Mengenal Machine/Deep Learning, Artificial Intelligence dan mengenal apa bedanya dengan Business Intelligence, apa hubungannya dengan Big Data dan Data Science/Analytics.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
Index.....................
History of Machine Learning.
What is Machine Learning.
Why ML.
Learning System Model.
Training and Testing.
Performance.
Algorithms.
Machine Learning Structure.
Application.
Conclusion.
----------------------------------------------
THANK YOU
Differences Between Machine Learning Ml Artificial Intelligence Ai And Deep L...SlideTeam
"You can download this product from SlideTeam.net"
Differences between Machine Learning ML Artificial Intelligence AI and Deep Learning DL is for the mid level managers to give information about what is AI, what is Machine Learning, what is deep learning, Machine learning process. You can also know the difference between Machine learning and Deep learning to understand AI, ML, and DL in a better way for business growth. https://bit.ly/325zI9o
Explainable AI (XAI) is becoming Must-Have NFR for most AI enabled product or solution deployments. Keen to know viewpoints and collaboration opportunities.
*What is Machine Learning?
-Definition
-Explanation
*Difference between Machine Learning and Standard Programs
*Machine Learning Models
-Supervised Learning
--Classification
--Regression
-Unsupervised Learning
--Clustering
*AI Evolution
-History of AI
-Neural Networks and Deep Learning
-Simple Neural Network and Deep Neural Network
-Difference between AI, Machine Learning, and Deep Learning
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
Index.....................
History of Machine Learning.
What is Machine Learning.
Why ML.
Learning System Model.
Training and Testing.
Performance.
Algorithms.
Machine Learning Structure.
Application.
Conclusion.
----------------------------------------------
THANK YOU
Differences Between Machine Learning Ml Artificial Intelligence Ai And Deep L...SlideTeam
"You can download this product from SlideTeam.net"
Differences between Machine Learning ML Artificial Intelligence AI and Deep Learning DL is for the mid level managers to give information about what is AI, what is Machine Learning, what is deep learning, Machine learning process. You can also know the difference between Machine learning and Deep learning to understand AI, ML, and DL in a better way for business growth. https://bit.ly/325zI9o
Explainable AI (XAI) is becoming Must-Have NFR for most AI enabled product or solution deployments. Keen to know viewpoints and collaboration opportunities.
*What is Machine Learning?
-Definition
-Explanation
*Difference between Machine Learning and Standard Programs
*Machine Learning Models
-Supervised Learning
--Classification
--Regression
-Unsupervised Learning
--Clustering
*AI Evolution
-History of AI
-Neural Networks and Deep Learning
-Simple Neural Network and Deep Neural Network
-Difference between AI, Machine Learning, and Deep Learning
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
This presentation is based on new trends and technology in computer science. In this presentation, we have depicted the basic principles of artificial intelligence. An attempt has been made to explain advanced technologies like machine learning and deep learning.
Basics of machine learning. Fundamentals of machine learning. These slides are collected from different learning materials and organized into one slide set.
This article aims to classify texts and predict the categories of occurrences, through the study of Artificial Intelligence models, using Machine Learning and Deep Learning for the classification of texts and analysis of predictions, suggesting the best option with the smallest error.
The solution was designed to be implemented in two stages: Machine Learning and Application, according to the diagram below from the Data Science Academy.
Machine Learning an Exploratory Tool: Key Conceptsachakracu
This was an Online Lecture Describing Key Concepts of Machine Learning Strategies inclusing Neural Networks
National Webinar On Education 4.0 “Ensuring Continuity in Learning and Innovation Through Digitization”
Organized By: Singhad Institute of Management, Pune in Association with Savitribai Phule Pune University
12th June 2020
Invited talk at Tsinghua University on "Applications of Deep Neural Network". As the tech. lead of deep learning task force at NIO USA INC, I was invited to give this colloquium talk on general applications of deep neural network.
In a world of data explosion, the rate of data generation and consumption is on the increasing side,
there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
In a world of data explosion, the rate of data generation and consumption is on the increasing side, there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection,
but making ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make a futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
Implementasi Teknologi Industri 4.0 pada TNI ADDony Riyanto
Militer dan teknologi adalah 2 hal yang sangat terkait erat. TNI khususnya dalam hal ini TNI AD harus mampu mengadopsi Industri 4.0 pada teklogi tempur maupun non-tempur. Presentasi ini disampaikan di jajaran DISINFOLAHTAD Mabes TNI AD, dalam rangka acara komunitas TIKAD pada 2018 yang lalu. Dan mendapatkan piagam dari KADISFOLAHTAD pada saat itu.
Blockchain tidak sama dengan bitcoin. Teknologi blockchain tidak hanya dipakai untuk cryptocurrency. Bisa jauh lebih besar dari itu, bahkan bisa untuk IoT dan Big Data. Saya menyebutnya sebagai the missing link, antara IoT dan Big Data. Teknologi Big Data masih memiliki isu trust, kepercayaan terhadap data yang disimpan dan diproses. Menghubungkan IoT langsung dengan sistem big data, akan memperbesar permasalahan ini. Namun tidak demikian jika kita menerapkan blockchain diantaranya. Rangkaian ini melengkapi teknologi ABCDI: AI, Blockchain, Cloud, bid Data, dan IoT
Ini adalah tutorial singkat untuk berkenalan dengan ROS dan ROS2 Galactic. Robotic Operating System bukanlah OS seperti Windows, atau MacOS, melainkan sebuah rangkaian sistem yang dapat membantu pengembangan robot yang kompleks. Dalam tutorial ini kita belajar cara menjalankan ROS2 Galactic dengan cukup mudah tanpa perlu instalasi ROS, yaitu menggunakan docker.
Membuat Desain Roket Amatir dan Menjalankan SimulasiDony Riyanto
Membuat desain roket amatir dan menjalankan simulasi roket buat pemula. Tidak dibutuhkan pengetahuan hardware sama sekali. Tidak membutuhkan roket beneran dan hardware sama sekali. Disini kita belajar dulu tentang apa itu roket amatir, rocket engine/motor, mendesain macam-macam jenis roket, menjalankan simulasi dan memahami source code firmware untuk controller rocket.
Creating UDP Broadcast App Using Python Socket on WIndows & LinuxDony Riyanto
This short slides demonstrating how to create simple UDP broadcast app using Python Socket on WIndows & Linux. There are some important notes, especially when writing Python script on different OS/kernel (On Windows, you need to bind network interface IP, while on Linux you should use SO_REUSEPORT, unless you're using kernel <3.9..change to SO_REUSEADDR)
Desain ground control & Sistem Pendukung untuk Male UAV/UCAVDony Riyanto
Desain ground control & Sistem Pendukung untuk Male UAV/UCAV. Catatan pribadi, dirangkum dari berbagai sumber, berkaitan dengan momentum peluncuran proyek riset PUNA MALE Elang Hitam, RI. Hasil kerjasama Kemenhan, TNI AU, PT DI, BPPT, PT LEN, LAPAN
Cloud Service Design for Computer Vision, Image & Video Processing+AnalyticsDony Riyanto
Sebuah presentasi yang merangkum berbagai portofolio kami terkait Image/Video Processing dan Analytics. Ada banyak aspek yang dibahas dan semoga semua ini dapat memberikan insight/pembelajaran bagi yang akan terjun ke dunia image/video processing secara online/cloud
Beberapa Studi Kasus Fintech Micro PaymentDony Riyanto
Merupakan catatan pribadi. Diambil dari beberapa pengalaman pribadi/tim selama di Sumatera Utara. Menjadi tantangan/pertimbangan untuk pengembangan micro payment terintegrasi, agar menjadi solusi buat: buruh/pekerja, UMKM, pabrik/employer, dan ekosistem didalamnya.
Implementasi Full Textsearch pada DatabaseDony Riyanto
Ini juga dokumen pendukung untuk sebuah kementerian yang hendak mengimplementasikan Full Textsearch pada database existing. Dokumen ini mengambil contoh kasus implementasi Full Textsearch pada stackoverflow menggunakan database PG
Beberapa strategi implementasi open api untuk legacy system existing appDony Riyanto
Ini dokumen pendukung untuk sebuah proyek di kementerian. Beberapa slide sudah dibuang. Intinya: ada legacy app (web) konvensional/serverside (tidak ada API), tapi mau di open supaya antar lembaga bisa akses, tentu dengan pengaturan ACL/credential. Nah disini saya gambarkan beberapa strategi/skenario yang memungkinkan
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
1. Machine Learning, Deep
Learning, AI, Big Data, Data
Science, Data Analytics
by Dony Riyanto
Prepared and Presented to Panin Asset Management
January 2019
3. Machine Learning
• Machine (ML) is a field of artificial intelligence that uses
statistical techniques to give computer systems the ability to
"learn" (e.g., progressively improve performance on a specific
task) from data, without being explicitly programmed.
4. Machine learning tasks
Machine learning tasks are typically classified into several broad categories:
• Supervised learning: The computer is presented with example inputs and their desired outputs, given by
a "teacher", and the goal is to learn a general rule that maps inputs to outputs. As special cases, the input
signal can be only partially available, or restricted to special feedback.
• Semi-supervised learning: The computer is given only an incomplete training signal: a training set with
some (often many) of the target outputs missing.
• Active learning: The computer can only obtain training labels for a limited set of instances (based on a
budget), and also has to optimize its choice of objects to acquire labels for. When used interactively, these
can be presented to the user for labeling.
• Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find
structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or
a means towards an end (feature learning).
• Reinforcement learning: Data (in form of rewards and punishments) are given only as feedback to the
program's actions in a dynamic environment, such as driving a vehicle or playing a game against an
opponent.[4]:3
9. Machine learning applications
Another categorization of machine learning tasks arises when one considers the desired output of a
machine-learned system:
• In classification, inputs are divided into two or more classes, and the learner must produce a model that
assigns unseen inputs to one or more (multi-label classification) of these classes. This is typically tackled
in a supervised way. Spam filtering is an example of classification, where the inputs are email (or other)
messages and the classes are "spam" and "not spam".
• In regression, also a supervised problem, the outputs are continuous rather than discrete.
• In clustering, a set of inputs is to be divided into groups. Unlike in classification, the groups are not
known beforehand, making this typically an unsupervised task.
• Density estimation finds the distribution of inputs in some space.
• Dimensionality reduction simplifies inputs by mapping them into a lower-dimensional space. Topic
modeling is a related problem, where a program is given a list of human language documents and is
tasked to find out which documents cover similar topics.
• Among other categories of machine learning problems, learning to learn learns its own inductive bias
based on previous experience. Developmental learning, elaborated for robot learning, generates its own
sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills
through autonomous self-exploration and social interaction with human teachers and using guidance
mechanisms such as active learning, maturation, motor synergies, and imitation.
10. Relation to Data Mining
Machine learning and data mining often employ the same methods and overlap significantly,
but while machine learning focuses on prediction, based on known properties learned
from the training data, data mining focuses on the discovery of (previously) unknown
properties in the data (this is the analysis step of knowledge discovery in databases).
Data mining uses many machine learning methods, but with different goals; on the other
hand, machine learning also employs data mining methods as "unsupervised learning" or as
a preprocessing step to improve learner accuracy. Much of the confusion between these
two research communities (which do often have separate conferences and separate
journals, ECML PKDD being a major exception) comes from the basic assumptions they
work with: in machine learning, performance is usually evaluated with respect to the ability
to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the
key task is the discovery of previously unknown knowledge. Evaluated with respect to
known knowledge, an uninformed (unsupervised) method will easily be outperformed by
other supervised methods, while in a typical KDD task, supervised methods cannot be used
due to the unavailability of training data.
13. Deep Learning
• Deep learning (also known as deep structured learning or hierarchical learning) is part of
a broader family of machine learning methods based on learning data representations, as
opposed to task-specific algorithms. Learning can also be supervised, semi-supervised or
unsupervised.
• Deep learning architectures such as deep neural networks, deep belief networks and
recurrent neural networks have been applied to fields including computer vision,
speech recognition, natural language processing, audio recognition, social network
filtering, machine translation, bioinformatics, drug design, medical image analysis,
material inspection and board game programs, where they have produced results
comparable to and in some cases superior to human experts.
• Deep learning models are vaguely inspired by information processing and communication
patterns in biological nervous systems yet have various differences from the structural
and functional properties of biological brains (especially human brains), which make them
incompatible with neuroscience evidences.
14. Deep Learning (contd)
• Most modern deep learning models are based on an artificial neural network, although they can also
include propositional formulas or latent variables organized layer-wise in deep generative models such as
the nodes in deep belief networks and deep Boltzmann machines.
• In deep learning, each level learns to transform its input data into a slightly more abstract and composite
representation. In an image recognition application, the raw input may be a matrix of pixels; the first
representational layer may abstract the pixels and encode edges; the second layer may compose and
encode arrangements of edges; the third layer may encode a nose and eyes; and the fourth layer may
recognize that the image contains a face. Importantly, a deep learning process can learn which features to
optimally place in which level on its own. (Of course, this does not completely obviate the need for hand-
tuning; for example, varying numbers of layers and layer sizes can provide different degrees of
abstraction.)
• The "deep" in "deep learning" refers to the number of layers through which the data is transformed.
More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP
is the chain of transformations from input to output. CAPs describe potentially causal connections
between input and output. For a feedforward neural network, the depth of the CAPs is that of the network
and is the number of hidden layers plus one (as the output layer is also parameterized). For recurrent
neural networks, in which a signal may propagate through a layer more than once, the CAP depth is
potentially unlimited.[2] No universally agreed upon threshold of depth divides shallow learning from deep
learning, but most researchers agree that deep learning involves CAP depth > 2. CAP of depth 2 has
been shown to be a universal approximator in the sense that it can emulate any function.[citation needed]
Beyond that more layers do not add to the function approximator ability of the network. Deep models
(CAP > 2) are able to extract better features than shallow models and hence, extra layers help in learning
features.
18. Artificial Intelligence
• Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by
machines, in contrast to the natural intelligence displayed by humans and other animals. In computer
science AI research is defined as the study of "intelligent agents": any device that perceives its
environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially,
the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans
associate with other human minds, such as "learning" and "problem solving".
• Includes:
• Experts Systems
• Fuzzy Logic
• Robotics (e.g humanoid, arm, chatbot, etc)
• Natural Language Processing
• Neural Language (general)
• Etc
• Some of the subfield of AI used/transformed itu Machine Learning/Deep Learning field, with the impact of
advance computational technology and huge amount of data (especially unstructured/ no row+column)
19. Data Science
• Data science is an interdisciplinary field that uses scientific methods, processes,
algorithms and systems to extract knowledge and insights from data in various forms,
both structured and unstructured, similar to data mining.
• Data science is a "concept to unify statistics, data analysis, machine learning and their
related methods" in order to "understand and analyze actual phenomena" with data. It
employs techniques and theories drawn from many fields within the context of
mathematics, statistics, information science, and computer science.
• Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science
(empirical, theoretical, computational and now data-driven) and asserted that
"everything about science is changing because of the impact of information technology"
and the data deluge.
21. Data Analysis
• Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal
of discovering useful information, informing conclusions, and supporting decision-making. Data analysis
has multiple facets and approaches, encompassing diverse techniques under a variety of names, while
being used in different business, science, and social science domains.
• Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery
for predictive rather than purely descriptive purposes, while business intelligence covers data analysis
that relies heavily on aggregation, focusing mainly on business information.[1] In statistical applications,
data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory
data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on
confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical
models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and
structural techniques to extract and classify information from textual sources, a species of unstructured
data. All of the above are varieties of data analysis.
• Data integration is a precursor to data analysis,[according to whom?] and data analysis is closely
linked[how?] to data visualization and data dissemination. The term data analysis is sometimes used as a
synonym for data modeling.
22.
23. Business Intelligence
• Business intelligence (BI) comprises the strategies and technologies used by enterprises for the data
analysis of business information. BI technologies provide historical, current and predictive views of
business operations. Common functions of business intelligence technologies include reporting, online
analytical processing, analytics, data mining, process mining, complex event processing, business
performance management, benchmarking, text mining, predictive analytics and prescriptive analytics. BI
technologies can handle large amounts of structured to help identify, develop and otherwise create
new strategic business opportunities. They aim to allow for the easy interpretation of these data.
Identifying new opportunities and implementing an effective strategy based on insights can provide
businesses with a competitive market advantage and long-term stability.
• Business intelligence can be used by enterprises to support a wide range of business decisions
ranging from operational to strategic. Basic operating decisions include product positioning or pricing.
Strategic business decisions involve priorities, goals and directions at the broadest level. In all cases,
BI is most effective when it combines data derived from the market in which a company operates (external
data) with data from company sources internal to the business such as financial and operations data
(internal data). When combined, external and internal data can provide a complete picture
• Business Intelligence (structured data source, enterprise goal intensive, traditional process flow, result:
insight from previous data for executives) VS Big Data Analytics (unstructured/semi structured, wide-
range goals, modern aproach/flow, result: predictive, for executive and/or machine to decide next step)
29. Big Data
• Big data is a term used to refer to data sets that are too large or complex for traditional data-
processing application software to adequately deal with. Data with many cases (rows) offer greater
statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false
discovery rate. Big data challenges include capturing data, data storage, data analysis, search,
sharing, transfer, visualization, querying, updating, information privacy and data source. Big data
was originally associated with three key concepts: volume, variety, and velocity. Other concepts later
attributed with big data are veracity (i.e., how much noise is in the data) and value.
• Current usage of the term "big data" tends to refer to the use of predictive analytics, user behavior
analytics, or certain other advanced data analytics methods that extract value from data, and seldom
to a particular size of data set. "There is little doubt that the quantities of data now available are indeed
large, but that’s not the most relevant characteristic of this new data ecosystem."Analysis of data sets
can find new correlations to" spot business trends, prevent diseases, combat crime and so
on."Scientists, business executives, practitioners of medicine, advertising and governments alike regularly
meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and
business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,
connectomics, complex physics simulations, biology and environmental research.
30.
31. Big Data Definition shiftingThe term has been in use since the 1990s, with some giving credit to John Mashey for popularizing the term. Big data usually
includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data
within a tolerable elapsed time. Big data philosophy encompasses unstructured, semi-structured and structured data, however
the main focus is on unstructured data.
Big data "size" is a constantly moving target, as of 2012 ranging from a few dozen terabytes to many exabytes of data. Big data
requires a set of techniques and technologies with new forms of integration to reveal insights from datasets that are diverse,
complex, and of a massive scale.
A 2016 definition states that "Big data represents the information assets characterized by such a high volume, velocity and variety
to require specific technology and analytical methods for its transformation into value". Additionally, a new V, veracity, is added by
some organizations to describe it, revisionism challenged by some industry authorities. The three Vs (volume, variety and velocity)
have been further expanded to other complementary characteristics of big data:
• Machine learning: big data often doesn't ask why and simply detects patterns
• Digital footprint: big data is often a cost-free byproduct of digital interaction (e.g people interaction in Facebook)
A 2018 definition states "Big data is where parallel computing tools are needed to handle data", and notes, "This represents a
distinct and clearly defined change in the computer science used, via parallel programming theories, and losses of some of the
guarantees and capabilities made by Codd’s relational model". The growing maturity of the concept more starkly delineates the
difference between "big data" and "Business Intelligence":
• Business Intelligence uses descriptive statistics with data with high information density to measure things, detect trends, etc.
• Big data uses inductive statistics and concepts from nonlinear system identification to infer laws (regressions, nonlinear
relationships, and causal effects) from large sets of data with low information density to reveal relationships and dependencies,
or to perform predictions of outcomes and behaviors.
39. Perkenalan
• Mendalami pemrograman dan teknik komputer/elektronik sejak 1991 hingga sekarang (-/+
29 tahun)
• Lebih menyukai bahasa pemrograman, logika dan algoritma (problem solving)
• Pernah bekerja profesional bbrp perusahaan lintas industri (ISP, pabrik, broadcasting,
telekomunikasi, finance/finserv, buku/perpustakaan)
• Pernah berkarya sebagai dosen/pengajar dan konsultan lintas industri (agriculture,
aquafarming/coldstorage, telco, retail, manufacturer, human resources/capital, dsb)
• Sejak 2013 mulai mendalami ke dunia IoT, Drone/UAV, infosec, bigdata, machine learning,
dsb
• Aplikasi AI pertama pernah dibuat tahun 1992 menggunakan Turbo Prolog, voice
recognition (1992/93), polymorphic / heuristic code (1993), aplikasi Fuzzy Logic (1996/97),
dosen AI tahun 2008/10, mulai mempelajari map reduce/hadoop 2014.
• Berpartisipasi aktif dalam bbrp conference terkait Big Data, antara lain: Data For Public
Policy (international conference, PBB/Pulse Lab), Big Data Week (1st, 2nd), Precission
Agruculture projects (with CIagri), Jakarta Smart City (literacy index, BPAD DKI Jakarta),
dsb