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Tag: deeplearning

Best of arXiv.org for AI, Machine Learning, and Deep Learning – September 2021

In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month. Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. arXiv…… Read more...

The insideBIGDATA IMPACT 50 List for Q4 2021

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most…… Read more...

🧙🏻‍♂️ Edge#130: The ML Engineering Magic Behind OpenAI Codex

What’s New in AI, a deep dive into one of the freshest research papers or technology frameworks that is worth your attention. Our goal is to keep you up to date with new developments in AI to complement the concepts we debate in other editions of our newsletter.

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💥 What’s New in AI: The ML Engineering Magic Behind OpenAI Codex

OpenAI Codex is one of the most impressive deep learning models ever created. Released a few months ago, Codex can generate code based on natural language sentences. The model is proficient in more than a dozen programming languages and can produce code for fairly complex instructions.… Read more...

insideBIGDATA Latest News – 10/4/2021

In this regular column, we’ll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we’re in close touch with vendors from this vast ecosystem, so we’re in a unique position to inform you about all that’s new and exciting. Our massive industry database is growing all the time so…

Continue reading: https://insidebigdata.com/2021/10/05/insidebigdata-latest-news-10-4-2021/

Source: insidebigdata.com
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Deep learning model to predict mRNA degradation

We will be using TensorFlow as our main library to build and train our model and JSON/Pandas to ingest the data. For visualization, we are going to use Plotly and for data manipulation Numpy.# Dataframeimport jsonimport pandas as pdimport numpy as np# Visualizationimport plotly.express as px# Deeplearningimport tensorflow.keras.layers as Limport tensorflow as tf# Sklearnfrom sklearn.model_selection import train_test_split#Setting seedstf.random.set_seed(2021)np.random.seed(2021)Target…

Continue reading: https://pub.towardsai.net/deep-learning-model-to-predict-mrna-degradation-1533a7f32ad4?source=rss—-98111c9905da—4

Source: pub.towardsai.net
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Understanding the Deep Learning Landscape

 
Extending our previous theme Companies who had AI and Digital at their core have fared far bette…, in this post, we consider the approaches to AI from individual companies
Sometimes you see a picture and it says something which you always suspected but were not quite able to fully articulate
The image above is one example (source analytics India mag – link below)
In a nutshell, what it says is .. companies in the AI space are choosing their favourite deep learning technique and…

Continue reading: http://www.datasciencecentral.com/xn/detail/6448529:BlogPost:1070919

Source: www.datasciencecentral.com
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A journey towards faster Reinforcement Learning

From Icarus burning his wings to the Wright brothers soaring through the sky, it took mankind thousands of years to learn how to fly, but how long will it take an AI to do the same?

In this article, we will be reviewing a practical aspect of Reinforcement Learning (RL): how to make it faster! My journey into Reinforcement Learning has been a wonderful experience, going from theoretical knowledge to applied experiments. However, one thing that really grinds my gears is having to wait for the agent to finish training before trying up another idea to improve my project. So, one day I decided to find ways to make the whole process faster.… Read more...

5 Things I’ve Learned as an Open Source Machine Learning Framework Creator

If you’re an aspiring creator or maintainer of open source machine learning frameworks, you might find these tips helpful.Photo taken by author (Bryce Canyon UT on my 2021 Road Trip)

Creating a successful open source project is difficult especially in the data science/machine learning/deep learning space. A large number of open source projects never get used and are quickly abandoned. As the creator of Flow Forecast, an open source deep learning for time series forecasting framework, I’ve had my fair share of both successes and pitfalls. Here is a compilation of the tips I have for aspiring creators/maintainers of open source machine learning frameworks.… Read more...

Better Quantifying the Performance of Object Detection in Video

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Deep neural networks: How to define?

The success of artificial intelligence (AI) nowadays is basically due to deep learning (DL) and its related models. DL is a subfield of machine learning (ML) where a set of algorithms try to model high-level data abstractions, making use of several processing layers, where each type of layer has specific purposes.

However, deep neural networks (DNNs), such as deep convolutional neural networks (CNNs), are based on multilayer perceptron (MLP), a class of feed-forward artificial neural network that has been used for quite some time, even before the advent of the first CNN in 1989. Hence, it comes the question: when a model/network is considered “deep” and not “shallow”?… Read more...

Teaching AI to Classify Time-series Patterns with Synthetic Data – KDnuggets

What do we want to achieve?

 
 
We want to train an AI agent or model that can do something like this,

Image source: Prepared by the author using this Pixabay image (Free to use)

Variances, anomalies, shifts

 
 
Little more specifically, we want to train an AI agent (or model) to identify/classify time-series data for,

low/medium/high variance
anomaly frequencies (little or high fraction of anomalies)
anomaly scales (are the anomalies too far from the normal or close)
a positive or negative shift in the time-series data (in the presence of some anomalies)

But, we don’t want to complicate things

 
 
However, we don’t want to do a ton of feature engineering or learn complicated time-series algorithms (e.g.… Read more...

Surpassing Trillion Parameters and GPT-3 with Switch Transformers – a path to AGI? – KDnuggets

Switch Transformers Have Unlocked Success in Machine Learning

It is practically a trope in certain types of science fiction for an advanced computer system to suddenly “awaken” and become self-aware, often accompanied by vastly improved capabilities when passing an unseen threshold in computing capacity.

Many prominent members of the AI community believe that this common element of AI in sci-fi is as much a literal prophecy as a plot device, and few are more outspoken about the promise of scale as a primary (if not the sole) driver of artificial general intelligence than Ilya Sutskever and Greg Brockman at OpenAI.… Read more...

Fourier Transforms (and More) Using Light

Linear transforms — like a Fourier transform — are a key math tool in engineering and science. A team from UCLA recently published a paper describing how they used deep learning techniques to design an all-optical solution for arbitrary linear transforms. The technique doesn’t use any conventional processing elements and, instead, relies on diffractive surfaces. They also describe a “data free” design approach that does not rely on deep learning.

There is obvious appeal to using light to compute transforms. The computation occurs at the speed of light and in a highly parallel fashion. The final system will have multiple diffractive surfaces to compute the final result.… Read more...

Let’s learn about Dimensionality Reduction

What is Dimensionality?

Dimensionality in statistics refers to “How many attributes a dataset has.”

For example:- We have data in spreadsheet format and we have vast amounts of variables (age, name, sex, Id, and so on..).

In a simple way “The number of input variables or features for a dataset is referred to as its dimensionality.”

Why Dimensional Reduction?

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, Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable.… Read more...

KDnuggets™ News 21:n37, Sep 29: Nine Tools I Wish I Mastered Before My PhD in Machine Learning; Path to Full Stack Data Science

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Whether you have a PhD or not, learn these very useful 9 tools to increase your mastery of Machine Learning; Check this detailed path to becoming a full stack Data Scientist; Then do one of these 20 Machine Learning Projects that will help you get a job; See a Breakdown of Deep Learning Frameworks; and more.

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Nine Tools I Wish I Mastered Before My PhD in Machine Learning

Continue reading: https://www.kdnuggets.com/2021/n37.htmlRead more...

What Is Artificial Intelligence (AI)?

According to the SAS Institute:

“Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.”

Artificial intelligence includes the following elements:

Models of human behaviorModels of human thoughtSystems that behave intelligentlySystems that behave rationallyA set of specific applications that use techniques in machine learning, deep learning and others

In the larger picture of Data Science, artificial intelligence (AI) can encompass (among others):

Other Definitions of Artificial Intelligence Include:

“Strategy to make data analytics tools smarter.”… Read more...

Combining Physics and Deep Learning

What are Digital Twins and how do they work?

Photo by Jørgen Håland on Unsplash
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Diverse Generation from a Single Video Made Possible — No dataset or deep learning required!

Have you ever wanted to edit a video?

Remove or add someone, change the background, make it last a bit longer, or change the resolution to fit a specific aspect ratio without compressing or stretching it. For those of you who already ran advertisement campaigns, you certainly wanted to have variations of your videos for AB testing and see what works best. Well, this new research by Niv Haim et al. can help you do all of these out of a single video and in HD! Indeed, using a simple video, you can perform any tasks I just mentioned in seconds or a few minutes for high-quality videos.… Read more...

Computer Vision in Agriculture – KDnuggets

Deep Learning in the Field: Modern Computer Vision for Agriculture

 
In today’s fast-paced world of city living and stressful work-life imbalances, especially on the (hopefully) tail-end of a year of pandemic quarantine measures, many young workers are yearning to get closer to nature and family. In the face of re-emerging commutes and the push-and-pull of back-to-the-office versus hybrid or fully-remote working, many young robots would rather ditch the status quo and return to the countryside to scratch a living from the land like their ancestors before them. And they’ll bring lasers, too.

Of course, we’re not talking about the weary office drones being herded back to the office after a year of blissfully working at home, but of robots armed with deep learning computer vision systems and precision actuators for a new breed of farming automation.

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Heard on the Street – 9/27/2021

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this new regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace. We invite submissions with a focus on our favored technology topics areas: big data, data science, machine learning, AI and deep learning….

Continue reading: https://insidebigdata.com/2021/09/27/heard-on-the-street-9-27-2021/

Source: insidebigdata.com
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Generate Video Variations – No dataset or deep learning required!

Watch the video and see more examples!Video variations from the original video (top left). Image examples from VGPNN [1].Have you ever wanted to edit a video?Remove or add someone, change the background, make it last a bit longer, or change the resolution to fit a specific aspect ratio without compressing or stretching it. For those of you who already ran advertisement campaigns, you certainly wanted to have variations of your videos for AB testing and see what works best. Well, this new…… Read more...

A Breakdown of Deep Learning Frameworks – KDnuggets

What is a Deep Learning Framework?

A deep learning framework is a software package used by researchers and data scientists to design and train deep learning models. The idea with these frameworks is to allow people to train their models without digging into the algorithms underlying deep learning, neural networks, and machine learning.

These frameworks offer building blocks for designing, training, and validating models through a high-level programming interface. Widely used deep learning frameworks such as PyTorch, TensorFlow, MXNet, and others can also use GPU-accelerated libraries such as cuDNN and NCCL to deliver high-performance multi-GPU accelerated training.

Why Use a Deep Learning Framework?

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