Artificial Intelligence,
History of Artificial Intelligence,
Artificial Intelligence Use Cases,
Artificial Intelligence Applications,
Ways of Achieving AI,
Machine Learning,
Deep Learning,
Supervised and Unsupervised Learning,
Classification Vs Prediction,
TensorFlow,
TensorFlow Graphs,
History of TensorFlow,
Companies using TensorFlow,
Using Deep Q Networks to Learn Video Game Strategies,
TensorFlow Use Cases,
AI & Deep Learning with TensorFlow,
How TensorFlow used today
For more updates on Big Data, Cloud Computing, Data Analytics, Artificial Intelligence, IoT subscribe to http://www.mybigdataanalytics.in
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
Artificial intelligence or AI in short is the latest technology on which the whole world is working today. We at myassignmenthelp.net are providing help with all the assignments and projects. So when ever you need help with any work related to AI feel free to get in touch
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
AI, Machine Learning and Deep Learning - The OverviewSpotle.ai
The deck takes you into a fascinating journey of Artificial Intelligence, Machine Learning and Deep Learning, dissect how they are connected and in what way they differ. Supported by illustrative case studies, the deck is your ready reckoner on the fundamental concepts of AI, ML and DL.
Explore more videos, masterclasses with global experts, projects and quizzes on https://spotle.ai/learn
Introduction to Artificial Intelligence.pptxRSAISHANKAR
My name is R. Sai Shankar. In here, I'm publish a small PowerPoint Presentation on Artificial Intelligence. Here is the link for my YouTube Channel "Learn AI With Shankar". Please Like Share Subscribe. Thank you.
https://youtu.be/3N5C99sb-gc
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
Artificial intelligence or AI in short is the latest technology on which the whole world is working today. We at myassignmenthelp.net are providing help with all the assignments and projects. So when ever you need help with any work related to AI feel free to get in touch
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
AI, Machine Learning and Deep Learning - The OverviewSpotle.ai
The deck takes you into a fascinating journey of Artificial Intelligence, Machine Learning and Deep Learning, dissect how they are connected and in what way they differ. Supported by illustrative case studies, the deck is your ready reckoner on the fundamental concepts of AI, ML and DL.
Explore more videos, masterclasses with global experts, projects and quizzes on https://spotle.ai/learn
Introduction to Artificial Intelligence.pptxRSAISHANKAR
My name is R. Sai Shankar. In here, I'm publish a small PowerPoint Presentation on Artificial Intelligence. Here is the link for my YouTube Channel "Learn AI With Shankar". Please Like Share Subscribe. Thank you.
https://youtu.be/3N5C99sb-gc
Artificial Intelligence (A.I) and Its Application -SeminarBIJAY NAYAK
this presentation includes the the Basics of Artificial Intelligence and its applications in various Field. feel free to ask anything. Editors are always welcome.
Artificial intelligence (AI) all in one presentation consists of almost all concepts of Artificial intelligence. i.e.
Artificial intelligence, Robotics, ANN, NLP, NLU, History of AI, History, Pakistan, basharat jehan, agriculture university peshawar, Forward and Backward Chaining, Grammers in AI, Morphology, Examples of Expert system, Laws of Robotics,Expert system languages, Syntactic Analysis in NLP, Lecture Notes
Types Of Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/y5swZ2Q_lBw
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka PPT on "Types Of Artificial Intelligence" will help you understand the different stages and types of Artificial Intelligence in depth. The following topics are covered in this Artificial Intelligence Tutorial:
History Of AI
What Is AI?
Stages Of Artificial Intelligence
Types Of Artificial Intelligence
Domains Of Artificial Intelligence
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Deep Learning Explained: The future of Artificial Intelligence and Smart Netw...Melanie Swan
This talk provides an overview of an important emerging artificial intelligence technology, deep learning neural networks. Deep learning is a branch of computer science focused on machine learning algorithms that model and make predictions about data. A key distinction is that deep learning is not merely a software program, but a new class of information technology that is changing the concept of the modern technology project by replacing hard-coded software with a capacity to learn and execute tasks. In the future, deep learning smart networks might comprise a global computational infrastructure tackling real-time data science problems such as global health monitoring, energy storage and transmission, and financial risk assessment.
Presenting the landscape of AI/ML in 2023 by introducing a quick summary of the last 10 years of its progress, current situation, and looking at things happening behind the scene.
A Brief History Of Artificial Intelligence | Developing Text To Speech Recogn...Edureka!
** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This edureka tutorial on "History of Artificial Intelligence" will provide you with detailed information about the evolution of Artificial Intelligence. It will also show the various use cases of Artificial Intelligence in everyday life with an example.
Follow us to never miss an update in the future.
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
Artificial Intelligence (A.I) and Its Application -SeminarBIJAY NAYAK
this presentation includes the the Basics of Artificial Intelligence and its applications in various Field. feel free to ask anything. Editors are always welcome.
Artificial intelligence (AI) all in one presentation consists of almost all concepts of Artificial intelligence. i.e.
Artificial intelligence, Robotics, ANN, NLP, NLU, History of AI, History, Pakistan, basharat jehan, agriculture university peshawar, Forward and Backward Chaining, Grammers in AI, Morphology, Examples of Expert system, Laws of Robotics,Expert system languages, Syntactic Analysis in NLP, Lecture Notes
Types Of Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/y5swZ2Q_lBw
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka PPT on "Types Of Artificial Intelligence" will help you understand the different stages and types of Artificial Intelligence in depth. The following topics are covered in this Artificial Intelligence Tutorial:
History Of AI
What Is AI?
Stages Of Artificial Intelligence
Types Of Artificial Intelligence
Domains Of Artificial Intelligence
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
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Deep Learning Explained: The future of Artificial Intelligence and Smart Netw...Melanie Swan
This talk provides an overview of an important emerging artificial intelligence technology, deep learning neural networks. Deep learning is a branch of computer science focused on machine learning algorithms that model and make predictions about data. A key distinction is that deep learning is not merely a software program, but a new class of information technology that is changing the concept of the modern technology project by replacing hard-coded software with a capacity to learn and execute tasks. In the future, deep learning smart networks might comprise a global computational infrastructure tackling real-time data science problems such as global health monitoring, energy storage and transmission, and financial risk assessment.
Presenting the landscape of AI/ML in 2023 by introducing a quick summary of the last 10 years of its progress, current situation, and looking at things happening behind the scene.
A Brief History Of Artificial Intelligence | Developing Text To Speech Recogn...Edureka!
** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This edureka tutorial on "History of Artificial Intelligence" will provide you with detailed information about the evolution of Artificial Intelligence. It will also show the various use cases of Artificial Intelligence in everyday life with an example.
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
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Develop a fundamental overview of Google TensorFlow, one of the most widely adopted technologies for advanced deep learning and neural network applications. Understand the core concepts of artificial intelligence, deep learning and machine learning and the applications of TensorFlow in these areas.
The deck also introduces the Spotle.ai masterclass in Advanced Deep Learning With Tensorflow and Keras.
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial For Be...Simplilearn
This presentation on TensorFlow will help you in understanding what exactly is TensorFlow and how it is used in Deep Learning. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this tutorial, you will learn the fundamentals of TensorFlow concepts, functions, and operations required to implement deep learning algorithms and leverage data like never before. This TensorFlow tutorial is ideal for beginners who want to pursue a career in Deep Learning. Now, let us deep dive into this TensorFlow tutorial and understand what TensorFlow actually is and how to use it.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning Libraries
3. Why TensorFlow?
4. What is TensorFlow?
5. What are Tensors?
6. What is a Data Flow Graph?
7. Program Elements in TensorFlow
8. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-google-keynote
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Jeff Dean, Senior Fellow at Google, presents the "Large-Scale Deep Learning for Building Intelligent Computer Systems" keynote at the May 2016 Embedded Vision Summit.
Over the past few years, Google has built two generations of large-scale computer systems for training neural networks, and then applied these systems to a wide variety of research problems that have traditionally been very difficult for computers. Google has released its second generation system, TensorFlow, as an open source project, and is now collaborating with a growing community on improving and extending its functionality. Using TensorFlow, Google's research group has made significant improvements in the state-of-the-art in many areas, and dozens of different groups at Google use it to train state-of-the-art models for speech recognition, image recognition, various visual detection tasks, language modeling, language translation, and many other tasks.
In this talk, Jeff highlights some of ways that Google trains large models quickly on large datasets, and discusses different approaches for deploying machine learning models in environments ranging from large datacenters to mobile devices. He will then discuss ways in which Google has applied this work to a variety of problems in Google's products, usually in close collaboration with other teams. This talk describes joint work with many people at Google.
Bringing Machine Learning to Mobile Apps with TensorFlowAlaina Carter
Machine learning has seen a significant rise and has changed the way we use our mobile devices. It improves productivity and offers an edge over the competition. Read more to know how machine learning with Tensor flow adds extraordinary power to intelligent mobile apps.
Artificial Intelligence and Deep Learning in Azure, CNTK and TensorflowJen Stirrup
Artificial Intelligence and Deep Learning in Azure, using Open Source technologies CNTK and Tensorflow. The tutorial can be found on GitHub here: https://github.com/Microsoft/CNTK/tree/master/Tutorials
and the CNTK video can be found here: https://youtu.be/qgwaP43ZIwA
The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
Video available at : https://www.youtube.com/watch?v=EoysuTMmmMc
Similar to Artificial Intelligence = ML + DL with Tensor Flow (20)
1. What are the differences between a DBMS and RDBMS?
2. Explain the terms database and DBMS. Also, mention the different types of DBMS.
3. What are the advantages of DBMS?
4. Mention the different languages present in DBMS
5. What do you understand by query optimization?
6. Do we consider NULL values the same as that of blank space or zero?
7. What do you understand by aggregation and atomicity?
8. What are the different levels of abstraction in the DBMS?
9. What is an entity-relationship model?
10. What do you understand by the terms Entity, Entity Type, and Entity Set in DBMS?
11. What are relationships and mention different types of relationships in the DBMS
12. What is concurrency control?
13. What are the ACID properties in DBMS?
14. What is normalization and what are the different types of normalization?
15. What are the different types of keys in the database?
16. What do you understand by correlated subqueries in DBMS?
17. Explain Database partitioning and its importance.
18. What do you understand by functional dependency and transitive dependency in DBMS?
19. What is the difference between two and three-tier architectures?
20. Mention the differences between Unique Key and Primary Key
21. What is a checkpoint in DBMS and when does it occur?
22. Mention the differences between Trigger and Stored Procedures
23. What are the differences between Hash join, Merge join and Nested loops?
24. What do you understand by Proactive, Retroactive and Simultaneous Update?
25. What are indexes? Mention the differences between the clustered and non-clustered index
26. What do you understand by intension and extension?
27. What do you understand by cursor? Mention the different types of cursor A cursor is a database object which helps in manipulating data, row by row and represents a result set.
28. Explain the terms specialization and generalization
29. What do you understand by Data Independence?
30. What are the different integrity rules present in the DBMS?
31. What does Fill Factor concept mean with respect to indexes?
32. What is Index hunting and how does it help in improving query performance?
33. What are the differences between network and hierarchical database model?
34. Explain what is a deadlock and mention how it can be resolved?
35. What are the differences between an exclusive lock and a shared lock?
=>Concept of Governance
=>Risk and Control (GRC) as applicable to IT operational risk
=>Importance of documentation
=>DATA FLOW DIAGRAM for every application
=>Review of changes in the Data flow, reporting, etc.
=>Parameters for review
=>Importance of review on SLA compliance
=>Reporting to IT Strategy committee, Board etc.
Importance of Data - Where to find it, how to store, manipulate, and characterize it
Artificial Intelligence (AI)- Introduction to AI & ML Technologies/ Applications
Machine Learning (ML), Basic Machine Learning algorithms.
Applications of AI & ML in Marketing, Sales, Finance, Operations, Supply Chain
& Human Resources Data Governance
Legal and Ethical Issues
Robotic Process Automation (RPA)
Internet of Things (IoT)
Cloud Computing
What is Data ?
What is Information?
Data Models, Schema and Instances
Components of Database System
What is DBMS ?
Database Languages
Applications of DBMS
Introduction to Databases
Fundamentals of Data Modeling and Database Design
Database Normalization
Types of keys in database management system
Distributed Database
CASE (COMPUTER AIDED SOFTWARE ENGINEERING)
CASE and its Scope
CASE support in software life cycle documentation
project management
Internal Interface
Reverse Software Engineering
Architecture of CASE environment.
SOFTWARE RELIABILITY AND QUALITY ASSURANCE
Reliability issues
Reliability metrics
Reliability growth modeling
Software quality
ISO 9000 certification for software industry
SEI capability maturity model
comparison between ISO and SEI CMM
Software Testing
Different Types of Software Testing
Verification
Validation
Unit Testing
Beta Testing
Alpha Testing
Black Box Testing
White Box testing
Error
Bug
Software Design
Design principles
Problem partitioning
Abstraction
Top down and bottom up-design
Structured approach
Functional versus object oriented approach
Design specifications and verification
Monitoring and control
Cohesiveness
Coupling
Fourth generation techniques
Functional independence
Software Architecture
Transaction and Transform Mapping
SDLC
PDLC
Software Development Life Cycle
Program Development Life Cycle
Iterative model
Advantages of Iterative model
Disadvantages of Iterative model
When to use iterative model
Spiral Model
Advantages of Spiral model
Disadvantages of Spiral model
When to use Spiral model
Role of Management in Software Development
Software Lifecycle Models / Software Development Models
Types of Software development models
Waterfall Model
Features of Waterfall Model
Phase of Waterfall Model
Prototype Model
Advantages of Prototype Model
Disadvantages of Prototype model
V Model
Advantages of V-model
Disadvantages of V-model
When to use the V-model
Incremental Model
ITERATIVE AND INCREMENTAL DEVELOPMENT
INCREMENTAL MODEL LIFE CYCLE
When to use the Incremental model
Rapid Application Development RAD Model
phases in the rapid application development (RAD) model
Advantages of the RAD model
Disadvantages of RAD model
When to use RAD model
Agile Model
Advantages of Agile model
Disadvantages of Agile model
When to use Agile model
Introduction to software engineering
Software products
Why Software is Important?
Software costs
Features of Software?
Software Applications
Software—New Categories
Software Engineering
Importance of Software Engineering
Essential attributes / Characteristics of good software
Software Components
Software Process
Five Activities of a Generic Process framework
Relative Costs of Fixing Software Faults
Software Qualities
Software crisis
Software Development Stages/SDLC
What is Software Verification
Advantages of Software Verification
Advantages of Validation
Cloud Computing
Categories of Cloud Computing
SaaS
PaaS
IaaS
Threads of Cloud Computing
Insurance Challenges
Cloud Solutions
Security of the Insurance Industry
Cloud Solutions
Insurance Security in the Insurance Industry with respect to Indian market
Application Software
Applications Software
Software Types
Task-Oriented Productivity Software
Business Software
Application Software and Ethics
Computers and People
Software:
Systems and Application Software
Identify and briefly describe the functions of the two basic kinds of software
Outline the role of the operating system and identify the features of several popular operating systems
Discuss how application software can support personal, workgroup, and enterprise business objectives
Identify three basic approaches to developing application software and discuss the pros and cons of each
Outline the overall evolution and importance of programming languages and clearly differentiate among the generations of programming languages
Identify several key software issues and trends that have an impact on organizations and individuals
Programming Languages
A formal language for describing computation?
A “user interface” to a computer?
Syntax + semantics?
Compiler, or interpreter, or translator?
A tool to support a programming paradigm?
Number Codes and Registers
2’s complement numbers
Addition and subtraction
Binary coded decimal
Gray codes for binary numbers
ASCII characters
Moving towards hardware
Storing data
Processing data
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Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
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The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
2. Contents
• Artificial Intelligence
• History of Artificial Intelligence
• Artificial Intelligence Use Cases
• Artificial Intelligence Applications
• Ways of Achieving AI
• Machine Learning
• Deep Learning
• Supervised and Unsupervised
Learning
• Classification Vs Prediction
• TensorFlow
• TensorFlow Graphs
• History of TensorFlow
• Companies using TensorFlow
• Using Deep Q Networks to Learn Video
Game Strategies
• TensorFlow Use Cases
• AI & Deep Learning with TensorFlow
• How TensorFlow used today
17. Machine
Learning
Machine learning refers to the use of
algorithms to parse data, process and
learn from it, in order to make
predictions or determinations about
something.
One of the best application for machine
learning is computer vision: OCR, object
tracking, object recognition etc.
18.
19.
20. Deep
Learning
Deep learning is a subfield of
machine learning concerned with
algorithms inspired by the structure
and function of the brain called
artificial neural networks (ANNs)
Compared to older ML algorithms,
Deep Learning performs better with
a large amount of data
24. Supervised and Unsupervised Learning
Supervised Learning
• All data has been labeled (supervised)
by an expert. Thanks to this labeling
process, we can help the network to
realise the difference between classes
(even though sometimes this does not
happen).
• Some techniques: NNs, SVM, etc.
Unsupervised Learning
• Our data are not labeled. Unsupervised
algorithms consider confidence measures
among samples in order to create
homogeneous clusters.
• Most famous technique:
Clustering (k-means,
hierarchical etc.)
25. Classification Vs Prediction
Classification
• Given an input observation,
classification is the problem of
identifying to which of a set of
categories (classes) the new observation
belongs.
• i.e: trafficsignals recognition,
emotion recognition etc.
Prediction
• Prediction refers to the problem of
estimating the behaviour of a
phenomenon by analysing the “previous
history”
• i.e: object tracking,
forecasting etc.
27. TensorFlow
TensorFlow is an open-source library for
numerical computation and machine
learning.
Its basic principle is simple: you build in
Python a graph of computation to perorm
and then TensorFlow runs it efficiently
using optimized C++ code.
TensorFlow supports computation across
multiple CPUs and GPUs
28. TensorFlor
Originally developed by Google Brain Team to
conduct machine learning and deep neural
networks research.
General enough to be applicable in a wide variety of
other domains as well .
Provides an extensive suite of functions and classes
that allow users to build various models from
scratch.
https://www.youtube.com/watch?v=MotG3XI2qSs
29. Tensor Flow Graphs
Running a Simple Graph
• Software that uses TensorFlow
is often divided into two phases:
graph building and execution.
• In order to evaluate this graph
we must run the session and all
its initialisers.
• TensorFlow supports
computation across multiple
x = tf.Variable(3,name=”x”)
y = tf.Variable(4,name=”y”) f
= x*x + 2*y + 5
sess = tf.Session()
sess.run(x.initializer)
sess.run(y.initializer)
res = sess.run(f) print
res
x = tf.Variable(3,name=”x”)
y = tf.Variable(4,name=”y”) f
= x*x + 2*y + 5
with tf.Session() as session:
x.initializer.run()
y.initializer.run()
result = f.eval()
30. History
Google Brain's second-generation system.
Version 1.0.0 was released on February 11, 2017.
can run on multiple CPUs and GPUs.
Available on 64-bit Linux, macOS, Windows, and mobile
computing platforms including Android and iOS.