ML refers to systems that can learn by themselves. Systems that get smarter and smarter over time without human intervention. Deep Learning (DL) is ML but applied to large data sets.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/can-you-see-what-i-see-the-power-of-deep-learning-a-presentation-from-streamlogic/
Scott Thibault, President and Founder of StreamLogic, presents the “Can You See What I See? The Power of Deep Learning” tutorial at the September 2020 Embedded Vision Summit.
It’s an exciting time to work in computer vision, mainly due to the technological advances in the area of deep learning. This talk is an introduction to some of the most important computer vision tasks that can be solved with deep learning.
In particular, Thibault focuses on the application of convolutional neural networks to the tasks of image classification, object detection and facial image recognition using embeddings. You will learn about the types of applications in which DNNs performing these functions are typically used, and discover some of the publicly available models and data sets that you can use to help bootstrap your own applications.
ML refers to systems that can learn by themselves. Systems that get smarter and smarter over time without human intervention. Deep Learning (DL) is ML but applied to large data sets.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/can-you-see-what-i-see-the-power-of-deep-learning-a-presentation-from-streamlogic/
Scott Thibault, President and Founder of StreamLogic, presents the “Can You See What I See? The Power of Deep Learning” tutorial at the September 2020 Embedded Vision Summit.
It’s an exciting time to work in computer vision, mainly due to the technological advances in the area of deep learning. This talk is an introduction to some of the most important computer vision tasks that can be solved with deep learning.
In particular, Thibault focuses on the application of convolutional neural networks to the tasks of image classification, object detection and facial image recognition using embeddings. You will learn about the types of applications in which DNNs performing these functions are typically used, and discover some of the publicly available models and data sets that you can use to help bootstrap your own applications.
Global Load Instruction Aggregation Based on Code MotionYasunobu Sumikawa
The 2012 International Symposium on Parallel Architecture, Algorithm and Programming.
If you download this file, you can see explanation of each slides.
Using parallel programming to improve performance of image processingChan Le
Implement Anisotropic Diffusion on CUDA platform
1 thread handle 1 pixel
Dividing the image to multiple sub-regions, process them parallely to exploit multiple cores
explain backpropagation with a simple example.
normally, we use cross-entropy as loss function.
and we set the activation function of the output layer as the logistic sigmoid. because we want to maximize (log) likelihood. (or minimize negative (log) likelihood), and we suppose that the function is a binomial distribution which is the maximum entropy function in two-class classification.
but in this example, we set the loss function (objective function or cost function) as sum of square, which is normally used in logistic regression, for simplifying the problem.
Social networks are not new, even though websites like Facebook and Twitter might make you want to believe they are; and trust me- I’m not talking about Myspace! Social networks are extremely interesting models for human behavior, whose study dates back to the early twentieth century. However, because of those websites, data scientists have access to much more data than the anthropologists who studied the networks of tribes!
Because networks take a relationship-centered view of the world, the data structures that we will analyze model real world behaviors and community. Through a suite of algorithms derived from mathematical Graph theory we are able to compute and predict behavior of individuals and communities through these types of analyses. Clearly this has a number of practical applications from recommendation to law enforcement to election prediction, and more.
Learning Predictive Modeling with TSA and KaggleYvonne K. Matos
Ever wanted to do a challenging data science project but feel like you don’t have enough experience? Just go for it! Diving into a 3 TB, 3D image dataset has been my best learning experience. I want to share this deep learning project with you, and tips for overcoming challenges along the way.
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data
Yufeng Guo | Coding the 7 steps of machine learning | Codemotion Madrid 2018 Codemotion
Machine learning has gained a lot of attention as the next big thing. But what is it, really, and how can we use it? In this talk, you'll learn the meaning behind buzzwords like hyperparameter tuning, and see the code behind each step of machine learning. This talk will help demystify the "magic" behind machine learning. You'll come away with a foundation that you can build on, and an understanding of the tools to build with!
DN 2017 | Multi-Paradigm Data Science - On the many dimensions of Knowledge D...Dataconomy Media
Gaining insight from data is not as straightforward as we often wish it would be – as diverse as the questions we’re asking are the quality and the quantity of the data we may have at hand. Any attempt to turn data into knowledge thus strongly depends on it dealing with big or not-so-big data, high- or low-dimensional data, exact or fuzzy data, exact or fuzzy questions, and the goal being accurate prediction or understanding. This presentation emphasizes the need for a multi-paradigm data science to tackle all the challenges we are facing today and may be facing in the future. Luckily, solutions are starting to emerge...
Need an detailed analysis of what this code-model is doing- Thanks #St.pdfactexerode
Need an detailed analysis of what this code/model is doing. Thanks
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
# Generate a batch of random noise
noise = np.random.normal(0, 1, (16, latent_dimensions))
# Generate images from the noise
generated_images = generator.predict(noise)
# Rescale the images to 0.
Global Load Instruction Aggregation Based on Code MotionYasunobu Sumikawa
The 2012 International Symposium on Parallel Architecture, Algorithm and Programming.
If you download this file, you can see explanation of each slides.
Using parallel programming to improve performance of image processingChan Le
Implement Anisotropic Diffusion on CUDA platform
1 thread handle 1 pixel
Dividing the image to multiple sub-regions, process them parallely to exploit multiple cores
explain backpropagation with a simple example.
normally, we use cross-entropy as loss function.
and we set the activation function of the output layer as the logistic sigmoid. because we want to maximize (log) likelihood. (or minimize negative (log) likelihood), and we suppose that the function is a binomial distribution which is the maximum entropy function in two-class classification.
but in this example, we set the loss function (objective function or cost function) as sum of square, which is normally used in logistic regression, for simplifying the problem.
Social networks are not new, even though websites like Facebook and Twitter might make you want to believe they are; and trust me- I’m not talking about Myspace! Social networks are extremely interesting models for human behavior, whose study dates back to the early twentieth century. However, because of those websites, data scientists have access to much more data than the anthropologists who studied the networks of tribes!
Because networks take a relationship-centered view of the world, the data structures that we will analyze model real world behaviors and community. Through a suite of algorithms derived from mathematical Graph theory we are able to compute and predict behavior of individuals and communities through these types of analyses. Clearly this has a number of practical applications from recommendation to law enforcement to election prediction, and more.
Learning Predictive Modeling with TSA and KaggleYvonne K. Matos
Ever wanted to do a challenging data science project but feel like you don’t have enough experience? Just go for it! Diving into a 3 TB, 3D image dataset has been my best learning experience. I want to share this deep learning project with you, and tips for overcoming challenges along the way.
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data
Yufeng Guo | Coding the 7 steps of machine learning | Codemotion Madrid 2018 Codemotion
Machine learning has gained a lot of attention as the next big thing. But what is it, really, and how can we use it? In this talk, you'll learn the meaning behind buzzwords like hyperparameter tuning, and see the code behind each step of machine learning. This talk will help demystify the "magic" behind machine learning. You'll come away with a foundation that you can build on, and an understanding of the tools to build with!
DN 2017 | Multi-Paradigm Data Science - On the many dimensions of Knowledge D...Dataconomy Media
Gaining insight from data is not as straightforward as we often wish it would be – as diverse as the questions we’re asking are the quality and the quantity of the data we may have at hand. Any attempt to turn data into knowledge thus strongly depends on it dealing with big or not-so-big data, high- or low-dimensional data, exact or fuzzy data, exact or fuzzy questions, and the goal being accurate prediction or understanding. This presentation emphasizes the need for a multi-paradigm data science to tackle all the challenges we are facing today and may be facing in the future. Luckily, solutions are starting to emerge...
Need an detailed analysis of what this code-model is doing- Thanks #St.pdfactexerode
Need an detailed analysis of what this code/model is doing. Thanks
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
# Generate a batch of random noise
noise = np.random.normal(0, 1, (16, latent_dimensions))
# Generate images from the noise
generated_images = generator.predict(noise)
# Rescale the images to 0.
Feature Engineering - Getting most out of data for predictive modelsGabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Feature Engineering - Getting most out of data for predictive models - TDC 2017Gabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Machine Learning : why we should know and how it worksKevin Lee
The most popular buzz word nowadays in the technology world is “Machine Learning (ML).” Most economists and business experts foresee Machine Learning changing every aspect of our lives in the next 10 years through automating and optimizing processes such as: self-driving vehicles; online recommendation on Netflix and Amazon; fraud detection in banks; image and video recognition; natural language processing; question answering machines (e.g., IBM Watson); and many more. This is leading many organizations to seek experts who can implement Machine Learning into their businesses.
Statistical programmers and statisticians in the pharmaceutical industry are in very interesting positions. We have very similar backgrounds as Machine Learning experts, such as programming, statistics, and data expertise, thus embodying the essential technical skill sets needed. This similarity leads many individuals to ask us about Machine Learning. If you are the leaders of biometric groups, you get asked more often.
The paper is intended for statistical programmers and statisticians who are interested in learning and applying Machine Learning to lead innovation in the pharmaceutical industry. The paper will start with the introduction of basic concepts of Machine Learning - hypothesis and cost function and gradient descent. Then, paper will introduce Supervised ML (e.g., Support Vector Machine, Decision Trees, Logistic Regression), Unsupervised ML (e.g., clustering) and the most powerful ML algorithm, Artificial Neural Network (ANN). The paper will also introduce some of popular SAS ® ML procedures and SAS Visual Data Mining and Machine Learning. Finally, the paper will discuss the current ML implementation, its future implementation and how programmers and statisticians could lead this exciting and disruptive technology in pharmaceutical industry.
Jupyter Notebooks for machine learning on Kubernetes & OpenShift | DevNation ...Red Hat Developers
In this session you will see how to take your machine model from development to production by watching the steps involved, which include: 1) Developing a ML model crafted via a Jupyter Notebook directly on top of Kubernetes/OpenShift; 2) Publishing that model as a service to be shared with your team or even the world; and 3) Monitoring the RESTful service via Grafana.
Similar to Introduction to deep learning using python (20)
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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.
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
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
2. About Wiivv
Wiivv is a technology
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8. The Power of Deep Neural Networks
Amount of Data
Performance
Traditional ML
Small NN
Medium NN
Large NN
9. Why is Deep Learning in Vogue?
● Hardware
○ GPUs
○ NVIDIA leading the way
● Tons of Data
○ ImageNet dataset: 1.4 million annotated images
● Better Algorithms
● Democratic
○ If you know Python, you can do deep learning
○ Many tutorials, pre-trained models