Introducing ML.NET For Absolute Beginners - Part 1
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This document introduces ML.NET, an open-source machine learning framework for building custom machine learning models. It discusses why machine learning is important, with examples like personalized recommendations and data mining. Key applications of ML.NET are mentioned, such as sentiment analysis, product recommendation, and fraud detection. The document also provides an overview of machine learning techniques like clustering, regression, and classification and the four stages of building models with ML.NET: initializing the model, training it, scoring, and evaluating.
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SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Digital Marketing Trends in 2024 | Guide for Staying Ahead
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
JavaLand 2024: Application Development Green Masterplan
My presentation slides I used at JavaLand 2024
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Recently uploaded (20)
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Trusted Execution Environment for Decentralized Process Mining
Trusted Execution Environment for Decentralized Process Mining
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying Ahead
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Introducing ML.NET For Absolute Beginners - Part 1
- 1. Introducing ML.NET For Absolute Beginners By Muhmmad Bilal Amjad Microsoft Most Valuable Professional
- 2. Machine Learning • Herbert Alexander Simon : “Learning is any process by which a system improves performance from experience. • Machine Learning is concerned with computer programs that automatically improve their performance through experience.
- 3. Why Machine Learning 1. Develop Systems that can automatically adapt and customize themselves to individual users. Example: Personalized Results, suggestions etc. 2. Data Mining: Discover new knowledge from large databases. 3. Ability to mimic human and replace certain monotonous tasks that require some intelligence. • What are your suggestions?
- 4. Why Machine Learning is the future? • Data is the new asset. • Flood of available data. • Increasing computational power. • Increasing support and demand from industries.
- 5. Some ML Applications 1. Image Processing 2. Computer Vision 3. Search Engine 4. Diagnosis 5. Speech Recognition 6. E-Commerce 7. Marketing 8. Personalization
- 7. Your Platform for building anything
- 8. What is ML.NET? 1. Machine Learning Framework for building custom ML Models. 2. Custom ML Made Easy. 3. Cross Platform 4. Open Source.
- 9. Applications of ML.NET 1. Sentiment Analysis 2. Product Recommendation 3. Price Prediction 4. Customer Segmentation 5. Fraud Detection 6. Spam Detection 7. Image Classification 8. Sales Forecasting
- 10. Machine Learning Modules. 1. Clustering Algorithm that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. 2. Regression Algorithm use for prediction. 3. Classification Algorithm to draw some conclusion from observed values. 4. Anomaly Algorithm that identify items or events that do not conform to an expected pattern or to other items present in a dataset.
- 11. 4 Stages of ML.NET 1. Initialize the model. Selecting the Best Fit Algorithm. 2. Train the model. Training is the process of analyzing input data by model. 3. Scoring. Score generates the results based on the trained model. It must have same info/column as of training model. 4. Evaluate. Trained Model will be compared with the test data to compare and predict the final results to be produced.
Editor's Notes
- Herbert- Cognitive Psychologist