This is the first lecture of the AI course offered by me at PES University, Bangalore. In this presentation we discuss the different definitions of AI, the notion of Intelligent Agents, distinguish an AI program from a complex program such as those that solve complex calculus problems (see the integration example) and look at the role of Machine Learning and Deep Learning in the context of AI. We also go over the course scope and logistics.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
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
Intro to AI STRIPS Planning & Applications in Video-games Lecture6-Part1Stavros Vassos
This is a short course that aims to provide an introduction to the techniques currently used for the decision making of non-player characters (NPCs) in commercial video games, and show how a simple deliberation technique from academic artificial intelligence research can be employed to advance the state-of-the art.
For more information and downloading the supplementary material please use the following links:
http://stavros.lostre.org/2012/05/19/video-games-sapienza-roma-2012/
http://tinyurl.com/AI-NPC-LaSapienza
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
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Las aplicaciones de Inteligencia Artificial como Machine Learning y Deep Learning se han convertido en parte importante en nuestras vidas. Los productos que compramos, si somos o no aptos para un préstamo bancario, las películas o series que Netflix nos recomienda, coches autoconducidos, reconocimiento de objetos, etc; toda esa información es dirigida hacia nosotros por estos algoritmos.
En la actualidad, estos campos de estudio son los más apasionantes y retadores en computación debido a su alto nivel de complejidad y gran demanda en el mercado. En esta presentación vamos a conocer y aprender a diferenciar estos conceptos, ya que son herramientas inevitables para el mejoramiento de la vida humana.
A continuación, te presentamos algunos de los temas específicos que se expondrán:
- Contexto de ML y DL en Inteligencia Artificial.
- Machine Learning.
- Supervised Learning.
- Unsupervised Learning.
- Deep Learning.
- Artificial Neural Network.
- Convolutional Neural Networks.
- Aplicaciones en ML y DL.
Artificial Intelligence, Machine Learning, Deep Learning
The 5 myths of AI
Deep Learning in action
Basics of Deep Learning
NVIDIA Volta V100 and AWS P3
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...Edureka!
This Edureka "What is Deep Learning" video will help you to understand about the relationship between Deep Learning, Machine Learning and Artificial Intelligence and how Deep Learning came into the picture. This tutorial will be discussing about Artificial Intelligence, Machine Learning and its limitations, how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning.
Below are the topics covered in this tutorial:
1. What Is Artificial Intelligence?
2. What Is Machine Learning?
3. Limitations Of Machine Learning
4. Deep Learning To The Rescue
5. What Is Deep Learning?
6. Deep Learning Applications
To take a structured training on Deep Learning, you can check complete details of our Deep Learning with TensorFlow course here: https://goo.gl/VeYiQZ
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
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 is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to learn and make predictions or decisions without being explicitly programmed. In essence, machine learning allows computers to automatically discover patterns, associations, and insights within data and use that knowledge to improve their performance on a task.
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
Intro to AI STRIPS Planning & Applications in Video-games Lecture6-Part1Stavros Vassos
This is a short course that aims to provide an introduction to the techniques currently used for the decision making of non-player characters (NPCs) in commercial video games, and show how a simple deliberation technique from academic artificial intelligence research can be employed to advance the state-of-the art.
For more information and downloading the supplementary material please use the following links:
http://stavros.lostre.org/2012/05/19/video-games-sapienza-roma-2012/
http://tinyurl.com/AI-NPC-LaSapienza
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Las aplicaciones de Inteligencia Artificial como Machine Learning y Deep Learning se han convertido en parte importante en nuestras vidas. Los productos que compramos, si somos o no aptos para un préstamo bancario, las películas o series que Netflix nos recomienda, coches autoconducidos, reconocimiento de objetos, etc; toda esa información es dirigida hacia nosotros por estos algoritmos.
En la actualidad, estos campos de estudio son los más apasionantes y retadores en computación debido a su alto nivel de complejidad y gran demanda en el mercado. En esta presentación vamos a conocer y aprender a diferenciar estos conceptos, ya que son herramientas inevitables para el mejoramiento de la vida humana.
A continuación, te presentamos algunos de los temas específicos que se expondrán:
- Contexto de ML y DL en Inteligencia Artificial.
- Machine Learning.
- Supervised Learning.
- Unsupervised Learning.
- Deep Learning.
- Artificial Neural Network.
- Convolutional Neural Networks.
- Aplicaciones en ML y DL.
Artificial Intelligence, Machine Learning, Deep Learning
The 5 myths of AI
Deep Learning in action
Basics of Deep Learning
NVIDIA Volta V100 and AWS P3
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...Edureka!
This Edureka "What is Deep Learning" video will help you to understand about the relationship between Deep Learning, Machine Learning and Artificial Intelligence and how Deep Learning came into the picture. This tutorial will be discussing about Artificial Intelligence, Machine Learning and its limitations, how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning.
Below are the topics covered in this tutorial:
1. What Is Artificial Intelligence?
2. What Is Machine Learning?
3. Limitations Of Machine Learning
4. Deep Learning To The Rescue
5. What Is Deep Learning?
6. Deep Learning Applications
To take a structured training on Deep Learning, you can check complete details of our Deep Learning with TensorFlow course here: https://goo.gl/VeYiQZ
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
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 is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to learn and make predictions or decisions without being explicitly programmed. In essence, machine learning allows computers to automatically discover patterns, associations, and insights within data and use that knowledge to improve their performance on a task.
This presentation is the part of the webinar conducted by CloudxLab. This was the free session on Machine Learning.
Cloudxlab conducts such webinars very frequently and to make sure you never miss the future webinar update, please see the 'Events' section at CloudxLab.com
This is the first lecture on Applied Machine Learning. The course focuses on the emerging and modern aspects of this subject such as Deep Learning, Recurrent and Recursive Neural Networks (RNN), Long Short Term Memory (LSTM), Convolution Neural Networks (CNN), Hidden Markov Models (HMM). It deals with several application areas such as Natural Language Processing, Image Understanding etc. This presentation provides the landscape.
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. Machine learning (ML) is defined as a discipline of artificial intelligence (AI) that provides machines the ability to automatically learn from data and past experiences to identify patterns and make predictions with minimal human intervention.
AILABS - Lecture Series - Is AI the New Electricity? - Advances In Machine Le...AILABS Academy
Prof. Garain discusses in brief on the backgrounds of learning algorithms & major breakthroughs that have been made in the field of machine perception in the last 50 yrs. He also discusses the role of statistical algorithms like artificial neural network, support vector machines, and other concepts related to Deep Learning algorithms.
Along with the above, Prof. Garain touched upon the basics of CNN & RNN, Long Short-Term Memory Networks (LSTM) & attention network & illustrate all of these using real-life problems. Several state-of-the-art problems like image captioning, visual question answering, medical image analysis etc. were discussed to make the potential of deep learning algorithms understandable.
Prof. Utpal Garain is one of the leading minds in Kolkata in the field of Neural Networks & Artificial Intelligence. His research interest is now focused on AI research, especially exploring deep learning methods for language, image and video analysis including NLP tools, OCRs, handwriting analysis, computational forensics and the like.
[DSC Europe 22] On the Aspects of Artificial Intelligence and Robotic Autonom...DataScienceConferenc1
Autonomy in targeting is a function that could be applied to any intelligent system, in particular the rapidly expanding array of robotic systems, in the air, on land and at sea – including swarms of small robots. This is an area of significant investment and emphasis for many armed forces, and the question is not so much whether we will see more intelligent robots, but whether and by what means they will remain under human control. Today’s remote-controlled weapons could become tomorrow’s autonomous weapons with just a software upgrade. The central element of any future autonomous weapon system will be the software. Military powers are investing in AI for a wide range of applications10 and significant efforts are already underway to harness developments in image, facial and behavior recognition using AI and machine learning techniques for intelligence gathering and “automatic target recognition” to identify people, objects or patterns. Although not all autonomous weapon systems incorporate AI and machine learning, this software could form the basis of future autonomous weapon systems.
Generative Adversarial Networks : Basic architecture and variantsananth
In this presentation we review the fundamentals behind GANs and look at different variants. We quickly review the theory such as the cost functions, training procedure, challenges and go on to look at variants such as CycleGAN, SAGAN etc.
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
Artificial Neural Networks have been very successfully used in several machine learning applications. They are often the building blocks when building deep learning systems. We discuss the hypothesis, training with backpropagation, update methods, regularization techniques.
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
Artificial Intelligence Course: Linear models ananth
In this presentation we present the linear models: Regression and Classification. We illustrate with several examples. Concepts such as underfitting (Bias) and overfitting (Variance) are presented. Linear models can be used as stand alone classifiers for simple cases and they are essential building blocks as a part of larger deep learning networks
Naive Bayes Classifier is a machine learning technique that is exceedingly useful to address several classification problems. It is often used as a baseline classifier to benchmark results. It is also used as a standalone classifier for tasks such as spam filtering where the naive assumption (conditional independence) made by the classifier seem reasonable. In this presentation we discuss the mathematical basis for the Naive Bayes and illustrate with examples
Mathematical Background for Artificial Intelligenceananth
Mathematical background is essential for understanding and developing AI and Machine Learning applications. In this presentation we give a brief tutorial that encompasses basic probability theory, distributions, mixture models, anomaly detection, graphical representations such as Bayesian Networks, etc.
This presentation discusses the state space problem formulation and different search techniques to solve these. Techniques such as Breadth First, Depth First, Uniform Cost and A star algorithms are covered with examples. We also discuss where such techniques are useful and the limitations.
In this presentation we discuss several concepts that include Word Representation using SVD as well as neural networks based techniques. In addition we also cover core concepts such as cosine similarity, atomic and distributed representations.
Deep Learning techniques have enabled exciting novel applications. Recent advances hold lot of promise for speech based applications that include synthesis and recognition. This slideset is a brief overview that presents a few architectures that are the state of the art in contemporary speech research. These slides are brief because most concepts/details were covered using the blackboard in a classroom setting. These slides are meant to supplement the lecture.
Overview of TensorFlow For Natural Language Processingananth
TensorFlow open sourced recently by Google is one of the key frameworks that support development of deep learning architectures. In this slideset, part 1, we get started with a few basic primitives of TensorFlow. We will also discuss when and when not to use TensorFlow.
This slide set on convolutional neural networks is meant to be supplementary material to the slides from Andrej Karpathy's course. In this slide set we explain the motivation for CNN and also describe how to understand CNN coming from a standard feed forward neural networks perspective. For detailed architecture and discussions refer the original slides. I might post more detailed slides later.
This presentation discusses decision trees as a machine learning technique. This introduces the problem with several examples: cricket player selection, medical C-Section diagnosis and Mobile Phone price predictor. It discusses the ID3 algorithm and discusses how the decision tree is induced. The definition and use of the concepts such as Entropy, Information Gain are discussed.
This presentation is a part of ML Course and this deals with some of the basic concepts such as different types of learning, definitions of classification and regression, decision surfaces etc. This slide set also outlines the Perceptron Learning algorithm as a starter to other complex models to follow in the rest of the course.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
In this presentation we discuss the hypothesis of MaxEnt models, describe the role of feature functions and their applications to Natural Language Processing (NLP). The training of the classifier is discussed in a later presentation.
In this presentation we describe the formulation of the HMM model as consisting of states that are hidden that generate the observables. We introduce the 3 basic problems: Finding the probability of a sequence of observation given the model, the decoding problem of finding the hidden states given the observations and the model and the training problem of determining the model parameters that generate the given observations. We discuss the Forward, Backward, Viterbi and Forward-Backward algorithms.
Discusses the concept of Language Models in Natural Language Processing. The n-gram models, markov chains are discussed. Smoothing techniques such as add-1 smoothing, interpolation and discounting methods are addressed.
The discrete or atomic representation of words don't scale well to support rich semantics. Distributed representations associate a word with a vector based on the context in which the word occurs. In this presentation we describe the problem of word representation with a few illustrations and also describe the approach taken by word2vec. We also discuss the limitations of using a static database approach.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
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.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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.
2. What is this course about?
• Ultimate AI dream: Build a machine that is
indistinguishable from humans.
• Google’s CEO Sundar Pichai: “AI First”
• This course will cover Applied AI using
modern techniques
• After taking this course you can:
• Develop a formal understanding of AI techniques
• Build products that require cutting edge
techniques that include and not limited to deep
learning techniques
Copyright 2016 JNResearch, All Rights Reserved
3. Human Cognitive tasks and AI applications
Humans Can: Typical AI Applications
See Image classification, Face Recognition
Speak Text to speech
Understand speech Speech Recognition : Speech to text
Write Handwriting generation, Draw
Understand written text Handwriting Recognition, Digit
Recognition
Drive Self Driving cars
Physical tasks: Walk, Run, … Robotics
6. AI : Working Definition for our course (Ref: Russel and Norvig)
7. Four schools of thought (Ref: Russel and Norvig)
• Think humanly
• Understand how humans think and model this process
• Cognitive Science
• Act humanly
• Turing Test
• Knowledge, Reasoning, Language, Understanding, learning
• Think rationally
• Rational implies thinking or doing the right thing
• Use logic to encode the right thing and process inputs with
this framework
• Act rationally
• Define the right thing as: “Maximizing the goal
achievement”
• A rational agent achieves the best or optimal outcome
8. So, what is an agent?
Agent
EnvironmentSensors
Actuators
Internal StateInternal State
• Agent perceives the environment (Percepts) and acts upon the environment in order to
maximize achievement of the required goal (Actions)
• Looked at from this perspective, an agent is a function that maps the percepts to actions
9. Intelligent Agents
• Intelligent agents interact with the Environment
• Interactions through:
• Sensors
• Actuators
• The function f(.) that maps the sensor to actuators is the control policy
• Determines how the agent makes the decisions
• Those decisions take place many many times in a loop: Perception-Action cycle
• The concept of intelligent agents is abstract. One can cast any real world problem
using this model – for instance, it is possible to apply this model to search engine
design.
Fig Credit: Sebastian Thrun, Udacity
10. Modelling real world problems
Can we model the following examples? Identify what the sensors are, what are the
actuators and how would you describe the agent?
• Home Security Systems: Suppose we have a system that can take pictures or
record video on a continuous basis and we are interested in detecting an intruder.
• The speech recognizer: Receive the speech input and transcribe
• Receive the inputs from location sensors and turn the steering wheel
• Look at the last 4 hours data on stock trend of a set of companies and buy the
stock of the most promising company
11. What distinguishes AI programs?
• Algorithms that perform complex mathematical
operations do these much faster than a human
• If a student is perceived more “intelligent” if he
solves these problems faster, then why not these
programs be termed “intelligent”?
• In short, when do we call a program a AI
program?
12. Terminology
• Fully versus Partial Observability
• Deterministic versus Stochastic
• Discrete versus continuous
• Benign versus Adversarial
13. Observability
• Fully versus Partially Observable
• If what the agent senses momentarily at any time
from the environment is completely sufficient to
make the decisions it is fully observable
• Examples: Chess game, Tennis service,
• Environment has an internal state
• The agent may be able to fully observe the
state or partial
• The agent needs internal memory when
dealing with partially observable situations
• Markov models help us to structure such a
memory
Fig Credit: Sebastian Thrun, Udacity
14. Deterministic versus Stochastic
• Deterministic: chess moves – outcome is pre
determined
• Stochastic: Moving the dice – the outcome is not
pre determined
15. Discrete versus Continuous
• Examples of actions modelled as a continuous
variable
• Throwing a dart : infinitely many ways to angle the dart
• Turning the steering by an angle (0 to 360 deg, real
valued)
• Magnitude of acceleration to apply
• Actions that are Discrete Variables
• Choosing a gear (1, 2, 3, 4, Top, Reverse) in a car with
manual transmission system
• Deciding (buy, sell, wait) on stocks of a finite, small set
of companies and acting
• Deciding which elective course to sign up based on the
data available and aptitude
16. Benign versus Adversarial
• A benign system is not attempting to defeat the
agent
• Weather may affect the actions of a self driving car –
e.g: reduced visibility and hence increased
uncertainty of actions. But it is not an adversary
• An adversarial system attempts to score over
the agent. It tries to win over and not allow the
agent to succeed.
• A Generative Adversarial Network (GAN) is
modelled as an adversarial game.
17. AI as uncertainty management
• What to do when you don’t know what to do?
• Reasons for uncertainty
• Sensor faults, limitations
• Adversaries : we don’t know what it will do
• Stochastic Environments
• Ignorance
19. Exercise
Analyze wrt Observability, Stochasticity, Discrete/Continuous, Benign/Adversarial,
Stationary/Dynamic environment
• A “teacher agent” that decides the action to take (Go deeper, Give a break, Ask questions,
Repeat the concept) based on its observation of the environment (classroom).
• Game of chess
• Self Driving car
• Face recognition
21. Modelling AI Problems
Different types of problems may require different types of approaches
• Some problems can be easily represented using state spaces
• E.g. Robot navigation through the maze
• Problems that can be solved using Machine Learning techniques
• E.g. Face recognition
• Probabilistic Graphical Models such as Bayes Networks, HMMs
• E.g Speech Recognition
• Problems that can be well addressed using deductive logic
• Given certain propositions and input, perform logical inference – e.g. imagine a chatbot that
encodes some knowledge and can reason with the user
22. Example#1 : Modelling as a Graph Search
• Real World Problem
• Suppose you are to reach the Bangalore Airport from PESIT (your current location). You prefer
the route that leads you to the destination fastest. You are given the map of Bangalore and
are provided with information on the traffic congestion along each route and distance.
(Assume this doesn’t change till you reach the destination)
• Model
• Represent the landmarks as nodes (states) of a graph, the goal state being the Airport. Edges
represent the connection between the landmarks. Edges are annotated with the time cost of
moving from one landmark to the next.
• Algorithm
• Graph search algorithms such as BFS, DFS, Uniform Cost Search, A* Search etc
23. Example#2 – Modelling as a ML Problem
• Real World Problem
• Suppose you are to perform handwriting recognition, where the input is a English text written by
hand.
• Model
• Two possibilities: If the handwriting is recorded using a sensor that captures the strokes, we can
model the problem as a time series analysis problem and choose a suitable ML Classifier (HMM,
RNN, …)
• If the input is an image, we can model the problem as a text recognition in an image, where the
input is a tensor and choose a convolutional neural network
• Algorithm
• Training: Supervised learning using stochastic gradient descent for the type of classifier chosen
24. Artificial Intelligence, Machine Learning and Deep Learning
• The goal of AI is to build human-like intelligence on machines
• ML is a core approach to achieve this goal.
• Key idea behind ML: Learning from data
• ML is narrower in scope relative to AI
• DL is a suite of techniques that form a sub set of a broad suite
of ML techniques
• ML includes a broad variety of techniques like Probabilistic
Graphical Models, Decision Trees, Neural Networks etc. The
models can be shallow or deep.
• Deep learning uses a large number of computing layers stacked
vertically (output of one feeds in to the input of the next).
• The depth can be spatial or temporal
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AI
ML DL
25. Machine Learning
• During the first edition of DARPA self driving cars challenge (2004), none of the
participants succeeded
• In the next edition (2005), 5 cars succeeded, with Stanford Stanley bagging the
first position
• Machine Learning made all the difference
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26. When to apply Machine Learning?
• Data
• No data, no machine learning!
• Patterns that exist in the data
• If the data doesn’t contain definitive patterns, there is nothing to learn from
• No satisfactory algorithm exists
• If a satisfactory algorithm exists, no need to do statistical learning
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30. ML – Some taxonomy
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Learning Taxonomy
What is “learnt”? Parameters, Structure, Hidden Concepts
Technique Supervised, Unsupervised, Reinforcement
What for? Prediction, Diagnostics, Summarization
How? Passive, Active, Online, Offline
Outputs Classification, Regression
Model Paradigm Generative, Discriminative
31. Can we learn better?
• Can we achieve a human-like performance at least for some narrowly defined tasks?
• If I happen to have lots of data, can my learning scale with data size?
• If the problem solved by a machine learning classifier is narrowly scoped, how to use
a ML approach to solve large, complex problems?
• How much of domain expertise we need to have in order to apply ML to our
problem? E.g. Should I be an expert in signal processing in order to design a speech
recognition system? Should I be a linguist knowledgeable on Kannada in order to
develop an English to Kannada machine translator?
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32. Deep Learning
• Large number of layers forming a deep network
• The depth can be spatial or temporal
• More complex models but less dependency on
human experts crafting the best features
• Due to the model’s higher capacity, can leverage
the data better – more the data you give, better
can be the learning
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33. Feature Learning (fig from Y Bengio)
• Representation Learning
• Automatically learn the “right”
features at each hidden layer
• Learn multiple levels of
representations increasing in
abstraction
• Allow effective sharing of the
learned parameters across
different tasks: Multitask learning
34. Three reasons to use deep learning
• Performance
• The difference between 93% to 96% can make all the difference
• Make cool technologies usable for a common man.
• Broad Applicability (Domain independence)
• Not limited to a narrow set of problems
• Minimize the need for domain specialized feature engineering
• New class of applications
• Applications that require higher level semantics as opposed to routine classification
• Multimodal fusion
• Generative models
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35. ML Everywhere!: Text, Speech, Image, Video
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36. Reflex Agents
• Actions of a reflex agent depends only on the current inputs
• The current percept determines the action
• Example:
• Imagine driving fast on a road in Bangalore. You suddenly notice a speed hump that is not
painted, nor there was any sign board on the road to caution you. The moment you notice
the hump that springs up unexpected, you just apply the brakes!
37. State Space Models
• Real world problems are modelled as a graph
• Example: Finding the low cost path between 2 cities when there are many
paths that are possible
• Solutions are represented as paths through the graph.
• Our goal is to find the optimal path
38. Examples
• Chinese words are written without spaces
• Arabic written without vowels
• English:rtfclntllgnc
41. Course Plan
• We will use the broad framework used by
Stanford AI course
• We also draw heavily on the Sebastian
Thrun’s courses on Self Driving Cars
• We differ in the following aspects:
• Deeper deep learning as a tool to build
intelligent agents
• Hands on: Projects based on TensorFlow
• Emphasis on Applied AI : Core computer vision
problems, Core NLP problems and discussions
on self driving cars
Fig Credit: Percy Liang, Stanford
42. Course Contents
• Unit 1: Classical AI techniques
• Unit 2: Machine Learning and Deep Learning
• Unit 3: Applied AI: Computer Vision
• Unit 4: Applied AI: Natural Language Processing
• Unit 5: Applied AI: Robotics and Self Driving Cars
43. Why should I take this course?
• AI is a hot topic in the industry
• Every major technology company (Google, Apple, Facebook, Microsoft, Adobe, …) has made
huge bets on AI
• Large number of start ups working in this space and well funded
• AI courses and degree programs are highly sought after in the academia
• This course with focus on both sound theoretical principles as well as hands on
development helps you master the basics
45. What are the pre-requisites?
• Technical
• Probability theory
• Calculus
• Linear Algebra
• Python Programming
• Aptitude
• Aptitude for AI, Machine Learning and willingness to experiment and a strong commitment to
class policy.
46. How will be the course experience?
• Is this an introductory course or a rigorous one?
• Rigorous – the course is going to cover both classical AI as well as modern applied AI.
• What way it will be rigorous?
• Contemporary Research topics will be covered in addition to traditional approaches
• Lab work will be intensive
• Total effort you will put in during the lab/evaluations/final exam will be sizable.
• Will it burn me out?
• If you have the right aptitude, it will not. On the contrary you will find the course thrilling.
(The best way is to find out if this is worth it is by consulting your seniors who have taken NLP
or AML in the recent times!)
47. Course Timings
• We will have 2 classes per week, each lasting 2 hours. One of the two classes will
be on Saturday.
• As I work in the industry, there may be a few rescheduling of the classes on some
occasions. We will keep such disruptions to a minimum.