This presentation provides an overview of artificial intelligence (AI) and deep learning. It begins with introductions to AI and deep learning, explaining that AI allows machines to perform tasks typically requiring human intelligence through machine learning. Deep learning is a type of machine learning using artificial neural networks inspired by the human brain. The presentation then discusses why AI has grown recently, citing increased computing power, data storage, and data availability. It also covers deep learning model development and concepts like underfitting and overfitting. The presentation describes different types of learning approaches like supervised, unsupervised, and reinforcement learning. It concludes with popular applications of deep learning like precision agriculture, computer vision, and recommendations.
Every thing about Artificial Intelligence Vaibhav Mishra
Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.
Cognitive ability of human brain and soft computing techniquesDr G R Sinha
The document discusses cognitive ability of the human brain and soft computing techniques. It begins with providing facts about the brain like the number of neurons and growth rate of neurons. It then discusses cognitive ability development through activities, memory, and experience. Soft computing techniques like neural networks, fuzzy logic, and genetic algorithms are presented as ways to understand cognition through applied neuroscience. Deep learning and convolutional neural networks are specifically highlighted as machine learning approaches for pattern recognition and classification.
Artificial Intelligence (ai) and Deep Learning ppt (By Shahrukh Shakeel)shahrukh1211
Artificial Intelligence (Ai) and Deep Learning with pictorial illustrations of Ai classifications and Machine Learning. This is a Research Paper Presentation on topic (Deep Learning Previous and Present Applications)
Artificial Intelligence And Its ApplicationsKnoldus Inc.
Artificial Intelligence(AI) is the simulation of human intelligence by machines. In other words, it is the method by which machines demonstrate certain aspects of human intelligence like learning, reasoning and self- correction. Since its inception, AI has demonstrated unprecedented growth. This learning process is inspired by us, the humans. In this knolx, we are going to discuss about this adaptation of learning processes.
The document discusses deep neural networks (DNN) and deep learning. It explains that deep learning uses multiple layers to learn hierarchical representations from raw input data. Lower layers identify lower-level features while higher layers integrate these into more complex patterns. Deep learning models are trained on large datasets by adjusting weights to minimize error. Applications discussed include image recognition, natural language processing, drug discovery, and analyzing satellite imagery. Both advantages like state-of-the-art performance and drawbacks like high computational costs are outlined.
IEEE EED2021 AI use cases in Computer VisionSAMeh Zaghloul
AI Use Cases in Computer Vision
Introduction and Overview about AI Use Cases in Computer Vision, to answer a basic question: “How Machines See?”, covering Neural Networks, Object detection and recognition, Content-based image retrieval, Object tracking, Image restoration, Scene reconstruction, Computer Vision Tools, Frameworks, Pretrained Models, and Public Train/Test Datasets.
With real-project examples on using Computer Vision in Egyptian Hieroglyph Alphabet recognition, Face Recognition/Matching, in addition to hands-on interactive session on Object/Image Tagging/Annotation on Videos/Images to prepare model training dataset.
This presentation provides an overview of artificial intelligence (AI) and deep learning. It begins with introductions to AI and deep learning, explaining that AI allows machines to perform tasks typically requiring human intelligence through machine learning. Deep learning is a type of machine learning using artificial neural networks inspired by the human brain. The presentation then discusses why AI has grown recently, citing increased computing power, data storage, and data availability. It also covers deep learning model development and concepts like underfitting and overfitting. The presentation describes different types of learning approaches like supervised, unsupervised, and reinforcement learning. It concludes with popular applications of deep learning like precision agriculture, computer vision, and recommendations.
Every thing about Artificial Intelligence Vaibhav Mishra
Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.
Cognitive ability of human brain and soft computing techniquesDr G R Sinha
The document discusses cognitive ability of the human brain and soft computing techniques. It begins with providing facts about the brain like the number of neurons and growth rate of neurons. It then discusses cognitive ability development through activities, memory, and experience. Soft computing techniques like neural networks, fuzzy logic, and genetic algorithms are presented as ways to understand cognition through applied neuroscience. Deep learning and convolutional neural networks are specifically highlighted as machine learning approaches for pattern recognition and classification.
Artificial Intelligence (ai) and Deep Learning ppt (By Shahrukh Shakeel)shahrukh1211
Artificial Intelligence (Ai) and Deep Learning with pictorial illustrations of Ai classifications and Machine Learning. This is a Research Paper Presentation on topic (Deep Learning Previous and Present Applications)
Artificial Intelligence And Its ApplicationsKnoldus Inc.
Artificial Intelligence(AI) is the simulation of human intelligence by machines. In other words, it is the method by which machines demonstrate certain aspects of human intelligence like learning, reasoning and self- correction. Since its inception, AI has demonstrated unprecedented growth. This learning process is inspired by us, the humans. In this knolx, we are going to discuss about this adaptation of learning processes.
The document discusses deep neural networks (DNN) and deep learning. It explains that deep learning uses multiple layers to learn hierarchical representations from raw input data. Lower layers identify lower-level features while higher layers integrate these into more complex patterns. Deep learning models are trained on large datasets by adjusting weights to minimize error. Applications discussed include image recognition, natural language processing, drug discovery, and analyzing satellite imagery. Both advantages like state-of-the-art performance and drawbacks like high computational costs are outlined.
IEEE EED2021 AI use cases in Computer VisionSAMeh Zaghloul
AI Use Cases in Computer Vision
Introduction and Overview about AI Use Cases in Computer Vision, to answer a basic question: “How Machines See?”, covering Neural Networks, Object detection and recognition, Content-based image retrieval, Object tracking, Image restoration, Scene reconstruction, Computer Vision Tools, Frameworks, Pretrained Models, and Public Train/Test Datasets.
With real-project examples on using Computer Vision in Egyptian Hieroglyph Alphabet recognition, Face Recognition/Matching, in addition to hands-on interactive session on Object/Image Tagging/Annotation on Videos/Images to prepare model training dataset.
This document provides an introduction and overview of deep learning, including its history and key figures. Deep learning is a breakthrough in machine learning that uses neural networks with multiple hidden layers to learn representations of data. It has gained traction in recent years due to increases in data, processing power, and algorithmic advances. Popular deep learning algorithms and tools are described.
Computer science department - a four page presentationmohamedsamyali
A minimalist presentation I gave in FCIS in 2006 about CS department. Some of the information is now out of date! I think it should be updated for nowadays :)
Vertex Perspectives | AI Optimized Chipsets | Part IIVertex Holdings
Deep learning is both computationally and memory intensive, necessitating enhancements in processor performance. In this issue, we explore how this has led to the rise of startups adopting alternative, innovative approaches and how it is expected to pave the way for different types of AI-optimized chipsets.
Introduction to the Artificial Intelligence and Computer Vision revolutionDarian Frajberg
Deep learning and computer vision have revolutionized artificial intelligence. Deep learning uses artificial neural networks inspired by the human brain to learn from large amounts of data without being explicitly programmed. Computer vision gives computers the ability to understand digital images and videos. Key breakthroughs include AlexNet achieving unprecedented accuracy on ImageNet in 2012, demonstrating the power of deep convolutional neural networks for computer vision tasks. Challenges remain around ensuring AI systems are beneficial to society, avoiding data biases, and increasing transparency.
The document provides an overview of artificial neural networks and deep learning. It begins with introductions to AI and machine learning, then discusses the history and basic concepts of artificial neural networks, including neurons, biological neural networks, and how ANNs learn through backpropagation. It also covers deep learning approaches like convolutional neural networks, recurrent neural networks, attention models, and recent achievements in language modeling. Examples of applications like autonomous vehicles are presented. It concludes with discussions of capsule networks and the SAS platform for deep learning.
This document summarizes a research paper that proposes a facial recognition system using a Raspberry Pi to unlock doors. It uses conventional techniques like Haar detection and PCA (principal component analysis). The system extracts features from faces and represents them as eigenfaces to encode facial identities. These eigenfaces are used as inputs to a neural network for facial classification and recognition. When a recognized face is detected, a servo motor will unlock the door automatically. If an unrecognized face is found, the system will notify an administrator. The goal is to create an efficient and affordable facial recognition access system as an alternative to keys or ID cards.
Aashirwad Kashyap is a third year B. Tech student studying Computer Science and Engineering at Indian Institute of Technology Ropar. He has technical skills in languages like C, C++, Java, Python and operating systems like Windows, Linux, and UNIX. Some of his projects include email fraud detection using machine learning, a three way security check system using face and voice recognition with machine learning and fingerprint scanning with image processing, and a weather app for Android. He is a member of the InterIIT volleyball team and the coding club at IIT Ropar.
This presentation attempts to explain some of the concepts used when describing data science, machine learning, and deep learning. IT also describes data science as a process, rather than as a set of specific tools and services.
This short presentation provides context for the field of AI today and makes some predictions about the advancements of the field in the enterprise in the next 5-10 years
Computational Intelligence: concepts and applications using AthenaPedro Almir
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence (AI) and is concentrated in the study of adaptive mechanisms to enable or facilitate intelligent behavior
in complex and changing environments. This presentation presents the key concepts of this area and how to use Athena to create intelligent systems. Athena is a visual tool developed aiming at offering a simple approach to the development of CI-based software systems, by dragging and dropping components in a visual environment, creating a new concept, that we call CI as a Service (CIaaS).
This document discusses data modeling and the eXERD data modeling tool. It provides definitions of data modeling, database modeling, and database modeling tools. Data modeling is used to simplify, visualize and document real-world information for computerization by determining the scope and relationships of entities. eXERD is introduced as a database modeling tool for everyone that is less expensive than other professional tools and more user-friendly. It automates repetitive tasks and allows for intuitive editing and integration with other tools.
This document provides an overview of artificial intelligence and its applications in cyber defense. It discusses topics like what AI is, the Turing test, fields of AI like expert systems, neural networks and intelligent agents. It provides examples of expert systems and their architecture. It also discusses applications of AI like credit granting, information retrieval and virus detection. Neural networks are described as artificial representations of the human brain that try to simulate its learning process. Different types of neural networks like biological and artificial are also mentioned.
The document summarizes a presentation on artificial general intelligence (AGI) given at the IntelliFest 2012 conference. It discusses the limitations of narrow AI and the constructivist approach needed for AGI. This involves self-constructing systems that can learn new tasks and adapt. The presentation highlights the HUMANOBS project, which uses a new architecture and programming language called Replicode to develop humanoid robots that can learn social skills through observation. Attention and temporal grounding are also identified as important issues for developing practical AGI systems.
Tejas Kumar Hoizal is pursuing a Master of Technology in VLSI from Indian Institute of Technology, Mandi. He has a Bachelors of Technology in Electronics and Communication Engineering from Vardhaman College of Engineering, Hyderabad. His past work experience includes being an Associate consultant at Capgemini Technology Services Limited, Bengaluru, where he developed algorithms and solutions to resolve issues in credit management systems. Currently, he is working on projects related to speech signal encryption/decryption using AES and performance analysis of CMOS full adders under the guidance of professors at IIT Mandi. His skills include programming in Verilog, C, Python and use of tools like Cadence.
CETPA Infotech offers deep learning training in Noida, providing participants with a comprehensive understanding of deep learning concepts and applications. The training program focuses on neural networks, deep neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced deep learning architectures. Participants will gain hands-on experience with popular deep learning frameworks and tools such as TensorFlow and Keras. Through practical projects, workshops, and expert-led sessions, participants will learn to develop and deploy deep learning models for various applications including computer vision, natural language processing, and predictive analytics. The deep learning training at CETPA Infotech in Noida equips participants with the skills and knowledge to excel in the rapidly evolving field of deep learning and opens up career opportunities in artificial intelligence and machine learning.
The upsurge of deep learning for computer vision applicationsIJECEIAES
Artificial intelligence (AI) is additionally serving to a brand new breed of corporations disrupt industries from restorative examination to horticulture. Computers can’t nevertheless replace humans, however, they will work superbly taking care of the everyday tangle of our lives. The era is reconstructing big business and has been on the rise in recent years which has grounded with the success of deep learning (DL). Cyber-security, Auto and health industry are three industries innovating with AI and DL technologies and also Banking, retail, finance, robotics, manufacturing. The healthcare industry is one of the earliest adopters of AI and DL. DL accomplishing exceptional dimensions levels of accurateness to the point where DL algorithms can outperform humans at classifying videos & images. The major drivers that caused the breakthrough of deep neural networks are the provision of giant amounts of coaching information, powerful machine infrastructure, and advances in academia. DL is heavily employed in each academe to review intelligence and within the trade-in building intelligent systems to help humans in varied tasks. Thereby DL systems begin to crush not solely classical ways, but additionally, human benchmarks in numerous tasks like image classification, action detection, natural language processing, signal process, and linguistic communication process.
Machine Learning and its subsequent fields have undergone tremendous growth in the past few years. It has a number of potential applications and is being used in different fields...
This document provides an overview of deep learning, including definitions, origins, applications, and limitations. It defines deep learning as a machine learning technique that uses multiple layers to learn representations of data. Deep learning algorithms attempt to learn multiple levels of representation using a hierarchy of layers. While deep learning has been used since around 2000, it has grown as a subset of machine learning focused on deep artificial neural networks. Deep learning can learn both unsupervised and supervised and is useful for tasks like speech recognition, natural language processing, image recognition, and self-driving cars. However, it requires large amounts of data and time to train models.
Deep learning is an emerging topic in artificial intelligence (AI). A subcategory of machine learning, deep learning deals with the use of neural networks to improve things like speech recognition, computer vision, and natural language processing. It's quickly becoming one of the most sought-after fields in computer science. In the last few years, deep learning has helped forge advances in areas as diverse as object perception, machine translation, and voice recognition--all research topics that have long been difficult for AI researchers to crack.
https://www.learntek.org/blog/machine-learning-vs-deep-learning/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
https://www.learntek.org/blog/machine-learning-vs-deep-learning/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
This document provides an introduction and overview of deep learning, including its history and key figures. Deep learning is a breakthrough in machine learning that uses neural networks with multiple hidden layers to learn representations of data. It has gained traction in recent years due to increases in data, processing power, and algorithmic advances. Popular deep learning algorithms and tools are described.
Computer science department - a four page presentationmohamedsamyali
A minimalist presentation I gave in FCIS in 2006 about CS department. Some of the information is now out of date! I think it should be updated for nowadays :)
Vertex Perspectives | AI Optimized Chipsets | Part IIVertex Holdings
Deep learning is both computationally and memory intensive, necessitating enhancements in processor performance. In this issue, we explore how this has led to the rise of startups adopting alternative, innovative approaches and how it is expected to pave the way for different types of AI-optimized chipsets.
Introduction to the Artificial Intelligence and Computer Vision revolutionDarian Frajberg
Deep learning and computer vision have revolutionized artificial intelligence. Deep learning uses artificial neural networks inspired by the human brain to learn from large amounts of data without being explicitly programmed. Computer vision gives computers the ability to understand digital images and videos. Key breakthroughs include AlexNet achieving unprecedented accuracy on ImageNet in 2012, demonstrating the power of deep convolutional neural networks for computer vision tasks. Challenges remain around ensuring AI systems are beneficial to society, avoiding data biases, and increasing transparency.
The document provides an overview of artificial neural networks and deep learning. It begins with introductions to AI and machine learning, then discusses the history and basic concepts of artificial neural networks, including neurons, biological neural networks, and how ANNs learn through backpropagation. It also covers deep learning approaches like convolutional neural networks, recurrent neural networks, attention models, and recent achievements in language modeling. Examples of applications like autonomous vehicles are presented. It concludes with discussions of capsule networks and the SAS platform for deep learning.
This document summarizes a research paper that proposes a facial recognition system using a Raspberry Pi to unlock doors. It uses conventional techniques like Haar detection and PCA (principal component analysis). The system extracts features from faces and represents them as eigenfaces to encode facial identities. These eigenfaces are used as inputs to a neural network for facial classification and recognition. When a recognized face is detected, a servo motor will unlock the door automatically. If an unrecognized face is found, the system will notify an administrator. The goal is to create an efficient and affordable facial recognition access system as an alternative to keys or ID cards.
Aashirwad Kashyap is a third year B. Tech student studying Computer Science and Engineering at Indian Institute of Technology Ropar. He has technical skills in languages like C, C++, Java, Python and operating systems like Windows, Linux, and UNIX. Some of his projects include email fraud detection using machine learning, a three way security check system using face and voice recognition with machine learning and fingerprint scanning with image processing, and a weather app for Android. He is a member of the InterIIT volleyball team and the coding club at IIT Ropar.
This presentation attempts to explain some of the concepts used when describing data science, machine learning, and deep learning. IT also describes data science as a process, rather than as a set of specific tools and services.
This short presentation provides context for the field of AI today and makes some predictions about the advancements of the field in the enterprise in the next 5-10 years
Computational Intelligence: concepts and applications using AthenaPedro Almir
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence (AI) and is concentrated in the study of adaptive mechanisms to enable or facilitate intelligent behavior
in complex and changing environments. This presentation presents the key concepts of this area and how to use Athena to create intelligent systems. Athena is a visual tool developed aiming at offering a simple approach to the development of CI-based software systems, by dragging and dropping components in a visual environment, creating a new concept, that we call CI as a Service (CIaaS).
This document discusses data modeling and the eXERD data modeling tool. It provides definitions of data modeling, database modeling, and database modeling tools. Data modeling is used to simplify, visualize and document real-world information for computerization by determining the scope and relationships of entities. eXERD is introduced as a database modeling tool for everyone that is less expensive than other professional tools and more user-friendly. It automates repetitive tasks and allows for intuitive editing and integration with other tools.
This document provides an overview of artificial intelligence and its applications in cyber defense. It discusses topics like what AI is, the Turing test, fields of AI like expert systems, neural networks and intelligent agents. It provides examples of expert systems and their architecture. It also discusses applications of AI like credit granting, information retrieval and virus detection. Neural networks are described as artificial representations of the human brain that try to simulate its learning process. Different types of neural networks like biological and artificial are also mentioned.
The document summarizes a presentation on artificial general intelligence (AGI) given at the IntelliFest 2012 conference. It discusses the limitations of narrow AI and the constructivist approach needed for AGI. This involves self-constructing systems that can learn new tasks and adapt. The presentation highlights the HUMANOBS project, which uses a new architecture and programming language called Replicode to develop humanoid robots that can learn social skills through observation. Attention and temporal grounding are also identified as important issues for developing practical AGI systems.
Tejas Kumar Hoizal is pursuing a Master of Technology in VLSI from Indian Institute of Technology, Mandi. He has a Bachelors of Technology in Electronics and Communication Engineering from Vardhaman College of Engineering, Hyderabad. His past work experience includes being an Associate consultant at Capgemini Technology Services Limited, Bengaluru, where he developed algorithms and solutions to resolve issues in credit management systems. Currently, he is working on projects related to speech signal encryption/decryption using AES and performance analysis of CMOS full adders under the guidance of professors at IIT Mandi. His skills include programming in Verilog, C, Python and use of tools like Cadence.
CETPA Infotech offers deep learning training in Noida, providing participants with a comprehensive understanding of deep learning concepts and applications. The training program focuses on neural networks, deep neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced deep learning architectures. Participants will gain hands-on experience with popular deep learning frameworks and tools such as TensorFlow and Keras. Through practical projects, workshops, and expert-led sessions, participants will learn to develop and deploy deep learning models for various applications including computer vision, natural language processing, and predictive analytics. The deep learning training at CETPA Infotech in Noida equips participants with the skills and knowledge to excel in the rapidly evolving field of deep learning and opens up career opportunities in artificial intelligence and machine learning.
The upsurge of deep learning for computer vision applicationsIJECEIAES
Artificial intelligence (AI) is additionally serving to a brand new breed of corporations disrupt industries from restorative examination to horticulture. Computers can’t nevertheless replace humans, however, they will work superbly taking care of the everyday tangle of our lives. The era is reconstructing big business and has been on the rise in recent years which has grounded with the success of deep learning (DL). Cyber-security, Auto and health industry are three industries innovating with AI and DL technologies and also Banking, retail, finance, robotics, manufacturing. The healthcare industry is one of the earliest adopters of AI and DL. DL accomplishing exceptional dimensions levels of accurateness to the point where DL algorithms can outperform humans at classifying videos & images. The major drivers that caused the breakthrough of deep neural networks are the provision of giant amounts of coaching information, powerful machine infrastructure, and advances in academia. DL is heavily employed in each academe to review intelligence and within the trade-in building intelligent systems to help humans in varied tasks. Thereby DL systems begin to crush not solely classical ways, but additionally, human benchmarks in numerous tasks like image classification, action detection, natural language processing, signal process, and linguistic communication process.
Machine Learning and its subsequent fields have undergone tremendous growth in the past few years. It has a number of potential applications and is being used in different fields...
This document provides an overview of deep learning, including definitions, origins, applications, and limitations. It defines deep learning as a machine learning technique that uses multiple layers to learn representations of data. Deep learning algorithms attempt to learn multiple levels of representation using a hierarchy of layers. While deep learning has been used since around 2000, it has grown as a subset of machine learning focused on deep artificial neural networks. Deep learning can learn both unsupervised and supervised and is useful for tasks like speech recognition, natural language processing, image recognition, and self-driving cars. However, it requires large amounts of data and time to train models.
Deep learning is an emerging topic in artificial intelligence (AI). A subcategory of machine learning, deep learning deals with the use of neural networks to improve things like speech recognition, computer vision, and natural language processing. It's quickly becoming one of the most sought-after fields in computer science. In the last few years, deep learning has helped forge advances in areas as diverse as object perception, machine translation, and voice recognition--all research topics that have long been difficult for AI researchers to crack.
https://www.learntek.org/blog/machine-learning-vs-deep-learning/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
https://www.learntek.org/blog/machine-learning-vs-deep-learning/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
This 4C16 module provides an introduction to machine learning and deep neural networks. The course covers fundamental machine learning algorithms and neural network architectures including logistic regression, classic classifiers, feedforward neural networks, convolutional neural networks, recurrent neural networks, and more. Students will complete hands-on labs programming in Python using Keras and TensorFlow on the Google Cloud Platform. The course aims to give students experience with machine learning applications in fields like computer vision, natural language processing, and more.
This document provides an introduction to deep learning in R. It defines artificial intelligence, machine learning, and deep learning. Deep learning uses multi-layered neural networks to learn data representations without requiring manual feature extraction. It notes that large datasets, powerful computers/GPUs, and new techniques for handling gradients have enabled recent advances in deep learning. Several R libraries for deep learning are listed, including Keras. An example sentiment analysis using Keras is shown, demonstrating defining and training a model on movie reviews to predict sentiment.
Deep learning is a branch of machine learning that uses artificial neural networks inspired by the human brain. These neural networks can learn complex patterns from large amounts of data without needing to be explicitly programmed. Deep learning uses neural networks that consist of interconnected layers that process data and learn hierarchical representations. Popular deep learning models include convolutional neural networks, recurrent neural networks, and deep belief networks.
This document discusses deep learning, including its relationship to artificial intelligence and machine learning. It describes deep learning techniques like artificial neural networks and how GPUs are useful for deep learning. Applications mentioned include computer vision, speech recognition, and bioinformatics. Both benefits like robustness and weaknesses like long training times are outlined. Finally, common deep learning algorithms, libraries and tools are listed.
This document provides an overview of deep learning and neural networks. It begins with definitions of machine learning, artificial intelligence, and the different types of machine learning problems. It then introduces deep learning, explaining that it uses neural networks with multiple layers to learn representations of data. The document discusses why deep learning works better than traditional machine learning for complex problems. It covers key concepts like activation functions, gradient descent, backpropagation, and overfitting. It also provides examples of applications of deep learning and popular deep learning frameworks like TensorFlow. Overall, the document gives a high-level introduction to deep learning concepts and techniques.
Deep Learning is the area of machine learning and one of the most talked about trends in business and computer science today.
In this talk, I will give a review of Deep Learning explaining what it is, what kinds of tasks it can do today, and what it probably could do in the future.
This document provides a copyright notice for slides from DeepLearning.AI courses. It states that the slides are distributed under a Creative Commons license for educational purposes, but cannot be used or distributed commercially without citing DeepLearning.AI as the source. A link is provided for more details on the license terms.
Toward enhancement of deep learning techniques using fuzzy logic: a survey IJECEIAES
This document provides an overview of deep learning techniques and how fuzzy logic can be used to enhance them. It discusses how deep learning works and some of its applications, such as self-driving cars, sentiment analysis, virtual assistants, and healthcare. It also provides an introduction to fuzzy logic and how it can simulate human thinking better than binary logic by allowing for degrees of truth. The document surveys previous studies that have combined deep learning and fuzzy logic models to improve deep learning performance by making the models better able to handle imprecise or ambiguous real-world data.
Machine learning, which is simply a neural network with three or more layers, is a subset of deep learning. Even though they are much below the capacity of the human brain, these neural networks make an effort to mimic its behaviour and enable it to "learn" from massive amounts of data. Burraq IT solutions provide the best Deep Learning Training courses in Lahore. While a single-layer neural network can still make rough predictions, the accuracy can be improved and optimized by adding hidden layers.
Machine learning is when computers learn from data without being explicitly programmed, by recognizing patterns in the data. There are three main types of machine learning: supervised learning where the machine learns under guidance from labeled data, unsupervised learning where the machine must figure out patterns without labels, and reinforcement learning where the machine learns from experience by discovering rewards or errors. Deep learning is a subset of machine learning that uses artificial neural networks inspired by the human brain to analyze data through supervised and unsupervised learning using large datasets. The main differences between machine learning and deep learning are that deep learning uses neural networks, requires huge datasets, and is self-reliant, while machine learning can work with smaller datasets and requires some human intervention.
Deep neural networks learn hierarchical representations of data through multiple layers of feature extraction. Lower layers identify low-level features like edges while higher layers integrate these into more complex patterns and objects. Deep learning models are trained on large labeled datasets by presenting examples, calculating errors, and adjusting weights to minimize errors over many iterations. Deep learning has achieved human-level performance on tasks like image recognition due to its ability to leverage large amounts of training data and learn representations automatically rather than relying on manually designed features.
Deep Learning for AI - Yoshua Bengio, MilaLucidworks
Deep Learning for AI
The keynote address covered several topics related to deep learning for AI:
1. Deep learning is based on the assumption that intelligence arises from general learning mechanisms that can acquire knowledge from data and experience.
2. Recent breakthroughs using deep learning have improved computer performance in areas like perception, language processing, games, and medical imaging analysis.
3. Deep learning exploits hierarchical feature learning through neural network architectures to allow machines to learn higher levels of abstraction from data, enabling better generalization.
4. While deep learning has achieved success, fully human-level AI still requires progress in unsupervised learning and constructing intuitive models from interacting with the world like humans do from a young age.
Similar to Deep Neural Network function of neural network and it application (20)
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
2. Agenda
AI,ML,DL
Deep Neural network
Working structure of deep neural network
Pros and cons of DL
Application of DKNW
Famous deep learning neural network
3.
4.
5. Deep learning
Deep learning is a subset of machine learning where artificial neural networks,
algorithms inspired by the human brain, learn from large amounts of data.
Deep learning is the one category of machine learning that emphasizes training
the computer about the basic instincts of human beings.
It is a prime technology behind the concept of virtual assistants, facial
recognition, driverless cars, etc.
The working of deep learning involves training the data and learning from the
experiences.
11. Advantage of Deep learning
Ability to generate new features from the limited available training data sets.
Its ability to work on unsupervised learning techniques helps in generating
actionable and reliable task outcomes.
It reduces the time required for feature engineering, one of the tasks that
requires major time in practicing machine learning. (Speaking of machine
learning.
With continuous training, its architecture has become adaptive to change and is
able to work on diverse problems.
12. Disadvantage of Deep learning
The cost of computational training significantly increases with an increase in
the number of datasets.
Lack of transparency in fault revision. No intermediate steps to provide the
arguments for a certain fault. In order to resolve the issue, a complete algorithm
gets revised.
Need for expensive resources, high-speed processing units and powerful GPU’s
for training to the data sets.
13. Famous Deep Neural Network
• Convolutional Neural Networks
• Deep Belief Networks
• Support Vector Machine
• K-means clustering
• Linear regression
• Recurrent Neural Network(RNN)