Scientist develops an image recognition algorithm that works 40% faster than analogs
Convolutional neural networks (CNNs), which include a sequence of convolutional
layers, are widely used in computer vision. Each layer in a network has an input
and an output. The digital description of the image goes to the input of the
first layer and is converted into a different set of numbers at the output. The
result goes to the input of the next layer and so on until the class label of
the object in the image is predicted in the last layer. For example, this class
can be a person, a cat, or a chair. For this, a CNN is trained on a set of
images with a known class label. The greater the number and variability of the
images of each class in the dataset are, the more accurate the trained network
will be. ... The study's author, Professor Andrey Savchenko of the HSE Campus in
Nizhny Novgorod, was able to speed up the work of a pre-trained convolutional
neural network with arbitrary architecture, consisting of 90-780 layers in his
experiments. The result was an increase in recognition speed of up to 40%, while
controlling the loss in accuracy to no more than 0.5-1%. The scientist relied on
statistical methods such as sequential analysis and multiple comparisons.
Comprehensive Guide To Dimensionality Reduction For Data Scientists
The approaches for Dimensionality Reduction can be roughly classified into two
categories. The first one is to discard less-variance features. The second one
is to transform all the features into a few high-variance features. We will have
a few of the original features in the former approach that do not undergo any
alterations. But in the later approach, we will not have any of the original
features, rather, we will have a few mathematically transformed features. The
former approach is straightforward. It measures the variance in each feature. It
claims that a feature with minimal variance may not have any pattern in it.
Therefore, it discards the features in the order of their variance from the
lowest to the highest. Backward Feature Elimination, Forward Feature
Construction, Low Variance Filter and Lasso Regression are the popular
techniques that fall under this category. The later approach claims that even a
less-important feature may have a small piece of valuable information. It does
not agree with discarding features based on variance analysis.
How security reskilling and automation can mend the cybersecurity skills gap
To understand the high demand for cybersecurity skills, consider how much has
changed in IT—especially in the last year. From a rapid increase in cloud
migrations to a huge shift toward remote work, IT teams everywhere have been
forced to adapt quickly to keep up with the changing needs of their
organizations. However, the rapid expansion of technology and explosion of
remote work has kept IT busy enough. They don’t have the capacity to adequately
handle responsibilities ranging from regular security hygiene to the patching
and forensics surrounding the latest zero-day threat. ... With the difficulty of
recruiting, hiring, and onboarding new cybersecurity experts from a small talent
pool, consider investing in retraining your workforce to organically grow needed
cybersecurity skills. Besides avoiding a lengthy headhunting process, this also
makes clear economic sense. According to the Harvard Business Review, it can
cost six times as much to hire from the outside rather than build talent from
within. In addition, focusing on retraining opens up career progression for your
best employees—building their skills, morale, and loyalty to your
organization.
The Flow System: Leadership for Solving Complex Problems
One of the most significant limitations in today’s leadership practices is the
lack of development. Most leadership training is disguised as leader education.
These training efforts also do not include time for emerging leaders to practice
their newly learned leadership skills. Without practice and the freedom to fail
during the developmental stages, it is nearly impossible for emerging leadership
to master skill. Another problem with leadership development is that most
programs deliver training to everyone the same way. Most leadership development
programs were initially designed as “one-size-fits-all” training. In The Flow
System, we make great efforts to design leadership and team development around
the contextual setting. We view leadership as a collective construct, not an
individual construct. We incorporate the team as the model of leadership, and
individual team members as leaders using a shared leadership model. This
collective becomes the organization’s leadership model, from the lower ranks up
to the executive level.
5 Practices To Give Great Code Review Feedback
The first thing to do is to have a very clear context about the PR. Sometimes we
want to go fast; we think we already know what our colleague wanted to do, the
best way to do it, and we just skim through the description. However, it is much
better to take some time and read the title and description of the PR carefully,
especially the latter because we could find all the assumptions that guided our
colleague. We could find a more detailed description of the task and perhaps a
good description of the main issue they faced when developing it. This could
give us all the information we need to perform a constructive review, taking
into consideration all the relevant aspects of it. ... When reviewing a piece of
code, focus on the most important parts: the logic, the choices of data
structure and algorithms, whether all the edge cases have been covered in the
tests, etc. Many of the other syntax/formatting elements should be taken care of
by a tool, such as a linter, a formatter, a spell checker. etc. There is no
point in highlighting them in a comment. The same idea holds on how the
documentation is written. There should be some conventions, and it is OK to tell
the contributor if they are not following them.
Machine learning does not magically solve your problems
Looking at the neural network approach we see that some of the manual tasks are
absorbed into the neural network. Specifically, feature engineering and
selection are done internally by the neural network. On the flipside, we have to
determine the network architecture (number of layers, interconnectedness, loss
function, etc) and tune the hyperparamers of the network. In addition, many
other tasks such as assessing the business problem still need to be done. As
with TSfresh/Lasso, the neural network is an approach that works well in a
specific situation, and is not a quick nor automated procedure. A good way to
frame to change from regression to the neural network is that instead of solving
the problem manually, we build a machine that solves the problem for us. Adding
this layer of abstraction allows us to solve problems we never thought we could
solve, but that still takes a lot of time and money to create. ... Machine
learning has some magical and awe-inspiring applications, extending the range of
applications we thought possible to be solved using a computer. However, the
awesome potential of machine learning does not mean that it automatically solves
our challenges.
Five Tips For Creating Design Systems
A product experience that delights is usually designed with persistent visuals
and consistent interaction. Users want to feel comfortable knowing that no
matter where they navigate, they won’t be surprised by what they find.
Repetition, in the case of product design, is not boring, but welcome. Design
systems create trust with users. Another benefit is the increased build velocity
from design and engineering teams. As designers, we are tasked with solving
problems. We want to create a simple understanding of how our users can
accomplish tasks in a workflow. Of course, we are tempted at times to invent new
patterns to solve design problems. We often forget, in the minutia of design
iterations, that we’ve already solved a particular problem in a prior project or
in another part of the current product. This inefficiency can lead to wasted
time, especially if those existing patterns and components have not been
documented. In a single-person design team, the negative effects may not be as
visible, but one can imagine the exponential nature of a larger design team
consistently duplicating existing work or creating new patterns that,
ultimately, create an inconsistent user experience.
A Gentle Introduction to Multiple-Model Machine Learning
Typically, a single output value is predicted. Nevertheless, there are
regression problems where multiple numeric values must be predicted for each
input example. These problems are referred to as multiple-output regression
problems. Models can be developed to predict all target values at once, although
a multi-output regression problem is another example of a problem that can be
naturally divided into subproblems. Like binary classification in the previous
section, most techniques for regression predictive modeling were designed to
predict a single value. Predicting multiple values can pose a problem and
requires the modification of the technique. Some techniques cannot be reasonably
modified for multiple values. One approach is to develop a separate regression
model to predict each target value in a multi-output regression problem.
Typically, the same algorithm type is used for each model. For example, a
multi-output regression with three target values would involve fitting three
models, one for each target.
Why is Business Intelligence (BI) important?
The term “data-driven decision-making” doesn’t fully encapsulate one of its
important subtexts: People almost always mean fast decisions. This distinction
matters because it’s one of the capabilities that modern BI tools and practices
enable: Decision-making that keeps pace (or close enough to it) with the speed
at which data is produced. “Data is now produced so fast and in such large
volumes that it is impossible to analyze and use effectively when using
traditional, manual methods such as spreadsheets, which are prone to human
error,” says Darren Turner, head of BI at Air IT. “The advantage of BI is that
it automatically analyzes data from various sources, all accurately presented in
one easy-to-digest dashboard.” Sure, everyone talks about the importance of
speed and agility across technology and business contexts. But that’s kind of
the point: If you’re not doing it, your competitors almost certainly are. ...
“In a marketplace where the volume of data is ever-increasing, the ability for
it to be processed and translated into sound business decisions is essential for
better understanding customer behavior and outperforming competitors.”
What Is NFT (Non Fungible Tokens)? What Does NFT Stand For?
The bulk of NFTs are stored on the Ethereum network.. Certain NFTs, which store
additional information that allows them to function differently are also
supported by the blockchain. Ethereum, like bitcoin and dogecoin, is a
cryptocurrency, but the blockchain frequently accepts such non-fungible tokens
(NFTs), which store additional information that enables them to function
differently Person tokens that are part of the Ethereum network that have extra
information are known as NFTs. The extra content is the most important feature,
as it allows them to be displayed as art, music, video (and so on) in JPGs,
MP3s, photographs, GIFs, and other formats. They can be bought and sold like any
other medium of art because they have value – and their value is largely
dictated by supply and demand, much like physical art. But that doesn’t suggest,
in any way, that there is just one digital version of NFT art available to
purchase. One can obviously replicate them, much like the art prints of
originals are used, bought and sold, but they won’t be of the same value as the
original one.
Quote for the day:
"It is not fair to ask of others what
you are not willing to do yourself." -- Eleanor Roosevelt
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