This document provides an introduction to deep learning. It discusses the history of machine learning and how neural networks work. Specifically, it describes different types of neural networks like deep belief networks, convolutional neural networks, and recurrent neural networks. It also covers applications of deep learning, as well as popular platforms, frameworks and libraries used for deep learning development. Finally, it demonstrates an example of using the Nvidia DIGITS tool to train a convolutional neural network for image classification of car park images.
This document provides an overview and introduction to deep learning including: what deep learning is, how it utilizes neural networks to learn patterns from large amounts of data, a brief history of deep learning, the differences between machine learning and deep learning, common deep learning architectures like artificial neural networks and deep neural networks, applications of deep learning like computer vision and natural language processing, and some of the major companies utilizing deep learning.
Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two common types of deep neural networks. RNNs include feedback connections so they can learn from sequence data like text, while CNNs are useful for visual data due to their translation invariance from pooling and convolutional layers. The document provides examples of applying RNNs and CNNs to tasks like sentiment analysis, image classification, and machine translation. It also discusses common CNN architecture components like convolutional layers, activation functions like ReLU, pooling layers, and fully connected layers.
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
The document discusses convolutional neural networks (CNNs). It begins with an introduction and overview of CNN components like convolution, ReLU, and pooling layers. Convolution layers apply filters to input images to extract features, ReLU introduces non-linearity, and pooling layers reduce dimensionality. CNNs are well-suited for image data since they can incorporate spatial relationships. The document provides an example of building a CNN using TensorFlow to classify handwritten digits from the MNIST dataset.
Tijmen Blankenvoort, co-founder Scyfer BV, presentation at Artificial Intelligence Meetup 15-1-2014. Introduction into Neural Networks and Deep Learning.
Deep Learning Tutorial | Deep Learning Tutorial For Beginners | What Is Deep ...Simplilearn
The document discusses deep learning and neural networks. It begins by defining deep learning as a subfield of machine learning that is inspired by the structure and function of the brain. It then discusses how neural networks work, including how data is fed as input and passed through layers with weighted connections between neurons. The neurons perform operations like multiplying the weights and inputs, adding biases, and applying activation functions. The network is trained by comparing the predicted and actual outputs to calculate error and adjust the weights through backpropagation to reduce error. Deep learning platforms like TensorFlow, PyTorch, and Keras are also mentioned.
This document provides an introduction to deep learning, including key developments in neural networks from the discovery of the neuron model in 1899 to modern networks with over 100 million parameters. It summarizes influential deep learning models such as AlexNet from 2012, ZF Net and GoogLeNet from 2013-2015, which helped reduce error rates on the ImageNet challenge. Top AI scientists who have contributed significantly to deep learning research are also mentioned. Common activation functions, convolutional neural networks, and deconvolution are briefly explained with examples.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
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.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
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.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Convolutional neural networks (CNNs) are a type of neural network used for image recognition tasks. CNNs use convolutional layers that apply filters to input images to extract features, followed by pooling layers that reduce the dimensionality. The extracted features are then fed into fully connected layers for classification. CNNs are inspired by biological processes and are well-suited for computer vision tasks like image classification, detection, and segmentation.
“Automatically learning multiple levels of representations of the underlying distribution of the data to be modelled”
Deep learning algorithms have shown superior learning and classification performance.
In areas such as transfer learning, speech and handwritten character recognition, face recognition among others.
(I have referred many articles and experimental results provided by Stanford University)
This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
This document provides an introduction to deep learning. It defines artificial intelligence, machine learning, data science, and deep learning. Machine learning is a subfield of AI that gives machines the ability to improve performance over time without explicit human intervention. Deep learning is a subfield of machine learning that builds artificial neural networks using multiple hidden layers, like the human brain. Popular deep learning techniques include convolutional neural networks, recurrent neural networks, and autoencoders. The document discusses key components and hyperparameters of deep learning models.
This document provides an overview of activation functions in deep learning. It discusses the purpose of activation functions, common types of activation functions like sigmoid, tanh, and ReLU, and issues like vanishing gradients that can occur with some activation functions. It explains that activation functions introduce non-linearity, allowing neural networks to learn complex patterns from data. The document also covers concepts like monotonicity, continuity, and differentiation properties that activation functions should have, as well as popular methods for updating weights during training like SGD, Adam, etc.
Batch normalization is a technique introduced in 2015 by Google researchers to address issues like internal covariate shift and vanishing gradients. It works by normalizing the inputs to each unit to have zero mean and unit variance based on the statistics of the mini-batch. This helps the network train deeper models with higher learning rates and be less sensitive to initialization. Batch normalization is applied before the activation function of each layer during both training and inference.
Transfer Learning and Fine-tuning Deep Neural NetworksPyData
This document outlines Anusua Trivedi's talk on transfer learning and fine-tuning deep neural networks. The talk covers traditional machine learning versus deep learning, using deep convolutional neural networks (DCNNs) for image analysis, transfer learning and fine-tuning DCNNs, recurrent neural networks (RNNs), and case studies applying these techniques to diabetic retinopathy prediction and fashion image caption generation.
Deep learning and neural networks are inspired by biological neurons. Artificial neural networks (ANN) can have multiple layers and learn through backpropagation. Deep neural networks with multiple hidden layers did not work well until recent developments in unsupervised pre-training of layers. Experiments on MNIST digit recognition and NORB object recognition datasets showed deep belief networks and deep Boltzmann machines outperform other models. Deep learning is now widely used for applications like computer vision, natural language processing, and information retrieval.
This document summarizes Melanie Swan's presentation on deep learning. It began with defining key deep learning concepts and techniques, including neural networks, supervised vs. unsupervised learning, and convolutional neural networks. It then explained how deep learning works by using multiple processing layers to extract higher-level features from data and make predictions. Deep learning has various applications like image recognition and speech recognition. The presentation concluded by discussing how deep learning is inspired by concepts from physics and statistical mechanics.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
Slides from Portland Machine Learning meetup, April 13th.
Abstract: You've heard all the cool tech companies are using them, but what are Convolutional Neural Networks (CNNs) good for and what is convolution anyway? For that matter, what is a Neural Network? This talk will include a look at some applications of CNNs, an explanation of how CNNs work, and what the different layers in a CNN do. There's no explicit background required so if you have no idea what a neural network is that's ok.
Large Scale Deep Learning with TensorFlow Jen Aman
Large-scale deep learning with TensorFlow allows storing and performing computation on large datasets to develop computer systems that can understand data. Deep learning models like neural networks are loosely based on what is known about the brain and become more powerful with more data, larger models, and more computation. At Google, deep learning is being applied across many products and areas, from speech recognition to image understanding to machine translation. TensorFlow provides an open-source software library for machine learning that has been widely adopted both internally at Google and externally.
by Dan Romuald Mbanga, Business Development Manager, AWS
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. In this workshop, we will provide an overview of deep learning focusing on getting started with the TensorFlow and Keras frameworks on AWS. Level 100
Big Data Malaysia - A Primer on Deep LearningPoo Kuan Hoong
This document provides an overview of deep learning, including a brief history of machine learning and neural networks. It discusses various deep learning models such as deep belief networks, convolutional neural networks, and recurrent neural networks. Applications of deep learning in areas like computer vision, natural language processing, and robotics are also covered. Finally, popular platforms, frameworks and libraries for developing deep learning systems are mentioned.
DSRLab seminar Introduction to deep learningPoo Kuan Hoong
Deep learning is a subfield of machine learning that has shown tremendous progress in the past 10 years. The success can be attributed to large datasets, cheap computing like GPUs, and improved machine learning models. Deep learning primarily uses neural networks, which are interconnected nodes that can perform complex tasks like object recognition. Key deep learning models include Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). CNNs are commonly used for computer vision tasks while RNNs are well-suited for sequential data like text or time series. Deep learning provides benefits like automatic feature learning and robustness, but also has weaknesses such
MDEC Data Matters Series: machine learning and Deep Learning, A PrimerPoo Kuan Hoong
The document provides an overview of machine learning and deep learning. It discusses the history and development of neural networks, including deep belief networks, convolutional neural networks, and recurrent neural networks. Applications of deep learning in areas like computer vision, natural language processing, and robotics are also covered. Finally, popular platforms, frameworks and libraries for developing deep learning models are presented, along with examples of pre-trained models that are available.
This document provides an introduction to deep learning. It begins with an overview of artificial intelligence techniques like computer vision, speech processing, and natural language processing that benefit from deep learning. It then reviews the history of deep learning algorithms from perceptrons to modern deep neural networks. The core concepts of deep learning processes, neural network architectures, and training techniques like backpropagation are explained. Popular deep learning frameworks like TensorFlow, Keras, and PyTorch are also introduced. Finally, examples of convolutional neural networks, recurrent neural networks, and generative adversarial networks are briefly described along with tips for training deep neural networks and resources for further learning.
Deep learning is introduced along with its applications and key players in the field. The document discusses the problem space of inputs and outputs for deep learning systems. It describes what deep learning is, providing definitions and explaining the rise of neural networks. Key deep learning architectures like convolutional neural networks are overviewed along with a brief history and motivations for deep learning.
Neural Networks and Deep Learning BasicsJon Lederman
This document provides an introduction to deep learning and neural networks. It discusses:
- Deep learning learns representations of data rather than relying on hand-engineered features.
- Deep learning architectures include neural networks, convolutional neural networks, and recurrent neural networks.
- Deep learning represents concepts in a nested hierarchy from simple to more abstract, with each layer learning slightly more complex representations. This allows it to learn its own feature detectors from raw data.
Deep learning techniques like convolutional neural networks (CNNs) and deep neural networks have achieved human-level performance on certain tasks. Pioneers in the field include Geoffrey Hinton, who co-invented backpropagation, Yann LeCun who developed CNNs for image recognition, and Andrew Ng who helped apply these techniques at companies like Baidu and Coursera. Deep learning is now widely used for applications such as image recognition, speech recognition, and distinguishing objects like dogs from cats, often outperforming previous machine learning methods.
Deep learning is a machine learning technique that uses artificial neural networks with multiple hidden layers to learn representations of data by increasing the level of abstraction from lower to higher layers. It has proven effective for multimedia data mining tasks like image tagging and caption generation. Deep neural networks can extract meaningful patterns from high-dimensional input using convolutional and recurrent layers, whereas shallow networks are limited. While deep learning has achieved good results, supervised approaches require large labeled datasets.
Training machine learning deep learning 2017Iwan Sofana
This document discusses deep learning and neural networks. It begins with a brief history of neural networks, from the earliest Perceptron algorithm in 1958 to modern developments enabled by increased computational power and data. Deep learning uses neural networks with multiple hidden layers to automatically learn representations of data and hierarchical feature detectors. Examples are given of applying deep learning to tasks like image recognition. The document outlines challenges of deep learning like the large amount of training required and complexity of modeling real-world behaviors.
Open Source AI and ML, Whats Possible Today?Justin Reock
After a quick refresher on deep learning and the composition of deep neural networks, drill down into how AirBnb, GE Healthcare, and Comma AI leverage various open source machine learning frameworks to achieve their goals. With a focus on TensorFlow, we’ll investigate the development process and decisions made by these three successful implementations of machine learning for real world applications.
WMCPA Quarterly
Upload photos Copy this Meetup
Things we will discuss are
1.Introduction of Machine learning and deep learning.
2.Applications of ML and DL.
3.Various learning algorithms of ML and DL.
4.Quick introduction of open source solutions for all programming languages.
5.Finally A broad picture of what you can do with Deep learning to this tech world.
This document provides an introduction to deep learning. It begins by discussing modeling human intelligence with machines and the history of neural networks. It then covers concepts like supervised learning, loss functions, and gradient descent. Deep learning frameworks like Theano, Caffe, Keras, and Torch are also introduced. The document provides examples of deep learning applications and discusses challenges for the future of the field like understanding videos and text. Code snippets demonstrate basic network architecture.
Neural networks are computational models inspired by the human brain that are used for machine learning. They consist of interconnected nodes that process information using a learning algorithm. Neural networks are used for applications like pattern recognition and classification. The first neural networks were developed in the 1940s-1950s, but modern networks use many layers of nodes, called deep learning, which has led to state-of-the-art performance in computer vision, natural language processing, and other domains. Deep learning requires large amounts of data and computational power but can automatically discover relevant features from data.
Unit one ppt of deeep learning which includes Ann cnnkartikaursang53
Deep learning involves using neural networks with multiple layers to automatically learn patterns from large amounts of data. The document discusses the working of deep learning networks, which take raw input data and pass it through successive hidden layers to determine higher-level features until reaching the output layer. It also covers applications of deep learning like image recognition and Amazon Alexa, as well as advantages such as automatic feature learning and ability to handle complex datasets.
Deep Learning: Evolution of ML from Statistical to Brain-like Computing- Data...Impetus Technologies
Presentation on 'Deep Learning: Evolution of ML from Statistical to Brain-like Computing'
Speaker- Dr. Vijay Srinivas Agneeswaran,Director, Big Data Labs, Impetus
The main objective of the presentation is to give an overview of our cutting edge work on realizing distributed deep learning networks over GraphLab. The objectives can be summarized as below:
- First-hand experience and insights into implementation of distributed deep learning networks.
- Thorough view of GraphLab (including descriptions of code) and the extensions required to implement these networks.
- Details of how the extensions were realized/implemented in GraphLab source – they have been submitted to the community for evaluation.
- Arrhythmia detection use case as an application of the large scale distributed deep learning network.
Deep learning: the future of recommendationsBalázs Hidasi
An informative talk about deep learning and its potential uses in recommender systems. Presented at the Budapest Startup Safary, 21 April, 2016.
The breakthroughs of the last decade in neural network research and the quick increasing of computational power resulted in the revival of deep neural networks and the field focusing on their training: deep learning. Deep learning methods have succeeded in complex tasks where other machine learning methods have failed, such as computer vision and natural language processing. Recently deep learning has began to gain ground in recommender systems as well. This talk introduces deep learning and its applications, with emphasis on how deep learning methods can solve long standing recommendation problems.
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
Build an efficient Machine Learning model with LightGBMPoo Kuan Hoong
Poo Kuan Hoong gives a presentation on building effective machine learning models with LightGBM. He begins with an introduction to decision trees and ensemble methods like gradient boosting. He explains that LightGBM is a gradient boosting framework that is faster and more accurate than other algorithms. It grows trees vertically rather than horizontally for increased speed and accuracy. Tips are provided for fine-tuning LightGBM like adjusting the number of leaves, learning rate, and using techniques like bagging and feature sub-sampling. A demo is then shown on a Kaggle dataset to predict safe drivers.
TensorFlow 2.0 focuses on simplicity and ease of use. It features Keras as the core API for building and training models using eager execution. It also improves support for deploying models to production on devices like mobile and embedded systems. Researchers can further experiment using new features like ragged tensors and TensorFlow Probability. While some APIs are being removed or renamed, there will be tools to assist migrating code from TensorFlow 1.x to 2.0.
TensorFlow and Keras are popular deep learning frameworks. TensorFlow is an open source library for numerical computation using data flow graphs. It was developed by Google and is widely used for machine learning and deep learning. Keras is a higher-level neural network API that can run on top of TensorFlow. It focuses on user-friendliness, modularization and extensibility. Both frameworks make building and training neural networks easier through modular layers and built-in optimization algorithms.
Explore and Have Fun with TensorFlow: Transfer LearningPoo Kuan Hoong
This document discusses transfer learning using TensorFlow. It begins with an introduction to deep learning and its applications. TensorFlow is introduced as an open-source library for machine learning using data flow graphs. Transfer learning is described as a technique where a model trained on one domain is reused on another domain by retraining or fine-tuning the last layers while keeping earlier layers fixed. This allows building accurate models using small datasets by leveraging knowledge gained from large datasets. The document demonstrates performing transfer learning using TensorFlow to retrain an Inception V3 model for a new image classification task.
Malaysia R User Group Meetup at Microsoft Malaysia, 13th July 2017. Facebook Page https://www.facebook.com/rusergroupmalaysia/
Video of the talk can be viewed here https://www.youtube.com/watch?v=lN057ua0dKU
Explore and have fun with TensorFlow: An introductory to TensorFlowPoo Kuan Hoong
This document provides an introduction to TensorFlow. It discusses key concepts like TensorFlow's architecture, variables, placeholders, gradients, and optimization. It also covers how to assemble and execute a TensorFlow graph with sessions. The presenter provides an overview of their background and links to TensorFlow user groups in Malaysia. The goal is to enable people to build and deploy their own deep learning models using TensorFlow and other libraries.
Deep learning refers to artificial neural networks with many layers. This document provides an introduction to deep learning and neural networks, including their strengths and weaknesses. It discusses popular deep learning libraries for R like H2O and MXNet. H2O allows users to perform distributed deep learning on large datasets using R. MXNet provides state-of-the-art deep learning models and efficient GPU computing capabilities for R. The document demonstrates how to customize neural networks and run deep learning models with H2O and MXNet in R.
Microsoft APAC Machine Learning & Data Science Community BootcampPoo Kuan Hoong
The document describes the Malaysia R User Group (MyRUG), which was started in June 2016 by Poo Kuan Hoong. MyRUG aims to provide a diverse group for members ranging from beginners to experts in R programming to share knowledge through bi-monthly meetups. The meetups feature talks by industry experts and practitioners as well as workshops and demos related to applying R. MyRUG has 203 members on Meetup and 379 followers on Facebook and has hosted 6 meetups covering topics like customer churn analytics, machine learning with Rattle and R, and speakers from companies like Microsoft, Oracle, and Sitecore Malaysia.
Customer Churn Analytics using Microsoft R OpenPoo Kuan Hoong
The document summarizes a presentation on using Microsoft R Open for customer churn analytics. It discusses using machine learning algorithms like logistic regression, support vector machines, and random forests to predict customer churn. It compares the performance of these models on a telecom customer dataset using metrics like confusion matrices and ROC curves. The presentation demonstrates building a churn prediction model in Microsoft R Open and R Tools for Visual Studio.
This document provides an overview of becoming a data scientist. It defines a data scientist and lists common job titles. It discusses the functions of a data scientist like devising business strategies, descriptive/predictive analytics, and data mining. Examples are provided of customer churn analysis and market basket analysis. The skills, aptitudes, and educational paths to become a data scientist are also outlined.
Handwritten Recognition using Deep Learning with RPoo Kuan Hoong
R User Group Malaysia Meet Up - Handwritten Recognition using Deep Learning with R
Source code available at: https://github.com/kuanhoong/myRUG_DeepLearning
The document discusses machine learning and big data research at the Data Science Institute of Multimedia University. The institute conducts research across various domains using machine learning techniques. Some areas of research include high performance computing for massive data sources, social media analytics, smart cities, and public health analytics. The document provides examples of how machine learning can be applied to problems in business analytics like predictive customer churn analysis and operations analytics like predictive maintenance. It also outlines the basic machine learning process of obtaining data, exploring it, building predictive models, applying and validating models, and taking action based on forecasts.
Context Aware Road Traffic Speech Information System from Social MediaPoo Kuan Hoong
This project develops a mobile application to transmit real-time traffic information to motorcyclists using data collected from Twitter. Traffic data from tweets is analyzed using named entity recognition, sentiment analysis, and statistical analysis to determine the traffic state. A Bluetooth-enabled helmet then plays the traffic state for the user based on their location provided by GPS coordinates.
Virtual Interaction Using Myo And Google Cardboard (slides)Poo Kuan Hoong
This document summarizes a student project that integrated the Myo armband with Google Cardboard to create an immersive virtual reality experience for learning Japanese characters. The project objectives were to develop a Google Cardboard app integrated with the Myo armband to enable user control of a 3D environment using gestures. The project scope involved using 5 gestures without positional tracking to display Japanese characters (hiragana) drawn in the air. Accomplishments included creating a 3D classroom, integrating the Myo, and adding sound effects to identify correct character strokes. Future work could improve the plugin response time and add more gestures.
A Comparative Study of HITS vs PageRank Algorithms for Twitter Users AnalysisPoo Kuan Hoong
Social Networks such as Facebook, Twitter, Google+
and LinkedIn have millions of users. These networks are constantly
evolving and it is a good source of information, both
explicitly and implicitly. The analysis of Social Network mainly
focuses on the aspect of social networking with an emphasis
on mapping relationships, patterns of interaction between user
and content information. One of the common research topics
focuses on the centrality measures where useful information of
the connected people in the social network is represented in
a graph. In this paper, we employed two link-based ranking
algorithms to analyze the ranking of the users: HITS (Hyperlink-
Induced Topic Search) and PageRank. We constructed Twitter
user retweet-relationship graph using 21 days worth of data.
Lastly, we compared the ranking sequence of the users in addition
to their followers count against the average and also whether
they are verified Twitter accounts. From the results obtained,
both HITS and PageRank showed a similar trend, and more
importantly highlighted the importance of the direction of the
edges in this work.
Towards Auto-Extracting Car Park Structures: Image Processing Approach on Low...Poo Kuan Hoong
There have been numerous interests in the area of detecting availability of car park bay using image processing techniques instead of utilizing expensive sensors. An area that has been neglected in doing so is the initial calibration of the image capturing device on the need to determine the car park structures. This paper proposes a technique that addresses this issue, using the limited processing capabilities of embedded systems. The results are promising, where in its current form, is semi-automated calibration for the car park structure detection and further enhancements can be made, to make it completely automated
Ensuring Secure and Permission-Aware RAG DeploymentsZilliz
In this talk, we will explore the critical aspects of securing Retrieval-Augmented Generation (RAG) deployments. The focus will be on implementing robust secured data retrieval mechanisms and establishing permission-aware RAG frameworks. Attendees will learn how to ensure that access control is rigorously maintained within the model when ingesting documents, ensuring that only authorized personnel can retrieve data. We will also discuss strategies to mitigate risks of data leakage, unauthorized access, and insider threats in RAG deployments. By the end of this session, participants will have a clearer understanding of the best practices and tools necessary to secure their RAG deployments effectively.
Using ScyllaDB for Real-Time Write-Heavy WorkloadsScyllaDB
Keeping latencies low for highly concurrent, intensive data ingestion
ScyllaDB’s “sweet spot” is workloads over 50K operations per second that require predictably low (e.g., single-digit millisecond) latency. And its unique architecture makes it particularly valuable for the real-time write-heavy workloads such as those commonly found in IoT, logging systems, real-time analytics, and order processing.
Join ScyllaDB technical director Felipe Cardeneti Mendes and principal field engineer, Lubos Kosco to learn about:
- Common challenges that arise with real-time write-heavy workloads
- The tradeoffs teams face and tips for negotiating them
- ScyllaDB architectural elements that support real-time write-heavy workloads
- How your peers are using ScyllaDB with similar workloads
How CXAI Toolkit uses RAG for Intelligent Q&AZilliz
Manasi will be talking about RAG and how CXAI Toolkit uses RAG for Intelligent Q&A. She will go over what sets CXAI Toolkit's Intelligent Q&A apart from other Q&A systems, and how our trusted AI layer keeps customer data safe. She will also share some current challenges being faced by the team.
TrustArc Webinar - Innovating with TRUSTe Responsible AI CertificationTrustArc
In a landmark year marked by significant AI advancements, it’s vital to prioritize transparency, accountability, and respect for privacy rights with your AI innovation.
Learn how to navigate the shifting AI landscape with our innovative solution TRUSTe Responsible AI Certification, the first AI certification designed for data protection and privacy. Crafted by a team with 10,000+ privacy certifications issued, this framework integrated industry standards and laws for responsible AI governance.
This webinar will review:
- How compliance can play a role in the development and deployment of AI systems
- How to model trust and transparency across products and services
- How to save time and work smarter in understanding regulatory obligations, including AI
- How to operationalize and deploy AI governance best practices in your organization
Planetek Italia is an Italian Benefit Company established in 1994, which employs 120+ women and men, passionate and skilled in Geoinformatics, Space solutions, and Earth science.
We provide solutions to exploit the value of geospatial data through all phases of data life cycle. We operate in many application areas ranging from environmental and land monitoring to open-government and smart cities, and including defence and security, as well as Space exploration and EO satellite missions.
Project Delivery Methodology on a page with activities, deliverablesCLIVE MINCHIN
I've not found a 1 pager like this anywhere so I created it based on my experiences. This 1 pager details a waterfall style project methodology with defined phases, activities, deliverables, assumptions. There's nothing in here that conflicts with commonsense.
Webinar: Transforming Substation Automation with Open Source SolutionsDanBrown980551
This webinar will provide an overview of open source software and tooling for digital substation automation in energy systems. The speakers will provide a brief overview of how open source collaborative development works in general, then delve into how it is driving innovation and accelerating the pace of substation automation. Examples of specific open source solutions and real-world implementations by utilities will be discussed. Participants will walk away with a better understanding of the challenges of automating substations, the ecosystem of solutions available to help, and best practices for implementing them.
Airports, banks, stock exchanges, and countless other critical operations got thrown into chaos!
In an unprecedented event, a recent CrowdStrike update had caused a global IT meltdown, leading to widespread Blue Screen of Death (BSOD) errors, and crippling 8.5 million Microsoft Windows systems.
What triggered this massive disruption? How did Microsoft step in to provide a lifeline? And what are the next steps for recovery?
Swipe to uncover the full story, including expert insights and recovery steps for those affected.
The Challenge of Interpretability in Generative AI Models.pdfSara Kroft
Navigating the intricacies of generative AI models reveals a pressing challenge: interpretability. Our blog delves into the complexities of understanding how these advanced models make decisions, shedding light on the mechanisms behind their outputs. Explore the latest research, practical implications, and ethical considerations, as we unravel the opaque processes that drive generative AI. Join us in this insightful journey to demystify the black box of artificial intelligence.
Dive into the complexities of generative AI with our blog on interpretability. Find out why making AI models understandable is key to trust and ethical use and discover current efforts to tackle this big challenge.
IVE 2024 Short Course - Lecture 2 - Fundamentals of PerceptionMark Billinghurst
Lecture 2 from the IVE 2024 Short Course on the Psychology of XR. This lecture covers some of the Fundamentals of Percetion and Psychology that relate to XR.
The lecture was given by Mark Billinghurst on July 15th 2024 at the University of South Australia.
2. Data Science Institute
• The Data Science Institute is a research
center based in the Faculty of Computing
& Informatics, Multimedia University.
• The members comprise of expertise
across faculties such as Faculty of
Computing and Informatics, Faculty of
Engineering, Faculty of Management &
Faculty of Information Science and
Technology.
• Conduct research in leading data science
areas including stream mining, video
analytics, machine learning, deep
learning, next generation data
visualization and advanced data
modelling.
6. Acknowledgement
Andrew Ng: Deep Learning,
Self-Taught Learning and
Unsupervised Feature
Learning [Youtube]
Yann LeCun: Deep
Learning Tutorial, ICML,
Atlanta, 2013 [PDF]
Geoff Hinton, Yoshua
Bengio & Yann LeCun:
Deep Learning: NIPS2015
Tutorial [PDF]
Yoshua Bengio: Theano: A Python
framework for fast computation of
mathematical expressions. [URL]
Andrej Karpathy: Visualizing and
Understanding Recurrent Networks, ICLR
2016, [PDF]
7. Outline
• A brief history of machine learning
• Understanding the human brain
• Neural Network: Concept, implementation and challenges
• Deep Belief Network (DBN): Concept and Application
• Convolutional Neural Network (CNN): Concept and Application
• Recurrent Neural Network (RNN): Concept and Application
• Deep Learning: Strengths, weaknesses and applications
• Deep Learning: Platforms, frameworks and libraries
• Demo
8. Introduction
• In the past 10 years, machine learning and
Artificial Intelligence have shown
tremendous progress
• The recent success can be attributed to:
• Explosion of data
• Cheap computing cost – CPUs and GPUs
• Improvement of machine learning models
• Much of the current excitement concerns a
subfield of it called “deep learning”.
9. A brief history of Machine learning
• Most of the machine learning methods are based on supervised
learning
Input
Feature
Representation
Learning Algorithm
16. Neural Network
• Deep Learning is primarily about neural networks, where a network is
an interconnected web of nodes and edges.
• Neural nets were designed to perform complex tasks, such as the task
of placing objects into categories based on a few attributes.
• Neural nets are highly structured networks, and have three kinds of
layers - an input, an output, and so called hidden layers, which refer
to any layers between the input and the output layers.
• Each node (also called a neuron) in the hidden and output layers has
a classifier.
18. Neural Network: Forward Propagation
• The input neurons first receive the
data features of the object. After
processing the data, they send their
output to the first hidden layer.
• The hidden layer processes this output
and sends the results to the next
hidden layer.
• This continues until the data reaches
the final output layer, where the
output value determines the object's
classification.
• This entire process is known as
Forward Propagation, or Forward prop.
19. Neural Network: Backward Propagation
• To train a neural network over a large set of labelled data, you must
continuously compute the difference between the network’s
predicted output and the actual output.
• This difference is called the cost, and the process for training a net is
known as backpropagation, or backprop
• During backprop, weights and biases are tweaked slightly until the
lowest possible cost is achieved.
• An important aspect of this process is the gradient, which is a
measure of how much the cost changes with respect to a change in a
weight or bias value.
20. The 1990s view of what was wrong with back-
propagation
• It required a lot of labelled training data
• almost all data is unlabeled
• The learning time did not scale well
• It was very slow in networks with multiple hidden layers.
• It got stuck at local optima
• These were often surprisingly good but there was no good theory
21. Deep Belief Network (DBN)
• The Deep Belief Network, or DBN,
was also conceived by Geoff Hinton.
• Used by Google for their work on the
image recognition problem.
• DBN is trained two layers at a time,
and these two layers are treated like
an RBM.
• Throughout the net, the hidden layer
of an RBM acts as the input layer of
the adjacent one. So the first RBM is
trained, and its outputs are then
used as inputs to the next RBM. This
procedure is repeated until the
output layer is reached.
22. Deep Belief Network (DBN)
• DBN is capable of recognizing the inherent patterns in the data. In
other words, it’s a sophisticated, multilayer feature extractor.
• The unique aspect of this type of net is that each layer ends up
learning the full input structure.
• Layers generally learn progressively complex patterns – for facial
recognition, early layers could detect edges and later layers would
combine them to form facial features.
• DBN learns the hidden patterns globally, like a camera slowly bringing
an image into focus.
• DBN still requires a set of labels to apply to the resulting patterns. As
a final step, the DBN is fine-tuned with supervised learning and a
small set of labeled examples.
24. Convolutional Neural Network (CNN)
• CNN inspired by the Visual Cortex.
• CNNs are deep nets that are used for image, object, and even speech
recognition.
• Pioneered by Yann Lecun (NYU)
• Deep supervised neural networks are generally too difficult to train.
• CNNs have multiple types of layers, the first of which is the
convolutional layer.
25. Convolutional Neural Network (CNN)
• A series of filters forms layer one, called the convolutional layer. The weights and
biases in this layer determine the effectiveness of the filtering process.
• Each flashlight represents a single neuron. Typically, neurons in a layer activate or
fire. On the other hand, in the convolutional layer, neurons search for patterns
through convolution. Neurons from different filters search for different patterns,
and thus they will process the input differently.
Filter 2/ Neural 2
W1=10
W3=4
W2=5
27. CNN: Application
• Classify a scene in an image
• Image Classifier Demo (NYU): http://horatio.cs.nyu.edu/
• Describe or understanding an image
• Toronto Deep Learning Demo: http://deeplearning.cs.toronto.edu/i2t
• MIT Scene Recognition Demo: http://places.csail.mit.edu/demo.html
• Handwriting recognition
• Handwritten digits recognition:
http://cs.stanford.edu/people/karpathy/convnetjs/demo/mnist.html
• Video classification
• Large-scale Video Classification with Convolutional Neural Networks
http://cs.stanford.edu/people/karpathy/deepvideo/
28. Recurrent Neural Network (RNN)
• The Recurrent Neural Net (RNN) is
the brainchild of Juergen
Schmidhuber and Sepp Hochreiter.
• RNNs have a feedback loop where
the net’s output is fed back into
the net along with the next input.
• RNNs receive an input and produce
an output. Unlike other nets, the
inputs and outputs can come in a
sequence.
• Variant of RNN is Long Term Short
Memory (LSTM)
29. RNN: Application
• RNN is suitable for time series data, where an output can be the next
value in a sequence, or the next several values
Classify Image frame by
frame
Image captioning Document Classification
30. Deep Learning: Benefits
• Robust
• No need to design the features ahead of time – features are automatically
learned to be optimal for the task at hand
• Robustness to natural variations in the data is automatically learned
• Generalizable
• The same neural net approach can be used for many different applications
and data types
• Scalable
• Performance improves with more data, method is massively parallelizable
31. Deep Learning: Weaknesses
• Deep Learning requires a large dataset, hence long training period.
• In term of cost, Machine Learning methods like SVMs and other tree
ensembles are very easily deployed even by relative machine learning
novices and can usually get you reasonably good results.
• Deep learning methods tend to learn everything. It’s better to encode prior
knowledge about structure of images (or audio or text).
• The learned features are often difficult to understand. Many vision features
are also not really human-understandable (e.g,
concatenations/combinations of different features).
• Requires a good understanding of how to model multiple modalities with
traditional tools.
35. Deep Learning: Platform & Frameworks &
Libraries
Platform
• Ersatz Labs - cloud-based deep learning platform [http://www.ersatz1.com/]
• H20 – deep learning framework that comes with R and Python interfaces
[http://www.h2o.ai/verticals/algos/deep-learning/]
Framework
• Caffe - deep learning framework made with expression, speed, and modularity in mind.
Developed by the Berkeley Vision and Learning Center (BVLC)
[http://caffe.berkeleyvision.org/]
• Torch - scientific computing framework with wide support for machine learning
algorithms that puts GPUs first. Based on Lua programming language [http://torch.ch/]
Library
• Tensorflow - open source software library for numerical computation using data flow
graphs from Google [https://www.tensorflow.org/]
• Theano - a python library developed by Yoshua Bengio’s team
[http://deeplearning.net/software/theano/]
36. Learned Models
• Trained Models can be shared with others
• Save the training time
• For example: AlexNet, GoogLeNet, ParseNet, etc
• URLs:
• https://github.com/BVLC/caffe/wiki/Model-Zoo
• http://deeplearning4j.org/model-zoo
38. Nvidia: Digits
• The NVIDIA Deep Learning GPU Training System (DIGITS) puts the
power of deep learning in the hands of data scientists and
researchers.
• Quickly design the best deep neural network (DNN) for your data
using real-time network behavior visualization.
• https://developer.nvidia.com/digits