects in a video stream from a camera and label them with bounding boxes and labels. It notes that most video surveillance footage is uninteresting unless there are moving objects. The project aims to address this by building an accurate, fast object detection system that can run on resource-constrained devices. It proposes using a hybrid CNN-SVM model trained on a large dataset to recognize objects and discusses the training and detection phases of the system.
Deep neural networks can be used for object detection and segmentation. Convolutional neural networks (CNNs) are specifically designed for image processing tasks. CNNs apply filters across an image in a sliding window manner and use max pooling to reduce dimensionality. Modern CNNs have hundreds of layers and are trained on large datasets like ImageNet for classification. For object detection, CNNs can generate bounding boxes and classify objects within them using approaches like YOLO, SSD, R-CNN, and Mask R-CNN. While requiring large labeled datasets, synthetic data generation can provide pixel-perfect labels to train detection models at a large scale.
This document describes a student project to detect object movement using a webcam. The project uses Python and OpenCV libraries. Key steps include capturing video frames, comparing frames to detect differences indicating movement, and highlighting the moving regions. The project aims to build a low-cost motion detection system for home users. It analyzes image frames to detect movement and displays the regions in separate color, gray, delta, and threshold frames for clear understanding.
This document summarizes a presentation on analyzing and optimizing virtual machine images through fragmentation. Some key points:
- VM images are currently stored whole in cloud repositories, even if they share common parts like operating system files. The goal is to store common parts once and reassemble images on demand from unique and common fragments.
- There are design decisions around the fragmentation approach, comparison method, and assembly algorithm to implement. The solution presented uses file system-level comparison and assembles fragments sequentially at boot time.
- Performance testing showed the approach achieved up to 83% faster image delivery, up to 88% smaller images, and up to 86% less storage compared to goals of 25%, 60%, and 80%
The document summarizes the progress and future work of a video searching application that uses automatic annotation. It analyzes video structure by fragmenting videos into frames and identifying duplicates. Deep learning is used to detect objects in images by training a model on categorized image data. Semantic textual searching tokenizes user queries and matches them to an ontology and database to return relevant video results. Future work includes optimizing code, improving techniques for frame analysis, transferring models to proto buffers, automatically categorizing videos, and analyzing query relationships.
The document introduces various computer vision topics including convolutional neural networks, popular CNN architectures, data augmentation, transfer learning, object detection, neural style transfer, generative adversarial networks, and variational autoencoders. It provides overviews of each topic and discusses concepts such as how convolutions work, common CNN architectures like ResNet and VGG, why data augmentation is important, how transfer learning can utilize pre-trained models, how object detection algorithms like YOLO work, the content and style losses used in neural style transfer, how GANs use generators and discriminators, and how VAEs describe images with probability distributions. The document aims to discuss these topics at a practical level and provide insights through examples.
The document describes a Bucharest Big Data Meetup occurring on June 5th. The meetup will include two tech talks: one on productionizing machine learning from 7:00-7:40 PM, and another on a technology comparison of databases vs blockchains from 7:40-8:15 PM. The meetup will conclude from 8:15-8:45 PM with pizza and drinks sponsored by Netopia.
This document discusses Tippett Studio's use of Python to develop a new visual effects and animation pipeline called JET. Tippett Studio is an Academy Award-winning visual effects company that employs over 200 artists. They developed JET to replace their outdated pipeline. JET uses Python to rapidly develop a cross-platform, modular pipeline tool with a dynamic user interface. Key aspects of JET include chunk templates that perform pipeline tasks, batch job script generation, and UI templates to customize interfaces for different artist roles. JET provides Tippett Studio with a flexible, customizable and scalable pipeline to efficiently create computer-generated imagery.
This document summarizes a presentation on deep image processing and computer vision. It introduces common deep learning techniques like CNNs, autoencoders, variational autoencoders and generative adversarial networks. It then discusses applications including image classification using models like LeNet, AlexNet and VGG. It also covers face detection, segmentation, object detection algorithms like R-CNN, Fast R-CNN and Faster R-CNN. Additional topics include document automation using character recognition and graphical element analysis, as well as identity recognition using face detection. Real-world examples are provided for document processing, handwritten letter recognition and event pass verification.
The document provides an overview of machine learning use cases. It begins with an agenda that will discuss the basic framework for ML projects, model deployment options, and various ML use cases like text classification, image classification, object detection, etc. It then covers the basic 5 step framework for ML projects - defining the problem, planning the solution, acquiring and preparing data, designing and training a model, and deploying the solution. Next, it discusses popular methods for various tasks like image classification, object detection, pose estimation. Finally, it shares several use cases for each task to demonstrate real-world applications.
Tom and Spike classifier using TensorFlow Object Detection. Presentation slides of the meetup TFOD conducted on 17/11/2018 at Algoscale Technologies Inc.
The document discusses video classification using deep neural networks. It provides an overview of video classification and how it is similar to image classification. It then discusses early neural networks like McCulloch-Pitts neurons and perceptrons that were inspired by the human brain. It moves on to explain convolutional neural networks and popular CNN models like LeNet, AlexNet, VGGNet, and GoogleNet that were important for video and image classification. The document also discusses object detection methods like R-CNN, Fast R-CNN, and Faster R-CNN and the single stage detector SSD. Key concepts discussed include anchor boxes, intersection over union, and the SSD architecture.
This document discusses early collaborative features being developed for the ResearchSpace (RS) project. It covers:
- An overview of previous and upcoming workshops covering search features, data annotation, and more.
- New team members joining the project.
- Plans to allow users to build collections from search results by copying, creating new collections, or adding to existing collections.
- Questions about terminology for collections and handling relationships between different collection types.
Introduction to computer vision with Convoluted Neural NetworksMarcinJedyk
Introduction to computer vision with Convoluted Neural Networks - going over history of CNNs, describing basic concepts such as convolution and discussing applications of computer vision and image recognition technologies
Synthesizing pseudo 2.5 d content from monocular videos for mixed realityNAVER Engineering
Free-viewpoint video (FVV) is a kind of advanced media that provides a more immersive user experience than traditional media. It allows users to interact with content because users can view media at the desired viewpoint and is becoming a next-generation media.
In creating FVV content, existing systems require complex and specialized capturing equipment and has low end-user usability because it needs a lot of expertise to use the system. This becomes an inconvenience for individuals or small organizations who want to create content and limits the end user’s ability to create FVV-based user-generated content (UGC) and inhibits the creation and sharing of various created content.
To tackle these problems, ParaPara is proposed in this work. ParaPara is an end-to-end system that uses a simple yet effective method to generate pseudo-2.5D FVV content from monocular videos, unlike the previously proposed systems. First, the system detects persons from the monocular video through a deep neural network, calculates the real-world homography matrix based on the minimal user interaction, and estimates the pseudo-3D positions of the detected persons. Then, person textures are extracted using general image processing algorithms and placed at the estimated real-world positions. Finally, the pseudo-2.5D content is synthesized from these elements. The content, which is synthesized by the proposed system, is implemented on Microsoft HoloLens; the user can freely place the generated content on the real world and watch it on a free viewpoint.
AWS re:Invent 2016: Deep Learning at Cloud Scale: Improving Video Discoverabi...Amazon Web Services
Deep learning continues to push the state of the art in domains such as video analytics, computer vision, and speech recognition. Deep networks are powered by amazing levels of representational power, feature learning, and abstraction. This approach comes at the cost of a significant increase in required compute power, which makes the AWS cloud an excellent environment for training. Innovators in this space are applying deep learning to a variety of applications. One such innovator, Vilynx, a startup based in Palo Alto, realized that the current pre-roll advertising-based models for mobile video weren’t returning publishers' desired levels of engagement. In this session, we explain the algorithmic challenges of scaling across multiple nodes, and what Intel is doing on AWS to overcome them. We describe the benefits of using AWS CloudFormation to set up a distributed training environment for deep networks. We also showcase Vilynx’s contributions to video discoverability, and explain how Vilynx uses AWS tools to understand video content. This session is sponsored by Intel.
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...StampedeCon
In this session, we’ll discuss approaches for applying convolutional neural networks to novel computer vision problems, even without having millions of images of your own. Pretrained models and generic image data sets from Google, Kaggle, universities, and other places can be leveraged and adapted to solve industry and business specific problems. We’ll discuss the approaches of transfer learning and fine tuning to help anyone get started on using deep learning to get cutting edge results on their computer vision problems.
This document provides an introduction to computer vision with convoluted neural networks. It discusses what computer vision aims to address, provides a brief overview of neural networks and their basic building blocks. It then covers the history and evolution of convolutional neural networks, how and why they work on digital images, their limitations, and applications like object detection. Examples are provided of early CNNs from the 1980s and 1990s and recent advancements through the 2010s that improved accuracy, including deeper networks, inception modules, residual connections, and efforts to increase performance like MobileNets. Training deep CNNs requires large datasets and may take weeks, but pre-trained networks can be fine-tuned for new tasks.
This document discusses single shot instance segmentation using a Siamese network as the backbone. It describes the problem of data scarcity when building models with large datasets and high accuracy. The proposed algorithm could help identify objects in large images more efficiently by using a reference image. The technical requirements, dataset, and references are provided. The plan is to implement this algorithm for smart kitchen applications to easily find products in images.
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
In the “Sharing is caring” spirit, we came up with a series of internal talks called, By Showmaxers, for Showmaxers, and we recently started making them public. There are already talks about Networks, and Android app building, available.
Our latest talk focuses on PostgreSQL Terminology, and is led by Angus Dippenaar. He worked on Showmax projects from South Africa, and moved to work with us in Prague, Czech Republic.
The talk was meant to fill some holes in our knowledge of PostgreSQL. So, it guides you through the basic PostgreSQL terminology you need to understand when reading the official documentation and blogs.
You may learn what all these POstgreSQL terms mean:
Command, query, local or global object, non-schema local objects, relation, tablespace, database, database cluster, instance and its processes like postmaster or backend; session, connection, heap, file segment, table, TOAST, tuple, view, materialized (view), transaction, commit, rollback, index, write-ahead log, WAL record, WAL file, checkpoint, Multi-version concurrency control (MVCC), dead tuples (dead rows), or transaction exhaustion.
The terminology is followed by a demonstration of transaction exhaustion.
Get the complete explanation and see the demonstration of the transaction exhaustion and of tuple freezing in the talk on YouTube: https://youtu.be/E-RkI3Ws7gM.
Štefan Šafár, Showmax CDN Engineer, gives a very useful presentation on Network fundamentals. As he notes, the talk was meant for anyone who gets anxious about setting up networks.
Štefan tailored the talk for beginners and programmers who see networks as magical things wrapped in mystery. You’ll see, when you understand the technology that lies behind networks, all of a sudden, it all seems easy.
Check out the the recording from the live talk as well.
More Related Content
Similar to Deep learning features and similarity of movies based on their video content
This document summarizes a presentation on analyzing and optimizing virtual machine images through fragmentation. Some key points:
- VM images are currently stored whole in cloud repositories, even if they share common parts like operating system files. The goal is to store common parts once and reassemble images on demand from unique and common fragments.
- There are design decisions around the fragmentation approach, comparison method, and assembly algorithm to implement. The solution presented uses file system-level comparison and assembles fragments sequentially at boot time.
- Performance testing showed the approach achieved up to 83% faster image delivery, up to 88% smaller images, and up to 86% less storage compared to goals of 25%, 60%, and 80%
The document summarizes the progress and future work of a video searching application that uses automatic annotation. It analyzes video structure by fragmenting videos into frames and identifying duplicates. Deep learning is used to detect objects in images by training a model on categorized image data. Semantic textual searching tokenizes user queries and matches them to an ontology and database to return relevant video results. Future work includes optimizing code, improving techniques for frame analysis, transferring models to proto buffers, automatically categorizing videos, and analyzing query relationships.
The document introduces various computer vision topics including convolutional neural networks, popular CNN architectures, data augmentation, transfer learning, object detection, neural style transfer, generative adversarial networks, and variational autoencoders. It provides overviews of each topic and discusses concepts such as how convolutions work, common CNN architectures like ResNet and VGG, why data augmentation is important, how transfer learning can utilize pre-trained models, how object detection algorithms like YOLO work, the content and style losses used in neural style transfer, how GANs use generators and discriminators, and how VAEs describe images with probability distributions. The document aims to discuss these topics at a practical level and provide insights through examples.
The document describes a Bucharest Big Data Meetup occurring on June 5th. The meetup will include two tech talks: one on productionizing machine learning from 7:00-7:40 PM, and another on a technology comparison of databases vs blockchains from 7:40-8:15 PM. The meetup will conclude from 8:15-8:45 PM with pizza and drinks sponsored by Netopia.
This document discusses Tippett Studio's use of Python to develop a new visual effects and animation pipeline called JET. Tippett Studio is an Academy Award-winning visual effects company that employs over 200 artists. They developed JET to replace their outdated pipeline. JET uses Python to rapidly develop a cross-platform, modular pipeline tool with a dynamic user interface. Key aspects of JET include chunk templates that perform pipeline tasks, batch job script generation, and UI templates to customize interfaces for different artist roles. JET provides Tippett Studio with a flexible, customizable and scalable pipeline to efficiently create computer-generated imagery.
This document summarizes a presentation on deep image processing and computer vision. It introduces common deep learning techniques like CNNs, autoencoders, variational autoencoders and generative adversarial networks. It then discusses applications including image classification using models like LeNet, AlexNet and VGG. It also covers face detection, segmentation, object detection algorithms like R-CNN, Fast R-CNN and Faster R-CNN. Additional topics include document automation using character recognition and graphical element analysis, as well as identity recognition using face detection. Real-world examples are provided for document processing, handwritten letter recognition and event pass verification.
The document provides an overview of machine learning use cases. It begins with an agenda that will discuss the basic framework for ML projects, model deployment options, and various ML use cases like text classification, image classification, object detection, etc. It then covers the basic 5 step framework for ML projects - defining the problem, planning the solution, acquiring and preparing data, designing and training a model, and deploying the solution. Next, it discusses popular methods for various tasks like image classification, object detection, pose estimation. Finally, it shares several use cases for each task to demonstrate real-world applications.
Tom and Spike classifier using TensorFlow Object Detection. Presentation slides of the meetup TFOD conducted on 17/11/2018 at Algoscale Technologies Inc.
The document discusses video classification using deep neural networks. It provides an overview of video classification and how it is similar to image classification. It then discusses early neural networks like McCulloch-Pitts neurons and perceptrons that were inspired by the human brain. It moves on to explain convolutional neural networks and popular CNN models like LeNet, AlexNet, VGGNet, and GoogleNet that were important for video and image classification. The document also discusses object detection methods like R-CNN, Fast R-CNN, and Faster R-CNN and the single stage detector SSD. Key concepts discussed include anchor boxes, intersection over union, and the SSD architecture.
This document discusses early collaborative features being developed for the ResearchSpace (RS) project. It covers:
- An overview of previous and upcoming workshops covering search features, data annotation, and more.
- New team members joining the project.
- Plans to allow users to build collections from search results by copying, creating new collections, or adding to existing collections.
- Questions about terminology for collections and handling relationships between different collection types.
Introduction to computer vision with Convoluted Neural NetworksMarcinJedyk
Introduction to computer vision with Convoluted Neural Networks - going over history of CNNs, describing basic concepts such as convolution and discussing applications of computer vision and image recognition technologies
Synthesizing pseudo 2.5 d content from monocular videos for mixed realityNAVER Engineering
Free-viewpoint video (FVV) is a kind of advanced media that provides a more immersive user experience than traditional media. It allows users to interact with content because users can view media at the desired viewpoint and is becoming a next-generation media.
In creating FVV content, existing systems require complex and specialized capturing equipment and has low end-user usability because it needs a lot of expertise to use the system. This becomes an inconvenience for individuals or small organizations who want to create content and limits the end user’s ability to create FVV-based user-generated content (UGC) and inhibits the creation and sharing of various created content.
To tackle these problems, ParaPara is proposed in this work. ParaPara is an end-to-end system that uses a simple yet effective method to generate pseudo-2.5D FVV content from monocular videos, unlike the previously proposed systems. First, the system detects persons from the monocular video through a deep neural network, calculates the real-world homography matrix based on the minimal user interaction, and estimates the pseudo-3D positions of the detected persons. Then, person textures are extracted using general image processing algorithms and placed at the estimated real-world positions. Finally, the pseudo-2.5D content is synthesized from these elements. The content, which is synthesized by the proposed system, is implemented on Microsoft HoloLens; the user can freely place the generated content on the real world and watch it on a free viewpoint.
AWS re:Invent 2016: Deep Learning at Cloud Scale: Improving Video Discoverabi...Amazon Web Services
Deep learning continues to push the state of the art in domains such as video analytics, computer vision, and speech recognition. Deep networks are powered by amazing levels of representational power, feature learning, and abstraction. This approach comes at the cost of a significant increase in required compute power, which makes the AWS cloud an excellent environment for training. Innovators in this space are applying deep learning to a variety of applications. One such innovator, Vilynx, a startup based in Palo Alto, realized that the current pre-roll advertising-based models for mobile video weren’t returning publishers' desired levels of engagement. In this session, we explain the algorithmic challenges of scaling across multiple nodes, and what Intel is doing on AWS to overcome them. We describe the benefits of using AWS CloudFormation to set up a distributed training environment for deep networks. We also showcase Vilynx’s contributions to video discoverability, and explain how Vilynx uses AWS tools to understand video content. This session is sponsored by Intel.
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...StampedeCon
In this session, we’ll discuss approaches for applying convolutional neural networks to novel computer vision problems, even without having millions of images of your own. Pretrained models and generic image data sets from Google, Kaggle, universities, and other places can be leveraged and adapted to solve industry and business specific problems. We’ll discuss the approaches of transfer learning and fine tuning to help anyone get started on using deep learning to get cutting edge results on their computer vision problems.
This document provides an introduction to computer vision with convoluted neural networks. It discusses what computer vision aims to address, provides a brief overview of neural networks and their basic building blocks. It then covers the history and evolution of convolutional neural networks, how and why they work on digital images, their limitations, and applications like object detection. Examples are provided of early CNNs from the 1980s and 1990s and recent advancements through the 2010s that improved accuracy, including deeper networks, inception modules, residual connections, and efforts to increase performance like MobileNets. Training deep CNNs requires large datasets and may take weeks, but pre-trained networks can be fine-tuned for new tasks.
This document discusses single shot instance segmentation using a Siamese network as the backbone. It describes the problem of data scarcity when building models with large datasets and high accuracy. The proposed algorithm could help identify objects in large images more efficiently by using a reference image. The technical requirements, dataset, and references are provided. The plan is to implement this algorithm for smart kitchen applications to easily find products in images.
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
Similar to Deep learning features and similarity of movies based on their video content (20)
In the “Sharing is caring” spirit, we came up with a series of internal talks called, By Showmaxers, for Showmaxers, and we recently started making them public. There are already talks about Networks, and Android app building, available.
Our latest talk focuses on PostgreSQL Terminology, and is led by Angus Dippenaar. He worked on Showmax projects from South Africa, and moved to work with us in Prague, Czech Republic.
The talk was meant to fill some holes in our knowledge of PostgreSQL. So, it guides you through the basic PostgreSQL terminology you need to understand when reading the official documentation and blogs.
You may learn what all these POstgreSQL terms mean:
Command, query, local or global object, non-schema local objects, relation, tablespace, database, database cluster, instance and its processes like postmaster or backend; session, connection, heap, file segment, table, TOAST, tuple, view, materialized (view), transaction, commit, rollback, index, write-ahead log, WAL record, WAL file, checkpoint, Multi-version concurrency control (MVCC), dead tuples (dead rows), or transaction exhaustion.
The terminology is followed by a demonstration of transaction exhaustion.
Get the complete explanation and see the demonstration of the transaction exhaustion and of tuple freezing in the talk on YouTube: https://youtu.be/E-RkI3Ws7gM.
Štefan Šafár, Showmax CDN Engineer, gives a very useful presentation on Network fundamentals. As he notes, the talk was meant for anyone who gets anxious about setting up networks.
Štefan tailored the talk for beginners and programmers who see networks as magical things wrapped in mystery. You’ll see, when you understand the technology that lies behind networks, all of a sudden, it all seems easy.
Check out the the recording from the live talk as well.
Learn how to develop an AndroidApp from a senior developer — for free! We decided to make one of our “Showmaxers teaching Showmaxers” events public. This one is from our Android developer Michal Ursiny. Check it out.
What you will learn and do:
- Introduction to Android development and what it takes to develop for Android - it’s actually pretty easy to start compared to other mobile platforms
- Java vs Kotlin - you can use both, but we recommend Kotlin
- How to create new project using Android Studio, the official IDE for Android development
- How to choose the appropriate minimum SDK version
- Understanding basic project structure:
sources
resources
AndroidManifest.xml
build.gradle
- You will run the demo project generated by Android Studio and modify it
- The basic building blocks:
Activity
Fragment
View
- How to build basic layouts using resources and themes
- The challenges - lifecycles and why to use viewmodels
- Permissions - how to access REST APIs using Retrofit library and why using third party image libraries is a good idea
Getting started
Download Android Studio - the official IDE based on IntelliJ IDEA. Configure your emulator or enable developer mode on your device and connect to the computer. Get acquainted with Android Studio.
Originally, the sample project used within the tutorial was targeting our internal Showmax Search API. It was changed to use GitHub Users Search API so it’s available and useful for everyone.
On our blog on https://tech.showmax.com/2021/02/android-crashcourse/ you can watch Michal’s easy-to-digest and comprehensive presentation embedded from YouTube.
Or just read the deck and learn the basics.
Try building the app yourself by following the shared sample project: https://github.com/Showmax/GithubUsersSearch
The presentation introduces tools and best practices that will help you introduce the SRE principles for PostgreSQL monitoring.
What to expect:
- An introduction to tools and best practices that will help you introduce SRE principles for PostgreSQL monitoring
- We define the four SRE “golden signals,” and explain how they’re used in PostgreSQL
- A relatively-detailed look at our software stack used for monitoring
- A look at high-level differences between traditional monitoring stacks and modern solutions
- An introduction to Prometheus and how it works with PostgreSQL
- Specific examples of Alertmanager rules for alerting
- A comprehensive walkthrough of Grafana dashboards
- Details on our logging pipeline, including our ELK stack and a few other Showmax specialties
GraphQL is getting more and more popular for developing web services. The traditional approach is to upgrade your backend service and then connect your frontend to it. We did it the other way around, starting with frontend and adding the backend later. This talk is about why and how we did it, so you can do it too.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
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
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
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.
State of Artificial intelligence Report 2023kuntobimo2016
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.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
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.
Challenges of Nation Building-1.pptx with more important
Deep learning features and similarity of movies based on their video content
1. Deep learning features and
similarity of movies based on their
video content
Summer Camp - Show Max - Lukáš Lopatovský
2. Assignment
● Deep learning allows extracting useful features from video
frames. Your task is to apply new deep learning frameworks
to extract features from video frames of selected movies
available in the ShowMax streaming platform.
● Goals:
● Extract deep features from video frames. Explore similar
movies in the space of latent features and adjust the
extraction process in order to create clusters of video assets
(eg. TV episodes).
3. Residual Networks
● Enable to build deeper (convolutional) neural
network. (State of the art method for the image
recognition.)
4. Residual Networks
● To enable to build deeper network, the residual
nets use the simple trick. They maintain the
residuum from the previous layer ( so do not
loose the previously known information )
5. Torch
- Efficient Tensor library (like NumPy) with an
efficient CUDA backend
- Neural Networks package -- build arbitrary
acyclic computation graphs with automatic
differentiation
- fast CUDA and CPU backends
- Good community and industry support - several
hundred community-built and maintained
packages.
7. What has been done
● The movies were classified using arbitrary
number of picture frames.
● We have used already trained ImageNet FB-
resnet network and own data set trained and
fine-tuned networks to classify movies.
● To detect the object in the image, we have
classify the whole image, as well as we have
made the various crops to get more accurate
predictions. (Cropping showed better results)
8. Classification output
● By classification of frames in the movie, the
special file is produced (.res). It is in a form to
contain all the important data. It can be later post-
process according to the special needs of the user:
- To create Object detection .srt file.
- To get various cumulative classification results.
- To trace the appearance of the object at the time-
line.
15. Own datasets
● The network was successfully trained and fine-tuned
from the ResNet network
● However, it showed some problems based from
improper dataset.
- Some categories contain many irrelevant pictures in second half
of the search. (Special case: “The doctor House”)
- The style of the images in the search is often very different to the
style found in the movie. (kitchen, car)
- Movies mostly contain images full of people, so the categories
containing people make false positive prediction. (cinema, theater)
23. Next step
● After the discussion in the company, the
programs were transformed to the easily usable
form.
● The feature vectors of the classification will be
used to find similarities among movies.
Compared to the existing algorithms and if
successful, incorporated into the current
recommendation system.