This is a slide deck from a presentation, that my colleague Shirin Glander (https://www.slideshare.net/ShirinGlander/) and I did together. As we created our respective parts of the presentation on our own, it is quite easy to figure out who did which part of the presentation as the two slide decks look quite different ... :)
For the sake of simplicity and completeness, I just copied the two slide decks together. As I did the "surrounding" part, I added Shirin's part at the place when she took over and then added my concluding slides at the end. Well, I'm sure, you will figure it out easily ... ;)
The presentation was intended to be an introduction to deep learning (DL) for people who are new to the topic. It starts with some DL success stories as motivation. Then a quick classification and a bit of history follows before the "how" part starts.
The first part of the "how" is some theory of DL, to demystify the topic and explain and connect some of the most important terms on the one hand, but also to give an idea of the broadness of the topic on the other hand.
After that the second part dives deeper into the question how to actually implement DL networks. This part starts with coding it all on your own and then moves on to less coding step by step, depending on where you want to start.
The presentation ends with some pitfalls and challenges that you should have in mind if you want to dive deeper into DL - plus the invitation to become part of it.
As always the voice track of the presentation is missing. I hope that the slides are of some use for you, though.
This is a slide deck from a presentation, that my colleague Shirin Glander (https://www.slideshare.net/ShirinGlander/) and I did together. As we created our respective parts of the presentation on our own, it is quite easy to figure out who did which part of the presentation as the two slide decks look quite different ... :)
For the sake of simplicity and completeness, I just copied the two slide decks together. As I did the "surrounding" part, I added Shirin's part at the place when she took over and then added my concluding slides at the end. Well, I'm sure, you will figure it out easily ... ;)
The presentation was intended to be an introduction to deep learning (DL) for people who are new to the topic. It starts with some DL success stories as motivation. Then a quick classification and a bit of history follows before the "how" part starts.
The first part of the "how" is some theory of DL, to demystify the topic and explain and connect some of the most important terms on the one hand, but also to give an idea of the broadness of the topic on the other hand.
After that the second part dives deeper into the question how to actually implement DL networks. This part starts with coding it all on your own and then moves on to less coding step by step, depending on where you want to start.
The presentation ends with some pitfalls and challenges that you should have in mind if you want to dive deeper into DL - plus the invitation to become part of it.
As always the voice track of the presentation is missing. I hope that the slides are of some use for you, though.
This is a slide deck from a presentation, that my colleague Uwe Friedrichsen (https://www.slideshare.net/ufried/) and I did together. As we created our respective parts of the presentation on our own, it is quite easy to figure out who did which part of the presentation as the two slide decks look quite different ... :)
For the sake of simplicity and completeness, Uwe copied the two slide decks together. As he did the "surrounding" part, he added my part at the place where I took over and then added concluding slides at the end. Well, I'm sure, you will figure it out easily ... ;)
The presentation was intended to be an introduction to deep learning (DL) for people who are new to the topic. It starts with some DL success stories as motivation. Then a quick classification and a bit of history follows before the "how" part starts.
The first part of the "how" is some theory of DL, to demystify the topic and explain and connect some of the most important terms on the one hand, but also to give an idea of the broadness of the topic on the other hand.
After that the second part dives deeper into the question how to actually implement DL networks. This part starts with coding it all on your own and then moves on to less coding step by step, depending on where you want to start.
The presentation ends with some pitfalls and challenges that you should have in mind if you want to dive deeper into DL - plus the invitation to become part of it.
As always the voice track of the presentation is missing. I hope that the slides are of some use for you, though.
Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things.
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.
How Can Machine Learning Help Your Research Forward?Wouter Deconinck
Machine learning is a buzzwords that conjures up visions of programming gurus and data magicians solving problems with little effort while others balk at the black-box nature and lack of first principles understanding. In this talk I hope to introduce some ways in which you can start to use powerful machine learning algorithms to solve certain classes of problems in ways that may be more generic than traditional approaches. I will use examples from a range of fields to demonstrate the power of machine learning, even though those field with access to large data sets have lead the charge. I will highlight differences between machine learning in physics and other data sciences. Finally, I will point out why a solid understanding of the underlying physical principles is a necessity to use machine learning in research with any success.
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
Valencian Summer School 2015
Day 1
Lecture 5
Data Transformation and Feature Engineering
Charles Parker (Alston Trading)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
State of the Art in Machine Learning, by Thomas Dietterich, Distinguished Professor Emeritus in the School of EECS at Oregon State University and Chief Scientist of BigML.
*MLSEV 2020: Virtual Conference.
Natural Language Query to SQL conversion using Machine Learning ApproachMinhazul Arefin
Natural Language Processing is a computer science and artificial intelligence topic concerned with computer-human language interactions and how computers are designed for processing and exploring a variety of natural language data, in particular. The Structured Query Language for non-expert users is usually a challenging database storage, they may not know the database structure. For database applications to improve the interaction between database and user, a new intelligent interface is therefore necessary. The concept of utilizing a natural language instead of a structured query language has led to the creation of the natural language interface to database systems as a new form of processing procedure. The aim of this research is to build a query generating process using an algorithm for the machine learning to represent information according to user's demands for answering query and obtaining information. For the conversion of Natural Language Query into Structured Query, we utilized a lowercase conversion, removing escaped words, tokenization, PoS tagging, word similarity, Jaro-Winklar matching algorithm, and the method Naive Bayes.
This is a slide deck from a presentation, that my colleague Uwe Friedrichsen (https://www.slideshare.net/ufried/) and I did together. As we created our respective parts of the presentation on our own, it is quite easy to figure out who did which part of the presentation as the two slide decks look quite different ... :)
For the sake of simplicity and completeness, Uwe copied the two slide decks together. As he did the "surrounding" part, he added my part at the place where I took over and then added concluding slides at the end. Well, I'm sure, you will figure it out easily ... ;)
The presentation was intended to be an introduction to deep learning (DL) for people who are new to the topic. It starts with some DL success stories as motivation. Then a quick classification and a bit of history follows before the "how" part starts.
The first part of the "how" is some theory of DL, to demystify the topic and explain and connect some of the most important terms on the one hand, but also to give an idea of the broadness of the topic on the other hand.
After that the second part dives deeper into the question how to actually implement DL networks. This part starts with coding it all on your own and then moves on to less coding step by step, depending on where you want to start.
The presentation ends with some pitfalls and challenges that you should have in mind if you want to dive deeper into DL - plus the invitation to become part of it.
As always the voice track of the presentation is missing. I hope that the slides are of some use for you, though.
Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things.
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.
How Can Machine Learning Help Your Research Forward?Wouter Deconinck
Machine learning is a buzzwords that conjures up visions of programming gurus and data magicians solving problems with little effort while others balk at the black-box nature and lack of first principles understanding. In this talk I hope to introduce some ways in which you can start to use powerful machine learning algorithms to solve certain classes of problems in ways that may be more generic than traditional approaches. I will use examples from a range of fields to demonstrate the power of machine learning, even though those field with access to large data sets have lead the charge. I will highlight differences between machine learning in physics and other data sciences. Finally, I will point out why a solid understanding of the underlying physical principles is a necessity to use machine learning in research with any success.
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
Valencian Summer School 2015
Day 1
Lecture 5
Data Transformation and Feature Engineering
Charles Parker (Alston Trading)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
State of the Art in Machine Learning, by Thomas Dietterich, Distinguished Professor Emeritus in the School of EECS at Oregon State University and Chief Scientist of BigML.
*MLSEV 2020: Virtual Conference.
Natural Language Query to SQL conversion using Machine Learning ApproachMinhazul Arefin
Natural Language Processing is a computer science and artificial intelligence topic concerned with computer-human language interactions and how computers are designed for processing and exploring a variety of natural language data, in particular. The Structured Query Language for non-expert users is usually a challenging database storage, they may not know the database structure. For database applications to improve the interaction between database and user, a new intelligent interface is therefore necessary. The concept of utilizing a natural language instead of a structured query language has led to the creation of the natural language interface to database systems as a new form of processing procedure. The aim of this research is to build a query generating process using an algorithm for the machine learning to represent information according to user's demands for answering query and obtaining information. For the conversion of Natural Language Query into Structured Query, we utilized a lowercase conversion, removing escaped words, tokenization, PoS tagging, word similarity, Jaro-Winklar matching algorithm, and the method Naive Bayes.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
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
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
2. ECE 8443: Lecture 01, Slide 1
• Pattern Recognition: “the act of taking raw data and taking an action based
on the category of the pattern.”
• Common Applications: speech recognition, fingerprint identification
(biometrics), DNA sequence identification
• Related Terminology:
Machine Learning: The ability of a machine to improve its performance
based on previous results.
Machine Understanding: acting on the intentions of the user
generating the data.
• Related Fields: artificial intelligence, signal processing and discipline-specific
research (e.g., target recognition, speech recognition, natural language
processing).
Terminology
3. ECE 8443: Lecture 01, Slide 2
• Which of these images are most scenic?
• How can we develop a system to automatically determine scenic beauty?
(Hint: feature combination)
• Solutions to such problems require good feature extraction and good
decision theory.
Recognition or Understanding?
5. ECE 8443: Lecture 01, Slide 4
• Regions of overlap represent the
classification error
• Error rates can be computed with
knowledge of the joint probability
distributions (see OCW-MIT-6-
450Fall-2006).
• Context is used to reduce overlap.
• In real problems, features are
confusable and represent
actual variation in the data.
• The traditional role of the
signal processing engineer
has been to develop better
features (e.g., “invariants”).
Features Are Confusable
7. ECE 8443: Lecture 01, Slide 6
Train Classifier
Choose Model
Choose Features
Evaluate Classifier
End
Collect Data
Start
Key issues:
• “There is no data like more data.”
• Perceptually-meaningful features?
• How do we find the best model?
• How do we estimate parameters?
• How do we evaluate performance?
Goal of the course:
• Introduce you to mathematically
rigorous ways to train and evaluate
models.
The Design Cycle
8. ECE 8443: Lecture 01, Slide 7
• I got 100% accuracy on...
Almost any algorithm works some of the time, but few real-world problems
have ever been completely solved.
Training on the evaluation data is forbidden.
Once you use evaluation data, you should discard it.
• My algorithm is better because...
Statistical significance and experimental design play a big role in
determining the validity of a result.
There is always some probability a random choice of an algorithm will
produce a better result.
• Hence, in this course, we will also learn how to evaluate algorithms.
Common Mistakes
9. ECE 8443: Lecture 01, Slide 8
• Sorting Fish: incoming fish are sorted
according to species using optical
sensing (sea bass or salmon?)
Feature Extraction
Segmentation
Sensing
• Problem Analysis:
set up a camera and take some sample
images to extract features
Consider features such as length,
lightness, width, number and shape of
fins, position of mouth, etc.
Image Processing Example
10. ECE 8443: Lecture 01, Slide 9
• Conclusion: Length is a poor discriminator
Length As A Discriminator
11. ECE 8443: Lecture 01, Slide 10
• Lightness is a better feature than length because it reduces the
misclassification error.
• Can we combine features in such a way that we improve performance?
(Hint: correlation)
Add Another Feature
12. ECE 8443: Lecture 01, Slide 11
• Treat features as a N-tuple (two-dimensional vector)
• Create a scatter plot
• Draw a line (regression) separating the two classes
Width And Lightness
13. ECE 8443: Lecture 01, Slide 12
• Can we do better than a linear classifier?
• What is wrong with this decision surface?
(Hint: generalization)
Decision Theory
14. ECE 8443: Lecture 01, Slide 13
• Why might a smoother decision surface be a better choice?
(Hint: Occam’s Razor).
• This course investigates how to find such “optimal” decision surfaces and
how to provide system designers with the tools to make intelligent
trade-offs.
Generalization and Risk
15. ECE 8443: Lecture 01, Slide 14
• Degrees of difficulty: • Real data is often much harder:
Correlation
16. ECE 8443: Lecture 01, Slide 15
….
• There are many excellent resources on
the Internet that demonstrate pattern
recognition concepts.
• There are many MATLAB toolboxes
that implement state of the art
algorithms.
• One such resource is a Java Applet
that lets you quickly explore how a
variety of algorithms process the
same data.
• An important first principle is:
There are no magic equations or
algorithms.
You must understand the properties
of your data and what a priori
knowledge you can bring to bear on
the problem.
First Principle
17. ECE 8443: Lecture 01, Slide 16
• How much can we trust isolated data
points?
• Optimal decision surface is a line
• Optimal decision surface changes
abruptly
• Optimal decision surface still a line
• Can we integrate prior knowledge about data, confidence, or willingness to
take risk?
Generalization And Risk
18. ECE 8443: Lecture 01, Slide 17
Bayesian Formulations
• Bayesian formulation for speech recognition:
• Objective: minimize the word error rate by maximizing
• Approach: maximize (training)
acoustic model (hidden Markov models, Gaussian mixtures, etc.
language model (finite state machines, N-grams)
acoustics (ignored during maximization)
• Bayes Rule allows us to convert the problem of estimating an unknown
posterior probability to a process in which we can postulate a model, collect
data under controlled conditions, and estimate the parameters of the model.
)
(
)
(
)
|
(
)
|
(
A
P
W
P
W
A
P
A
W
P
)
|
( A
W
P
)
|
( W
A
P
:
)
|
( W
A
P
:
)
(W
P
:
)
(A
P
Message
Source
Linguistic
Channel
Articulatory
Channel
Acoustic
Channel
Message Words Phones Features
19. ECE 8443: Lecture 01, Slide 18
Summary
• Pattern recognition vs. machine learning vs. machine understanding
• First principle of pattern recognition?
• We will focus more on decision theory and less on feature extraction.
• This course emphasizes statistical and data-driven methods for optimizing
system design and parameter values.
• Second most important principle?