In this TensorFlow tutorial, you will be learning all the basics of TensorFlow and how to create a Deep Learning Model. It includes the following topics:
1. Deep Learning vs Machine Learning
2. What is TensorFlow?
3. TensorFlow Use-Case
TensorFlow and Keras are popular deep learning frameworks. TensorFlow is an open source library for numerical computation using data flow graphs. It was developed by Google and is widely used for machine learning and deep learning. Keras is a higher-level neural network API that can run on top of TensorFlow. It focuses on user-friendliness, modularization and extensibility. Both frameworks make building and training neural networks easier through modular layers and built-in optimization algorithms.
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial For Be...Simplilearn
This presentation on TensorFlow will help you in understanding what exactly is TensorFlow and how it is used in Deep Learning. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this tutorial, you will learn the fundamentals of TensorFlow concepts, functions, and operations required to implement deep learning algorithms and leverage data like never before. This TensorFlow tutorial is ideal for beginners who want to pursue a career in Deep Learning. Now, let us deep dive into this TensorFlow tutorial and understand what TensorFlow actually is and how to use it.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning Libraries
3. Why TensorFlow?
4. What is TensorFlow?
5. What are Tensors?
6. What is a Data Flow Graph?
7. Program Elements in TensorFlow
8. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
This document introduces Apache Cassandra, a distributed column-oriented NoSQL database. It discusses Cassandra's architecture, data model, query language (CQL), and how to install and run Cassandra. Key points covered include Cassandra's linear scalability, high availability and fault tolerance. The document also demonstrates how to use the nodetool utility and provides guidance on backing up and restoring Cassandra data.
This Edureka Recurrent Neural Networks tutorial will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story
Below are the topics covered in this tutorial:
1. Why Not Feedforward Networks?
2. What Are Recurrent Neural Networks?
3. Training A Recurrent Neural Network
4. Issues With Recurrent Neural Networks - Vanishing And Exploding Gradient
5. Long Short-Term Memory Networks (LSTMs)
6. LSTM Use-Case
The Business Case for Semantic Web Ontology & Knowledge GraphCambridge Semantics
This document discusses how semantic web ontologies and knowledge graphs can help reduce high IT costs by providing a common schema and linking data across systems. It introduces AnzoGraph DB, a graph database built on semantic web standards that can perform both analytics and graph algorithms on large datasets. The document demonstrates how public flight delay data can be converted to a knowledge graph and analyzed using techniques like PageRank, shortest paths, and querying for delayed flights. Overall, it argues that semantic technologies can help address the problem of data integration costs by enabling linked and standardized data.
Training Week: Create a Knowledge Graph: A Simple ML Approach Neo4j
This document provides an overview of creating a knowledge graph using machine learning approaches. It discusses using natural language processing to extract entities, relationships, and triples from text to build a knowledge graph. It then describes using graph embedding techniques like word2vec and node2vec to vectorize the knowledge graph and perform machine learning tasks like node similarity. The document demonstrates these approaches using Python libraries for NLP, graph databases, and machine learning.
Deep learning is a branch of machine learning that uses neural networks with multiple processing layers to learn representations of data with multiple levels of abstraction. It has been applied to problems like image recognition, natural language processing, and game playing. Deep learning architectures like deep neural networks use techniques like pretraining, dropout, and early stopping to avoid overfitting. Popular deep learning frameworks and libraries include TensorFlow, Keras, and PyTorch.
This document provides an introduction and overview of Apache Hive. It discusses how Hive originated at Facebook to manage large amounts of data stored in Oracle databases. It then defines what Hive is, how it works by compiling SQL queries into MapReduce jobs, and its architecture. Key components of Hive like its data model, metastore, and commands for creating tables and loading data are summarized.
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
The document is a presentation about TensorFlow. It begins with an introduction that defines machine learning and deep learning. It then discusses what TensorFlow is, including that it is an open-source library for deep learning and ML, was developed by Google Brain, and uses data flow graphs to represent computations. The presentation explains benefits of TensorFlow like parallelism, distributed execution, and portability. It provides examples of companies using TensorFlow and demonstrates cool projects that can be built with it, like image classification, object detection, and speech recognition. Finally, it concludes that TensorFlow is helping achieve amazing advancements in machine learning.
The document describes the sequence-to-sequence (seq2seq) model with an encoder-decoder architecture. It explains that the seq2seq model uses two recurrent neural networks - an encoder RNN that processes the input sequence into a fixed-length context vector, and a decoder RNN that generates the output sequence from the context vector. It provides details on how the encoder, decoder, and training process work in the seq2seq model.
The document provides an overview of convolutional neural networks (CNNs) and their layers. It begins with an introduction to CNNs, noting they are a type of neural network designed to process 2D inputs like images. It then discusses the typical CNN architecture of convolutional layers followed by pooling and fully connected layers. The document explains how CNNs work using a simple example of classifying handwritten X and O characters. It provides details on the different layer types, including convolutional layers which identify patterns using small filters, and pooling layers which downsample the inputs.
Slides from PyData Berlin, July 2017 meetup
Plotly Dash is a newest addition to a rich ecosystem of tools to build visual data science and BI applications in Python.
PyTorch vs TensorFlow: The Force Is Strong With Which One? | Which One You Sh...Edureka!
( ** Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka comparison PPT of "PyTorch vs TensorFlow" provides you with a detailed comparison between the top 2 Python Deep Learning Frameworks.
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TensorFlow is a popular machine learning library developed by Google. It allows highly scalable deep learning across many devices from data centers to phones. TensorFlow represents computations as data flow graphs with variables as tensors. This allows flexible and efficient training and deployment of models for tasks like image recognition, translation, and more.
TensorFlow Tutorial | Deep Learning With TensorFlow | TensorFlow Tutorial For...Simplilearn
The document provides an overview of using TensorFlow to build deep learning models. It discusses how TensorFlow uses computational graphs to process data and perform computations. Tensors represent multi-dimensional data and are core to TensorFlow's operations. The document also demonstrates how to build simple models like linear regression and recurrent neural networks (RNNs) using TensorFlow. An example RNN model predicts monthly milk production using time series data.
The document discusses representing knowledge in a graph for chatbots. It describes using natural language processing and information extraction to understand text and create a knowledge graph. The knowledge graph is used to power applications by extracting entities, detecting intents, and probabilistically traversing the graph to answer queries while considering the user context and conversation.
Deep learning lecture - part 1 (basics, CNN)SungminYou
This presentation is a lecture with the Deep Learning book. (Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. MIT press, 2017) It contains the basics of deep learning and theories about the convolutional neural network.
What Is Hadoop | Hadoop Tutorial For Beginners | EdurekaEdureka!
( Hadoop Training: https://www.edureka.co/hadoop )
This Edureka "What is Hadoop" tutorial ( Hadoop Blog series: https://goo.gl/LFesy8 ) helps you to understand how Big Data emerged as a problem and how Hadoop solved that problem. This tutorial will be discussing about Hadoop Architecture, HDFS & it's architecture, YARN and MapReduce in detail. Below are the topics covered in this tutorial:
1) 5 V’s of Big Data
2) Problems with Big Data
3) Hadoop-as-a solution
4) What is Hadoop?
5) HDFS
6) YARN
7) MapReduce
8) Hadoop Ecosystem
What is Artificial Intelligence | Artificial Intelligence Tutorial For Beginn...Edureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This tutorial on Artificial Intelligence gives you a brief introduction to AI discussing how it can be a threat as well as useful. This tutorial covers the following topics:
1. AI as a threat
2. What is AI?
3. History of AI
4. Machine Learning & Deep Learning examples
5. Dependency on AI
6.Applications of AI
7. AI Course at Edureka - https://goo.gl/VWNeAu
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Top 5 Deep Learning and AI Stories - October 6, 2017NVIDIA
Read this week's top 5 news updates in deep learning and AI: Gartner predicts top 10 strategic technology trends for 2018; Oracle adds GPU Accelerated Computing to Oracle Cloud Infrastructure; chemistry and physics Nobel Prizes are awarded to teams supported by GPUs; MIT uses deep learning to help guide decisions in ICU; and portfolio management firms are using AI to seek alpha.
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...Edureka!
This Edureka Big Data tutorial helps you to understand Big Data in detail. This tutorial will be discussing about evolution of Big Data, factors associated with Big Data, different opportunities in Big Data. Further it will discuss about problems associated with Big Data and how Hadoop emerged as a solution. Below are the topics covered in this tutorial:
1) Evolution of Data
2) What is Big Data?
3) Big Data as an Opportunity
4) Problems in Encasing Big Data Opportunity
5) Hadoop as a Solution
6) Hadoop Ecosystem
7) Edureka Big Data & Hadoop Training
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017Carol Smith
What is machine learning? Is UX relevant in the age of artificial intelligence (AI)? How can I take advantage of cognitive computing? Get answers to these questions and learn about the implications for your work in this session. Carol will help you understand at a basic level how these systems are built and what is required to get insights from them. Carol will present examples of how machine learning is already being used and explore the ethical challenges inherent in creating AI. You will walk away with an awareness of the weaknesses of AI and the knowledge of how these systems work.
ReactJS Tutorial For Beginners | ReactJS Redux Training For Beginners | React...Edureka!
This Edureka ReactJS Tutorial For Beginners will help you in understanding the fundamentals of ReactJS and help you in building a strong foundation in React framework. Below are the topics covered in this tutorial:
1. Why ReactJS?
2. What Is ReactJS?
3. Advantages Of ReactJS
4. ReactJS Installation and Program
5. ReactJS Fundamentals
Docker Swarm For High Availability | Docker Tutorial | DevOps Tutorial | EdurekaEdureka!
The document discusses Docker containers, Docker Swarm, and achieving high availability with Docker Swarm. It defines a Docker container as an isolated application platform containing everything needed to run an application. Docker Swarm is described as a technique to create and maintain a cluster of Docker engines to provide high availability, load balancing, and scaling of services. The document demonstrates setting up a Docker Swarm cluster with two nodes and deploying an Angular application across the nodes for high availability.
Angular 4 Data Binding | Two Way Data Binding in Angular 4 | Angular 4 Tutori...Edureka!
This Angular 4 tutorial will introduce you to the Angular Data Binding concept.
To watch the YouTube videos in this Angular 4 tutorial playlist, click here: https://www.youtube.com/watch?v=R4wGCHzn6-Q&list=PL9ooVrP1hQOF4aDuqaWYWSuj1isPF6HHg.
Not sure what to share on SlideShare?
SlideShares that inform, inspire and educate attract the most views. Beyond that, ideas for what you can upload are limitless. We’ve selected a few popular examples to get your creative juices flowing.
What Is DevOps? | Introduction To DevOps | DevOps Tools | DevOps Tutorial | D...Edureka!
In this Edureka Devops tutorial, you will learn what is DevOps, and why it is the most efficient software development methodology today. The following topics have been covered in this tutorial:
1. Software Development Challenges
2. How Does DevOps Minimize Challenges?
3. DevOps Tools & Techniques
4. Demand For DevOps Engineers
El documento presenta casos exitosos del negocio de agua y saneamiento de EPM. Brevemente describe a EPM como un grupo empresarial que presta servicios de agua, saneamiento, energía y gas. Luego, resume siete casos exitosos en el negocio de agua y saneamiento, incluyendo programas de habilitación de viviendas, prepago de agua, eficiencia energética, reducción de agua no contabilizada, saneamiento del río Medellín, gestión del drenaje urbano y gestión del riesgo. Finalmente, menc
El documento destaca la importancia de la Web 2.0 para la interacción entre empresas y usuarios, donde los usuarios aportan contenido. También señala que las plataformas sociales pueden desaparecer y que cumplen un rol integrador. Además, recomienda definir un plan estratégico con objetivos, acciones, tiempos y costos para crear marca, fidelizar clientes, generar visitas e investigar, además de medir logros mediante indicadores clave.
El documento describe el proyecto Unidades de Vida Articulada (UVA) de EPM, que transforma 12 tanques de almacenamiento de agua en Medellín en parques comunitarios. El objetivo es fortalecer el encuentro ciudadano a través de la recreación, cultura y participación. El proyecto ganó el premio Lafarge Holcim por su estrategia de mejorar la calidad de vida transformando la infraestructura de servicios públicos en espacios cívicos.
Las tecnologías de la información y la comunicación (TIC) han transformado notablemente la educación al cambiar tanto la forma de enseñar como de aprender. Ofrecen diversos recursos como material didáctico, entornos virtuales e internet que fomentan un aprendizaje significativo, activo y flexible. Además, permiten la simulación de fenómenos que los estudiantes pueden explorar sin riesgos y desarrollan el pensamiento crítico. Sin embargo, se requiere dotar a las instituciones de salas de informática funcionales
Este documento describe un grupo empresarial colombiano conformado por 44 empresas que prestan servicios de energía eléctrica, gas natural, agua potable, saneamiento básico, recolección de basuras y tecnologías de la información en Colombia, Centroamérica y España. El grupo fue fundado en 1955 y está liderado por Empresas Públicas de Medellín.
EPM busca promover el uso eficiente de la energía a través de campañas educativas y estudios. Las campañas han logrado reducir el consumo en hogares en un 8.9% al enseñar consejos de ahorro. Los estudios evalúan los impactos de intervenciones como capacitación, sustitución de electrodomésticos y mejoras locativas en el consumo y mora de usuarios. Además, EPM analiza cómo el prepago y la recarga de vehículos eléctricos pueden apoyar el uso eficiente.
Este documento presenta la situación actual de la empresa EPM, una empresa de servicios públicos en Colombia. Describe los objetivos del trabajo como dar a conocer la situación actual de EPM, sus objetivos estratégicos, e implementar objetivos SMART para seleccionar una actividad a innovar. También resume lo que hace EPM al proveer servicios como energía eléctrica y agua, y presenta su misión, visión y objetivos estratégicos.
This document outlines various software solutions for managing corporate performance, projects, assets, and business processes for organizations involved in designing, supplying, installing, constructing, maintaining, refurbishing and decommissioning facilities. It includes solutions for financial management, project management, asset management, human resources, health and safety, procurement, supply chain management, and plant operations optimization.
TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Py...Edureka!
This Edureka TensorFlow Tutorial (Blog: https://goo.gl/HTE7uB) will help you in understanding various important basics of TensorFlow. It also includes a use-case in which we will create a model that will differentiate between a rock and a mine using TensorFlow. Below are the topics covered in this tutorial:
1. What are Tensors?
2. What is TensorFlow?
3. TensorFlow Code-basics
4. Graph Visualization
5. TensorFlow Data structures
6. Use-Case Naval Mine Identifier (NMI)
Deep Learning Tutorial | Deep Learning Tutorial for Beginners | Neural Networ...Edureka!
This Edureka "Deep Learning Tutorial" (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. Single Layer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
YouTube Link: https://youtu.be/SpZSMvI-keU
** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This Edureka PPT will provide you with a crisp comparison between the two Deep Learning Frameworks - Theano and TensorFlow and will help you choose the right one for yourself.
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This is my Summer internship project presentation.I have Worked on total three projects and all the brief related details are provided in the presentation.
Thanks to Eckovation.
Introduction to Tensor Flow for Optical Character Recognition (OCR)Vincenzo Santopietro
This document discusses using TensorFlow for optical character recognition. It begins with introductions and contact information. It then provides overviews of machine learning and deep learning, describing how deep learning performs better than older ML algorithms with large amounts of data. It discusses supervised vs unsupervised learning and classification vs prediction problems. The rest of the document focuses on TensorFlow, describing how it builds and executes computation graphs across CPUs and GPUs, the lifecycle of node values, using placeholders to feed data, and saving/restoring models with checkpoints.
1. The document discusses feature engineering techniques for natural language processing (NLP) tasks. It describes 15 common features that can be extracted from text data like word counts, punctuation counts, part-of-speech counts.
2. The features are demonstrated on a Twitter dataset to classify tweets as real or fake news. Models trained with the engineered features achieved up to 4% higher accuracy than models without the features.
3. Feature engineering helps machine learning models better understand language contexts and meanings, leading to improved performance on NLP tasks compared to using models alone.
Workshop about TensorFlow usage for AI Ukraine 2016. Brief tutorial with source code example. Described TensorFlow main ideas, terms, parameters. Example related with linear neuron model and learning using Adam optimization algorithm.
The document discusses metrics and monitoring concepts, including the need for standardized, self-describing metrics to make them easier to understand, query, and work with. It provides examples of implementations that aim to structure metrics according to these principles by including descriptive tags and metadata alongside time-series data. The conclusion advocates adopting a "metrics 2.0" approach to gain benefits like reducing the manual effort required to interpret and use metrics for tasks like debugging and alerting.
Natural language processing open seminar For Tensorflow usagehyunyoung Lee
This is presentation for Natural Language Processing open seminar in Kookmin University.
The open seminar reference : https://cafe.naver.com/nlpk
My presentation about how to use tensorflow for NLP open seminar for newbies for tensorflow.
The document provides a summary of the key concepts and topics covered in a computer fundamentals course over the course of a semester. It discusses decimal, binary, octal, and hexadecimal number systems and how to convert between them. It also covers basic computer hardware components, software types, the structure and functions of computers, and flowcharts. The midterm exam covered HTML for creating web pages and database concepts. The final exam focused on SQL for querying and manipulating data in a database.
The document describes new features and enhancements in MySQL 8.0, including common table expressions, window functions, improved UTF-8 support, geospatial functions, new locking options for SELECT statements, JSON functions, index extensions, cost model improvements, query hints, and better support for IPv6 and UUID data types. The presentation agenda outlines each topic at a high level.
The document provides answers to common questions asked during SAS interviews or for SAS certification. Key points:
- The OUTPUT statement overrides automatic output in DATA steps and writes observations only when executed.
- The STOP statement stops processing the current DATA step and resumes after.
- There are differences between using the DROP= option in SET vs DATA statements and between reading from an external file vs existing dataset.
- Functions operate across observations while procedures operate within.
Certification Study Group -Professional ML Engineer Session 2 (GCP-TensorFlow...gdgsurrey
What We Will Discuss:
Reviewing progress in the machine learning certification journey
𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Lightening talk on Training an AI Voice Conversion Model Using Google Colab by Adam Berg
Content Review by Vasudev Maduri
Data Preparation and Processing
Solution Architecture with TensorFlow Extended (TFX)
Data Ingestion Challenges and Solutions
Sample Question Review
Previewing next steps and topics, including course completions and material reviews.
The document provides answers to common questions asked in SAS interviews or for SAS certification. Key points:
- The OUTPUT statement overrides automatic output in DATA steps and writes observations only when executed.
- The STOP statement stops processing the current DATA step and resumes after.
- DROP= in the SET statement drops variables from processing, while DROP= in the DATA statement drops them from the output dataset.
- The END= option reads the last observation of a dataset to a new dataset.
Deep Learning Introduction - WeCloudDataWeCloudData
This document provides an overview of machine learning and deep learning concepts including:
- Machine learning basics such as supervised vs. unsupervised learning and performance measures.
- A brief history of deep learning and basics such as neural networks.
- Linear algebra concepts from vectors to tensors that are important for machine learning.
- Specific machine learning algorithms including linear regression, logistic regression, and TensorFlow basics for defining and executing computation graphs.
Construindo Aplicações Deep Learning com TensorFlow e Amazon SageMaker - MCL...Amazon Web Services
Deep Learning continua a impulsionar o estado da arte em domínios como visão computacional, linguagem natural e mecanismos de recomendação. Nesta sessão, você irá aprender como começar a usar o framework de Deep Learning TensorFlow usando o Amazon SageMaker, uma plataforma para criar, treinar e implantar facilmente modelos em escala. Você aprende como criar um modelo usando o TensorFlow configurando um Notebook Jupyter para começar a efetuar reconhecimento de imagem e objeto. Você também aprende como treinar e implantar rapidamente um modelo por meio do Amazon SageMaker.
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete RecordAsst.prof M.Gokilavani
LIST OF EXPERIMENTS:
1. Implement simple vector addition in Tensor Flow.
2. Implement a regression model in Keras.
3. Implement a perception in TensorFlow/Keras Environment.
4. Implement a Feed Forward Network in TensorFlow/Keras.
5. Implement an image classifier using CNN in TensorFlow/Keras.
6. Improve the deep Learning model by fine tuning hyper parameters.
7. Implement a Transfer Learning concept in image classification.
8. Using a pre trained model on Keras for transfer learning.
9. Perform Sentimental Analysis using RNN.
10. Implement an LSTM based Auto encoding inTensorflow/Keras.
11. Image generation using GAN.
ADDITIONAL EXPERIMENTS
12. Train a deep Learning model to classify a given image using pre trained model.
13. Recommendation system from sales data using Deep Learning.
14. Implement Object detection using CNN.
15. Implement any simple Reinforcement Algorithm for an NLP problem.
Data centric Metaprogramming by Vlad UlrecheSpark Summit
- The document discusses optimizing data representations for performance by informing the compiler about data structures.
- A data-centric metaprogramming approach allows defining transformations that change how data types are represented, such as storing employee fields together rather than within a Vector.
- Transformations are defined through compiler plugins to automate the changes, rather than requiring manual code changes. Scopes ensure transformations only affect intended code.
Data Centric Metaprocessing by Vlad UlrecheSpark Summit
- The document discusses optimizing data representations for performance by informing the compiler about data structures.
- A data-centric metaprogramming approach allows defining transformations between representations, like mapping a Vector of Employees to a more efficient EmployeeVector.
- Transformations are specified once and applied automatically by the compiler throughout the specified scope.
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What to learn during the 21 days Lockdown | EdurekaEdureka!
Register Here: https://resources.edureka.co/21-days-learning-plan-webinar/
In light of the complete national lockdown for 21 days, we invite you to join a FREE webinar by renowned Mentor and Advisor, Nitin Gupta as he helps you create a 21-day learning gameplan to maximize returns for your career.
The webinar will help freshers and experienced professionals to capitalize on these 21 days and figure out the best technologies to learn while confined to home.
You will also get all your questions and doubts resolved in real-time.
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Top 10 Dying Programming Languages in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/LSM7hD6GM4M
Get Edureka Certified in Trending Programming Languages: https://www.edureka.co
In this highly competitive IT industry, everyone wants to learn programming languages that will keep them ahead of the game. But knowing what to learn so you gain the most out of your knowledge is a whole other ball game. So, we at Edureka have prepared a list of Top 10 Dying Programming Languages 2020 that will help you to make the right choice for your career. Meanwhile, if you ever wondered about which languages are slated for continuing uptake and possible greatness, we have a list for that, too.
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Top 5 Trending Business Intelligence Tools | EdurekaEdureka!
YouTube Link: https://youtu.be/eEwq_mPd1iI
Edureka BI Certification Training Courses: https://www.edureka.co/bi-and-visualization-certification-courses
Receiving insights and finding trends is absolutely critical for businesses to scale and adapt as the years go on. This is exactly what business intelligence does and the best thing about these software solutions is that their potential uses are practically unlimited.
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Tableau Tutorial for Data Science | EdurekaEdureka!
YouTube Link:https://youtu.be/ZHNdSKMluI0
Edureka Tableau Certification Training: https://www.edureka.co/tableau-certification-training
This Edureka's PPT on "Tableau for Data Science" will help you to utilize Tableau as a tool for Data Science, not only for engagement but also comprehension efficiency. Through this PPT, you will learn to gain the maximum amount of insight with the least amount of effort.
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Edureka Python Certification Training: https://www.edureka.co/data-science-python-certification-course
This Edureka PPT on 'Python Programming' will help you learn Python programming basics with the help of interesting hands-on implementations.
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Get Edureka Certified in Trending Project Management Certifications: https://www.edureka.co/project-management-and-methodologies-certification-courses
Whether you want to scale up your career or are trying to switch your career path, Project Management Certifications seems to be a perfect choice in either case. So, we at Edureka have prepared a list of Top 5 Project Management Certifications that you must check out in 2020 for a major career boost.
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Top Maven Interview Questions in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/5iTcAR4fScM
**DevOps Certification Courses - https://www.edureka.co/devops-certification-training***
This video on 'Maven Interview Questions' discusses the most frequently asked Maven Interview Questions. This PPT will help give you a detailed explanation of the topics which will help you in acing the interviews.
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** Linux Administration Certification Training - https://www.edureka.co/linux-admin **
Linux Mint is the first operating system that people from Windows or Mac are drawn towards when they have to switch to Linux in their work environment. Linux Mint has been around since the year 2006 and has grown and matured into a very user-friendly OS. Do watch the PPT till the very end to see all the demonstrations.
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How to Deploy Java Web App in AWS| EdurekaEdureka!
YouTube Link:https://youtu.be/Ozc5Yu_IcaI
** Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training**
This Edureka PPT shows how to deploy a java web application in AWS using AWS Elastic Beanstalk. It also describes the advantages of using AWS for this purpose.
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*** Edureka Digital Marketing Course: https://www.edureka.co/post-graduate/digital-marketing-certification***
This Edureka PPT on "Top 10 Reasons to Learn Digital Marketing" will help you understand why you should take up Digital Marketing
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** RPA Training: https://www.edureka.co/robotic-process-automation-training**
This PPT on RPA in 2020 will provide a glimpse of the accomplishments and benefits provided by RPA. Also, it will list out the new changes and technologies that will collaborate with RPA in 2020.
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**DevOps Certification Courses - https://www.edureka.co/devops-certification-training **
This PPT shows how to configure Jenkins to receive email notifications. It also includes a demo that shows how to do it in 6 simple steps in the Windows machine.
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EA Algorithm in Machine Learning | EdurekaEdureka!
YouTube Link: https://youtu.be/DIADjJXrgps
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training **
This Edureka PPT on 'EM Algorithm In Machine Learning' covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model.
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Cognitive computing aims to mimic human reasoning and behavior to solve complex problems. It works by simulating human thought processes through adaptive, interactive, iterative and contextual means. Cognitive computing supplements human decision making in sectors like customer service and healthcare, while artificial intelligence focuses more on autonomous decision making with applications in finance, security and more. A use case of cognitive AI is using it to assess skills, find relevant jobs, negotiate pay, suggest career paths and provide salary comparisons and job openings to help humans.
YouTube Link: https://youtu.be/0djPrlaxx_U
Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training
This Edureka PPT on AWS Cloud Practitioner will provide a complete guide to your AWS Cloud Practitioner Certification exam. It will explain the exam details, objectives, why you should get certified and also how AWS certification will help your career.
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Blue Prism Top Interview Questions | EdurekaEdureka!
YouTube Link: https://youtu.be/ykbRdUNIbyQ
** RPA Training: https://www.edureka.co/robotic-process-automation-certification-courses**
This PPT on Blue Prism Interview Questions will cover the Top 50 Blue Prism related questions asked in your interviews.
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AWS Architect Certification Training: https://www.edureka.co/aws-certification-training
This PPT will help you in understanding how AWS deals smartly with Big Data. It also shows how AWS can solve Big Data challenges with ease.
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A star algorithm | A* Algorithm in Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/amlkE0g-YFU
** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
This Edureka PPT on 'A Star Algorithm' teaches you all about the A star Algorithm, the uses, advantages and disadvantages and much more. It also shows you how the algorithm can be implemented practically and has a comparison between the Dijkstra and itself.
Check out our playlist for more videos: http://bit.ly/2taym8X
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Kubernetes Installation on Ubuntu | EdurekaEdureka!
YouTube Link: https://youtu.be/UWg3ORRRF60
Kubernetes Certification: https://www.edureka.co/kubernetes-certification
This Edureka PPT will help you set up a Kubernetes cluster having 1 master and 1 node. The detailed step by step instructions is demonstrated in this PPT.
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DevOps Training: https://www.edureka.co/devops-certification-training
This Edureka DevOps Tutorial for Beginners talks about What is DevOps and how it works. You will learn about several DevOps tools (Git, Jenkins, Docker, Puppet, Ansible, Nagios) involved at different DevOps stages such as version control, continuous integration, continuous delivery, continuous deployment, continuous monitoring.
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Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
Are you interested in dipping your toes in the cloud native observability waters, but as an engineer you are not sure where to get started with tracing problems through your microservices and application landscapes on Kubernetes? Then this is the session for you, where we take you on your first steps in an active open-source project that offers a buffet of languages, challenges, and opportunities for getting started with telemetry data.
The project is called openTelemetry, but before diving into the specifics, we’ll start with de-mystifying key concepts and terms such as observability, telemetry, instrumentation, cardinality, percentile to lay a foundation. After understanding the nuts and bolts of observability and distributed traces, we’ll explore the openTelemetry community; its Special Interest Groups (SIGs), repositories, and how to become not only an end-user, but possibly a contributor.We will wrap up with an overview of the components in this project, such as the Collector, the OpenTelemetry protocol (OTLP), its APIs, and its SDKs.
Attendees will leave with an understanding of key observability concepts, become grounded in distributed tracing terminology, be aware of the components of openTelemetry, and know how to take their first steps to an open-source contribution!
Key Takeaways: Open source, vendor neutral instrumentation is an exciting new reality as the industry standardizes on openTelemetry for observability. OpenTelemetry is on a mission to enable effective observability by making high-quality, portable telemetry ubiquitous. The world of observability and monitoring today has a steep learning curve and in order to achieve ubiquity, the project would benefit from growing our contributor community.
Performance Budgets for the Real World by Tammy EvertsScyllaDB
Performance budgets have been around for more than ten years. Over those years, we’ve learned a lot about what works, what doesn’t, and what we need to improve. In this session, Tammy revisits old assumptions about performance budgets and offers some new best practices. Topics include:
• Understanding performance budgets vs. performance goals
• Aligning budgets with user experience
• Pros and cons of Core Web Vitals
• How to stay on top of your budgets to fight regressions
What Not to Document and Why_ (North Bay Python 2024)Margaret Fero
We’re hopefully all on board with writing documentation for our projects. However, especially with the rise of supply-chain attacks, there are some aspects of our projects that we really shouldn’t document, and should instead remediate as vulnerabilities. If we do document these aspects of a project, it may help someone compromise the project itself or our users. In this talk, you will learn why some aspects of documentation may help attackers more than users, how to recognize those aspects in your own projects, and what to do when you encounter such an issue.
These are slides as presented at North Bay Python 2024, with one minor modification to add the URL of a tweet screenshotted in the presentation.
Coordinate Systems in FME 101 - Webinar SlidesSafe Software
If you’ve ever had to analyze a map or GPS data, chances are you’ve encountered and even worked with coordinate systems. As historical data continually updates through GPS, understanding coordinate systems is increasingly crucial. However, not everyone knows why they exist or how to effectively use them for data-driven insights.
During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to maintain and transform your data’s coordinate systems in an easy-to-digest way, accurately representing the geographical space that it exists within. During this webinar, you will have the chance to:
- Enhance Your Understanding: Gain a clear overview of what coordinate systems are and their value
- Learn Practical Applications: Why we need datams and projections, plus units between coordinate systems
- Maximize with FME: Understand how FME handles coordinate systems, including a brief summary of the 3 main reprojectors
- Custom Coordinate Systems: Learn how to work with FME and coordinate systems beyond what is natively supported
- Look Ahead: Gain insights into where FME is headed with coordinate systems in the future
Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!
GDG Cloud Southlake #34: Neatsun Ziv: Automating AppsecJames Anderson
The lecture titled "Automating AppSec" delves into the critical challenges associated with manual application security (AppSec) processes and outlines strategic approaches for incorporating automation to enhance efficiency, accuracy, and scalability. The lecture is structured to highlight the inherent difficulties in traditional AppSec practices, emphasizing the labor-intensive triage of issues, the complexity of identifying responsible owners for security flaws, and the challenges of implementing security checks within CI/CD pipelines. Furthermore, it provides actionable insights on automating these processes to not only mitigate these pains but also to enable a more proactive and scalable security posture within development cycles.
The Pains of Manual AppSec:
This section will explore the time-consuming and error-prone nature of manually triaging security issues, including the difficulty of prioritizing vulnerabilities based on their actual risk to the organization. It will also discuss the challenges in determining ownership for remediation tasks, a process often complicated by cross-functional teams and microservices architectures. Additionally, the inefficiencies of manual checks within CI/CD gates will be examined, highlighting how they can delay deployments and introduce security risks.
Automating CI/CD Gates:
Here, the focus shifts to the automation of security within the CI/CD pipelines. The lecture will cover methods to seamlessly integrate security tools that automatically scan for vulnerabilities as part of the build process, thereby ensuring that security is a core component of the development lifecycle. Strategies for configuring automated gates that can block or flag builds based on the severity of detected issues will be discussed, ensuring that only secure code progresses through the pipeline.
Triaging Issues with Automation:
This segment addresses how automation can be leveraged to intelligently triage and prioritize security issues. It will cover technologies and methodologies for automatically assessing the context and potential impact of vulnerabilities, facilitating quicker and more accurate decision-making. The use of automated alerting and reporting mechanisms to ensure the right stakeholders are informed in a timely manner will also be discussed.
Identifying Ownership Automatically:
Automating the process of identifying who owns the responsibility for fixing specific security issues is critical for efficient remediation. This part of the lecture will explore tools and practices for mapping vulnerabilities to code owners, leveraging version control and project management tools.
Three Tips to Scale the Shift Left Program:
Finally, the lecture will offer three practical tips for organizations looking to scale their Shift Left security programs. These will include recommendations on fostering a security culture within development teams, employing DevSecOps principles to integrate security throughout the development
Data Protection in a Connected World: Sovereignty and Cyber Securityanupriti
Delve into the critical intersection of data sovereignty and cyber security in this presentation. Explore unconventional cyber threat vectors and strategies to safeguard data integrity and sovereignty in an increasingly interconnected world. Gain insights into emerging threats and proactive defense measures essential for modern digital ecosystems.
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
Video traffic on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research and of industrial networked multimedia services certainly was the HTTP Adaptive Streaming (HAS) technique. This resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) which, together with HTTP Live Streaming (HLS), is widely used for multimedia delivery in today’s networks. Existing challenges in multimedia systems research deal with the trade-off between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, latency), and (iii) quality of experience (QoE). Optimizing towards one aspect usually negatively impacts at least one of the other two aspects if not both. This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry. In this talk, we will present selected novel approaches and research results of the first year of the ATHENA CD Lab’s operation. We will highlight HAS-related research on (i) multimedia content provisioning (machine learning for video encoding); (ii) multimedia content delivery (support of edge processing and virtualized network functions for video networking); (iii) multimedia content consumption and end-to-end aspects (player-triggered segment retransmissions to improve video playout quality); and (iv) novel QoE investigations (adaptive point cloud streaming). We will also put the work into the context of international multimedia systems research.
What's Next Web Development Trends to Watch.pdfSeasiaInfotech2
Explore the latest advancements and upcoming innovations in web development with our guide to the trends shaping the future of digital experiences. Read our article today for more information.
UiPath Community Day Kraków: Devs4Devs ConferenceUiPathCommunity
We are honored to launch and host this event for our UiPath Polish Community, with the help of our partners - Proservartner!
We certainly hope we have managed to spike your interest in the subjects to be presented and the incredible networking opportunities at hand, too!
Check out our proposed agenda below 👇👇
08:30 ☕ Welcome coffee (30')
09:00 Opening note/ Intro to UiPath Community (10')
Cristina Vidu, Global Manager, Marketing Community @UiPath
Dawid Kot, Digital Transformation Lead @Proservartner
09:10 Cloud migration - Proservartner & DOVISTA case study (30')
Marcin Drozdowski, Automation CoE Manager @DOVISTA
Pawel Kamiński, RPA developer @DOVISTA
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
09:40 From bottlenecks to breakthroughs: Citizen Development in action (25')
Pawel Poplawski, Director, Improvement and Automation @McCormick & Company
Michał Cieślak, Senior Manager, Automation Programs @McCormick & Company
10:05 Next-level bots: API integration in UiPath Studio (30')
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
10:35 ☕ Coffee Break (15')
10:50 Document Understanding with my RPA Companion (45')
Ewa Gruszka, Enterprise Sales Specialist, AI & ML @UiPath
11:35 Power up your Robots: GenAI and GPT in REFramework (45')
Krzysztof Karaszewski, Global RPA Product Manager
12:20 🍕 Lunch Break (1hr)
13:20 From Concept to Quality: UiPath Test Suite for AI-powered Knowledge Bots (30')
Kamil Miśko, UiPath MVP, Senior RPA Developer @Zurich Insurance
13:50 Communications Mining - focus on AI capabilities (30')
Thomasz Wierzbicki, Business Analyst @Office Samurai
14:20 Polish MVP panel: Insights on MVP award achievements and career profiling
Navigating Post-Quantum Blockchain: Resilient Cryptography in Quantum Threatsanupriti
In the rapidly evolving landscape of blockchain technology, the advent of quantum computing poses unprecedented challenges to traditional cryptographic methods. As quantum computing capabilities advance, the vulnerabilities of current cryptographic standards become increasingly apparent.
This presentation, "Navigating Post-Quantum Blockchain: Resilient Cryptography in Quantum Threats," explores the intersection of blockchain technology and quantum computing. It delves into the urgent need for resilient cryptographic solutions that can withstand the computational power of quantum adversaries.
Key topics covered include:
An overview of quantum computing and its implications for blockchain security.
Current cryptographic standards and their vulnerabilities in the face of quantum threats.
Emerging post-quantum cryptographic algorithms and their applicability to blockchain systems.
Case studies and real-world implications of quantum-resistant blockchain implementations.
Strategies for integrating post-quantum cryptography into existing blockchain frameworks.
Join us as we navigate the complexities of securing blockchain networks in a quantum-enabled future. Gain insights into the latest advancements and best practices for safeguarding data integrity and privacy in the era of quantum threats.
AC Atlassian Coimbatore Session Slides( 22/06/2024)apoorva2579
This is the combined Sessions of ACE Atlassian Coimbatore event happened on 22nd June 2024
The session order is as follows:
1.AI and future of help desk by Rajesh Shanmugam
2. Harnessing the power of GenAI for your business by Siddharth
3. Fallacies of GenAI by Raju Kandaswamy
Hire a private investigator to get cell phone recordsHackersList
Learn what private investigators can legally do to obtain cell phone records and track phones, plus ethical considerations and alternatives for addressing privacy concerns.
The Rise of Supernetwork Data Intensive ComputingLarry Smarr
Invited Remote Lecture to SC21
The International Conference for High Performance Computing, Networking, Storage, and Analysis
St. Louis, Missouri
November 18, 2021
Details of description part II: Describing images in practice - Tech Forum 2024BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and transcript: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
Details of description part II: Describing images in practice - Tech Forum 2024
Introduction To TensorFlow | Deep Learning Using TensorFlow | TensorFlow Tutorial | Edureka
1. Agenda
▪ Difference Between Machine Learning and Deep Learning
▪ What is Deep Learning?
▪ What is TensorFlow?
▪ TensorFlow Data Structures
▪ TensorFlow Use-Case
2. Agenda
▪ Difference Between Machine Learning and Deep Learning
▪ What is Deep Learning?
▪ What is TensorFlow?
▪ TensorFlow Data Structures
▪ TensorFlow Use-Case