This document provides an overview of deep learning including:
- Deep learning uses multiple layers of nonlinear processing units for feature extraction and transformation from input data.
- Deep learning architectures like deep neural networks have been applied to fields including computer vision, speech recognition, and natural language processing.
- Training deep networks involves learning features from raw data in an unsupervised manner before fine-tuning in a supervised way using labeled data.
- Popular deep learning models covered include convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks.
- Deep learning has achieved success in applications such as image recognition, generation and style transfer, as well as natural language processing, audio processing, and medical domains.
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
Character Recognition using Artificial Neural NetworksJaison Sabu
Mini Project, Computer Science Department, College of Engineering Chengannur 2003-2007, Affiliated to Cochin University of Science and Technology (CUSAT), Kerala, India
Deep learning is now making the Artificial Intelligence near to Human. Machine Learning and Deep Artificial Neural Network make the copy of Human Brain. The success is due to large storage, computation with efficient algorithms to handle more behavioral and cognitive problem
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
Character Recognition using Artificial Neural NetworksJaison Sabu
Mini Project, Computer Science Department, College of Engineering Chengannur 2003-2007, Affiliated to Cochin University of Science and Technology (CUSAT), Kerala, India
Deep learning is now making the Artificial Intelligence near to Human. Machine Learning and Deep Artificial Neural Network make the copy of Human Brain. The success is due to large storage, computation with efficient algorithms to handle more behavioral and cognitive problem
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
This article aims to classify texts and predict the categories of occurrences, through the study of Artificial Intelligence models, using Machine Learning and Deep Learning for the classification of texts and analysis of predictions, suggesting the best option with the smallest error.
The solution was designed to be implemented in two stages: Machine Learning and Application, according to the diagram below from the Data Science Academy.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. contents
Introduction and overveiw
Deep learning challenges
Deep N.N
Unsupervised Preprocessing Networks
Deep Belief Networks
Denoising auto encoder
Stacked Auto Encoders
Deep Boltzmann Machines
CNN – Convolutional Neural Networks
Recurrent N.N
Long Short-Term Memory RNN (LSTM)
Generative Adversarial Neural
Deep Reinforcement Learning
Applications.
2
3. introduction
Deep learning (also known as deep structured
learning or hierarchical learning) is part of a broader family
of machine learning methods based on learning data
representations, as opposed to task-specific algorithms.
Learning can be supervised, semi-
supervised or unsupervised.
use a cascade of multiple layers of nonlinear
processing units for feature extraction and transformation.
Each successive layer uses the output from the previous layer
as input
Deep learning architectures such as deep neural
networks, deep belief networks and recurrent neural
networks have been applied to fields including computer
vision, speech recognition, natural language processing,
4. Introduction cont..
Deep learning algorithms can be applied to unsupervised learning
tasks.
This is an important benefit because unlabeled data are more
abundant than labeled data.
5. Inspired by the Brain
The first hierarchy of neurons that receives information in the
visual cortex are sensitive to specific edges while brain regions
further down the visual pipeline are sensitive to more complex
structures such as faces.
Our brain has lots of neurons connected together and the
strength of the connections between neurons represents long
term knowledge.
6. Deep Learning training
Overview
Train networks with many layers (Multiple layers work to build an
improved feature space
First layer learns 1st
order features (e.g. edges…)
2nd
layer learns higher order features (combinations of first layer
features, combinations of edges, etc.)
Some models learn in an unsupervised mode and discover general
features of the input space – serving multiple tasks related to the
unsupervised instances (image recognition, etc.)
Final layer of transformed features are fed into supervised layer(s)
And entire network is often subsequently tuned using supervised training of
the entire net, using the initial weightings learned in the unsupervised phase
7. Deep Learning Architecture
A deep neural network consists of a hierarchy of layers, whereby each
layer transforms the input data into more abstract representations (e.g.
edge -> nose -> face). The output layer combines those features to make
predictions
11. Problems with Back Propagation
Gradient is progressively getting more dilute
Below top few layers, correction signal is minimal
Gets stuck in local minima
Especially since they start out far from ‘good’ regions
(i.e., random initialization)
12. DNN challenges
As with ANNs, many issues can arise with naively trained DNNs. Two
common issues are overfitting and computation time.
DNNs are prone to overfitting because of the added layers of
abstraction, which allow them to model rare dependencies in the
training data.
Regularization methods such as Ivakhnenko's unit pruning or
weight decay (regularization) or sparsity (regularization) can be
applied during training to combat overfitting. Alternatively dropout
regularization randomly omits units from the hidden layers during
training.
This helps to exclude rare dependencies.
Finally, data can be augmented via methods such as cropping
and rotating such that smaller training sets can be increased in size
to reduce the chances of overfitting.
13. Challenge cont..
DNNs must consider many training parameters, such as
the size (number of layers and number of units per layer),
the learning rate and initial weights.
Sweeping through the parameter space for optimal
parameters may not be feasible due to the cost in time
and computational resources.
Various tricks such as batching (computing the gradient
on several training examples at once rather than
individual examples) speed up computation.
The large processing throughput of GPUs has produced
significant speedups in training, because the matrix and
vector computations required are well-suited for GPUs.
14. Challenge Cont..
Alternatively, we may need to look for other type of
neural network which has straightforward and
convergent training algorithm.
CMAC (cerebellar model articulation controller) is such
kind of neural network. For example, there is no need to
adjust learning rates or randomize initial weights for
CMAC. The training process can be guaranteed to
converge in one step with a new batch of data, and the
computational complexity of the training algorithm is
linear with respect to the number of neurons involved
15. Greedy Layer-Wise Training
1. Train first layer using your data without the labels (unsupervised)
Since there are no targets at this level, labels don't help.
Then freeze the first layer parameters and start training the second
layer using the output of the first layer as the unsupervised input to the
second layer
1. Repeat this for as many layers as desired
This builds the set of robust features
1. Use the outputs of the final layer as inputs to a supervised
layer/model and train the last supervised layer (s) (leave early
weights frozen)
2. Unfreeze all weights and fine tune the full network by training with
a supervised approach, given the pre-training weight settings
15
16. David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com
These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html
17. Deep Belief Networks(DBNs)
Unsupervised pre-learning provides a good initialization
of the network
Probabilistic generative model
Deep architecture – multiple layers
Supervised fine-tuning
Generative: Up-down algorithm
Discriminative: backpropagation
18.
19. DBN Greedy training
First step:
Construct an RBM with an input layer v and a hidden
layer h
Train the RBM
A restricted Boltzmann machine (RBM) is:
a generative stochastic artificial neural network that
can learn a probability distribution over its set of inputs.
20.
21.
22. Auto-Encoders
A type of unsupervised learning,
An autoencoder is typically a feedforward neural network which aims to
learn a compressed, distributed representation (encoding) of a dataset.
Conceptually, the network is trained to “recreate” the input, i.e., the
input and the target data are the same. In other words: you’re trying to
output the same thing you were input, but compressed in some way.
In effect, we want a few small nodes in the middle to really learn the
data at a conceptual level, producing a compact representation that in
some way captures the core features of our input.
22
23. David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com
These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html
26. David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com
These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html
30. Convolutional Neural Nets (CNN)
Convolution layers a feature detector that automagically learns to filter out
not needed information from an input by using convolution kernel.
Pooling layers compute the max or average value of a particular feature over
a region of the input data (downsizing of input images).Also helps to detect
objects in some unusual places and reduces memory size.
31. CNN
High accuracy for image applications – Breaking all records and
doing it using just raw pixel features.
Special purpose net – Just for images or problems with strong grid-like
local spatial/temporal correlation
Once trained on one problem (e.g. vision) could use same net (often
tuned) for a new similar problem – general creator of vision features
Unlike traditional nets, handles variable sized inputs
Same filters and weights, just convolve across different sized image and
dynamically scale size of pooling regions (not # of nodes), to normalize
Different sized images, different length speech segments, etc.
Lots of hand crafting and CV tuning to find the right recipe of
receptive fields, layer interconnections, etc.
Lots more Hyperparameters than standard nets, and even than other
deep networks, since the structures of CNNs are more handcrafted
CNNs getting wider and deeper with speed-up techniques (e.g. GPU,
ReLU, etc.) and lots of current research, excitement, and success
31
54. Finally ..
That’s the basic idea..
There are many types of deep learning,
different kinds of autoencoder, variations on architectures and
training algorithms, etc…
Very fast growing area …
If do full supervised, we may not bet the benefits of building up the incrementally abstracted feature space
Steps 1-4 called pre-training as it gets the weights close enough so that standard training in step 5 can be effective
Do fine tuning for sure if lots of labeled data, if little labeled data, not as helpful.
Though Deep Nets done first, start with auto-encoders because they are simpler
Mention Zipser auotencoder with reverse engineering, then Cottrell compression where unable to reverse engineer
If h is smaller than x then “undercomplete” autoencoding – also would use “regularized” autoencoding
Can use just new features in the new training set or concatenate both original and new
Dynamic size – Pooling region just sums/maxes over an area with one final weight so no hard changes when we adjust pool region size
Simard 2003, Simple consistent CNN structure 5x5 with 2x2 subsampling with number of features 5 in first C-layer, 50 in next, until too small.
Don’t actually used pool layer as instead just connect every other node which samples rather than max/average.
Each layer reduces feature size by (n-3)/2. Just two layers for mnist.
They also use elastic distortions which is a type of jitter to get increased data. 99.6% - best at the time, Distortions also help a lot with standard MLP
Thus an approach with less Hyperparameter fiddling
Ciresan and Schmidhuber 2012, Multi column DNN. CNN with depth 6-10 (deeper if initial input image is bigger), and wider on fields, 1-2 hidden layers in MLP, columns are CNNs (an ensemble with different parameters, features, etc.) where their output is averaged, jitter inputs, multi-day GPU training, annealed LR (.001 dropping to .00003) 99.76% mnist