Artificial intelligence & machine learning
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The document discusses machine learning and deep learning techniques. It provides examples of different machine learning algorithms like decision trees, linear regression, neural networks and deep learning models. It also discusses applications of machine learning in areas like computer vision, natural language processing and bioinformatics. Finally, it talks about technologies that can help democratize machine learning like distributed computing frameworks and open source libraries.
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Recent trends discussed include digital transformation, COVID-19 impact, remote working, and disruptive technologies like quantum physics and driverless vehicles. Machine learning techniques can help analyze large, complex datasets and make predictions. Unsupervised machine learning models can find hidden patterns in unlabeled data and group objects based on similarities. Supervised learning predicts target variables using labeled examples to train algorithms like decision trees and random forests. The machine learning process involves data preparation, algorithm selection, model training, prediction, and evaluation.
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Machine learning
This document provides an overview of machine learning:
1. Machine learning is a branch of artificial intelligence that uses data to help computers learn without being explicitly programmed. It can recognize patterns in large amounts of data.
2. Machine learning involves collecting large datasets, creating algorithms to detect patterns in the data, and using those patterns to make predictions on new data.
3. Machine learning has many applications like improving health, making utilities more efficient, and simplifying the future through technologies like personalized assistants, optimized transportation, and computer vision.
Recommended
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The document discusses machine learning and deep learning techniques. It provides examples of different machine learning algorithms like decision trees, linear regression, neural networks and deep learning models. It also discusses applications of machine learning in areas like computer vision, natural language processing and bioinformatics. Finally, it talks about technologies that can help democratize machine learning like distributed computing frameworks and open source libraries.
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Recent trends discussed include digital transformation, COVID-19 impact, remote working, and disruptive technologies like quantum physics and driverless vehicles. Machine learning techniques can help analyze large, complex datasets and make predictions. Unsupervised machine learning models can find hidden patterns in unlabeled data and group objects based on similarities. Supervised learning predicts target variables using labeled examples to train algorithms like decision trees and random forests. The machine learning process involves data preparation, algorithm selection, model training, prediction, and evaluation.
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Data Mining - The Big Picture!
Recently, in the fields Business Intelligence and Data Management, everybody is talking about data science, machine learning, predictive analytics and many other “clever” terms with promises to turn your data into gold. In this slides, we present the big picture of data science and machine learning. First, we define the context for data mining from BI perspective, and try to clarify various buzzwords in this field. Then we give an overview of the machine learning paradigms. After that, we are going to discuss - at a high level - the various data mining tasks, techniques and applications. Next, we will have a quick tour through the Knowledge Discovery Process. Screenshots from demos will be shown, and finally we conclude with some takeaway points.
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Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
The document summarizes an agenda for a presentation on machine learning and data science. It includes an introduction to CRISP-DM (Cross Industry Standard for Data Mining), guided analytics, and a KNIME demo. It also discusses the differences between machine learning, artificial intelligence, and data science. Machine learning produces predictions, artificial intelligence produces actions, and data science produces insights. It provides an overview of the CRISP-DM process for data mining projects including the business understanding, data understanding, data preparation, modeling, evaluation, and deployment phases. It also discusses guided analytics and interactive systems to assist business analysts in finding insights and predicting outcomes from data.
Machine learning
This document provides an overview of machine learning:
1. Machine learning is a branch of artificial intelligence that uses data to help computers learn without being explicitly programmed. It can recognize patterns in large amounts of data.
2. Machine learning involves collecting large datasets, creating algorithms to detect patterns in the data, and using those patterns to make predictions on new data.
3. Machine learning has many applications like improving health, making utilities more efficient, and simplifying the future through technologies like personalized assistants, optimized transportation, and computer vision.
Automatic Model Documentation with H2O
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Video recording of the session can be viewed here: https://youtu.be/YEtDwYSXXJo
For many companies, model documentation is a requirement for any model to be used in the business. For other companies, model documentation is part of a data science team’s best practices. Model documentation includes how a model was created, training and test data characteristics, what alternatives were considered, how the model was evaluated, and information on model performance.
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Email: info@phdassistance.com
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Video recording of the session can be viewed here: https://youtu.be/YEtDwYSXXJo
For many companies, model documentation is a requirement for any model to be used in the business. For other companies, model documentation is part of a data science team’s best practices. Model documentation includes how a model was created, training and test data characteristics, what alternatives were considered, how the model was evaluated, and information on model performance.
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Engineering Intelligent Systems using Machine Learning
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- Overview of the conference with ~80 sessions and 2,700 participants
- Popular business applications of data mining like recommendation systems, predictive maintenance, and customer targeting
- The typical predictive modeling flow including data preparation, model training, evaluation, and deployment
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In this session, Greg will discuss what it will take to guide the evolution of technology and culture in parallel: leadership, technology that enables rapid scale and a complete & reliable data flow, and a data driven culture.
Advanced Analytics in Banking, CITI
In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
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In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
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The requirements for analysing big volumes of data have increased over the last few decades. The process of selecting, cleaning, modelling and interpreting data is called the KDD process. The decision of how to approach each step in this process has often been made manually by experts. However, experts cannot be aware of all methods, nor is it feasible to try all of them. Researchers have proposed different approaches for automating, or at least advising, the stages of the KDD process. This talk will outline the different types of Intelligent Discovery Assistants as described in the work of Serban et al. “A survey of intelligent assistants for data analysis” and point out some future directions.
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Top Rated Dissertation Data Analysis Services | PhD Assistance
Data Analytics is the keystone of transformative technologies like Artificial Intelligence (AI) and Machine Learning (ML). In the realm of AI and ML applications, data-driven insights empower businesses and researchers to make informed decisions, unravel patterns, and predict future trends.
For complete dissertation by statistics solution, visit - https://shorturl.at/oMSXY
Check our site to know more about real-time data analytics examples - https://shorturl.at/oszJ6
For #Enquiry:
Email: info@phdassistance.com
India: +91 91769 66446
UK: +44 7537144372
Real-Time Data Analytics Examples
Real-time data analytics analyses data as it’s generated or received, providing immediate insights and actionable information. Unlike traditional batch processing, which deals with data in fixed intervals, real-time data source analytics operate on a continuous data stream
For machine learning project proposal, visit - https://www.phdassistance.com/services/phd-data-analysis/quantitative-confirmatory-analysis/
Check our site to know more about ai applications examples - https://www.phdassistance.com/services/phd-data-analysis/
For #Enquiry:
Email: info@phdassistance.com
India: +91 91769 66446
UK: +44 7537144372
Deep learning tutorial (i)
This document provides an overview of deep learning tutorials and the deep learning landscape. It discusses the evolution of machine learning from 2012 to the present, focusing on developments in deep neural networks. It outlines popular deep learning system architectures including distributed architectures, standalone toolkits, and bleeding edge directions like convolutional neural networks, LSTMs, memory networks, reinforcement learning, and generative models. The document aims to give readers an introduction to the key concepts and industry applications of deep learning.
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1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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