Heartbeat

FEBRUARY 21, 2024

Photo by Almos Bechtold on Unsplash Deep learning is a machine learning sub-branch that can automatically learn and understand complex tasks using artificial neural networks. Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns.

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Heartbeat

OCTOBER 11, 2023

This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. What is Deep Learning? This is why the technique is known as "deep" learning.

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Heartbeat

AUGUST 29, 2023

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. It is a tremendous tool with the ability to completely alter numerous sectors.

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Heartbeat

OCTOBER 18, 2023

In this article, we embark on a journey to explore the transformative potential of deep learning in revolutionizing recommender systems. However, deep learning has opened new horizons, allowing recommendation engines to unravel intricate patterns, uncover latent preferences, and provide accurate suggestions at scale.

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Heartbeat

AUGUST 1, 2023

A World of Computer Vision Outside of Deep Learning Photo by Museums Victoria on Unsplash IBM defines computer vision as “a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs [1].”

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Heartbeat

MAY 2, 2023

Artificial intelligence has undergone a revolution thanks to deep learning. Deep learning allows machines to learn from vast amounts of data and carry out complex tasks that were previously only considered possible by humans (like translation between languages, recognizing objects etc.).

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Heartbeat

NOVEMBER 27, 2023

Deep learning automates and improves medical picture analysis. Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs.

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Heartbeat

JUNE 23, 2023

However, with the advent of deep learning, researchers have explored various neural network architectures to model and forecast time series data. In this post, we will look at deep learning approaches for time series analysis and how they might be used in real-world applications. Let’s dive in!

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Heartbeat

APRIL 23, 2023

Photo by RetroSupply on Unsplash Introduction Deep learning has been widely used in various fields, such as computer vision, NLP, and robotics. The success of deep learning is largely due to its ability to learn complex representations from data using deep neural networks. What is Epistemic Uncertainty?

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ODSC - Open Data Science

OCTOBER 17, 2023

But without a strong understanding of deep learning, you’ll have a difficult time getting the most out of the cutting-edge developments in the industry. At ODSC West this October 30th to November 2nd, you’ll build the core knowledge and skills you need with the sessions in the deep learning track , such as the ones listed below.

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AWS Machine Learning Blog

NOVEMBER 19, 2024

The excitement is building for the fourteenth edition of AWS re:Invent, and as always, Las Vegas is set to host this spectacular event. Third, we’ll explore the robust infrastructure services from AWS powering AI innovation, featuring Amazon SageMaker , AWS Trainium , and AWS Inferentia under AI/ML, as well as Compute topics.

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Heartbeat

FEBRUARY 22, 2023

Deep learning for feature extraction, ensemble models, and more Photo by DeepMind on Unsplash The advent of deep learning has been a game-changer in machine learning, paving the way for the creation of complex models capable of feats previously thought impossible.

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Heartbeat

NOVEMBER 21, 2023

Computer vision, the field dedicated to enabling machines to perceive and understand visual data, has witnessed a monumental shift in recent years with the advent of deep learning. Photo by charlesdeluvio on Unsplash Welcome to a journey through the advancements and applications of deep learning in computer vision.

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Google Research AI blog

FEBRUARY 7, 2023

The explosion in deep learning a decade ago was catapulted in part by the convergence of new algorithms and architectures, a marked increase in data, and access to greater compute. One of the questions in the quest for a modular deep network is how a database of concepts with corresponding computational modules could be designed.

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PyImageSearch

DECEMBER 11, 2023

The sessions were packed with interesting talks on a variety of topics, and the networking events were a great way to meet new people and make connections. Kicking Off with a Keynote The second day of the Google Machine Learning Community Summit began with an inspiring keynote session by Soonson Kwon, the ML Community Lead at Google.

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Heartbeat

MAY 23, 2023

As technology continues to improve exponentially, deep learning has emerged as a critical tool for enabling machines to make decisions and predictions based on large volumes of data. Edge computing may change how we think about deep learning. Standardizing model management can be tricky but there is a solution.

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Google Research AI blog

MAY 23, 2023

However, with machine learning (ML), we have an opportunity to automate and streamline the code review process, e.g., by proposing code changes based on a comment’s text. As of today, code-change authors at Google address a substantial amount of reviewer comments by applying an ML-suggested edit. 3-way-merge UX in IDE.

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Heartbeat

OCTOBER 3, 2023

Conclusion This is the end of the two-part series on memory mapping, where I have given a comprehensive beginner tutorial on the best method for a deep learning dive. These methods are not limited to deep learning as we have acquired an understanding of binaries and how they could be used to store information.

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Heartbeat

SEPTEMBER 27, 2023

Dealing with your memory constraints Photo by Ian Talmacs on Unsplash In the previous article , we discussed the possibility of having memory constraints when trying to get into deep learning. When working with deep learning, arrays might become larger than what could fit in memory.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. And although generative AI has appeared in previous events, this year we’re taking it to the next level. This year, learn about LLMOps, not just MLOps!

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Heartbeat

JANUARY 31, 2023

In deep learning, activation functions add non-linearity to the neural networks. This activation function is widely used in machine learning models where we need to predict the probability of any input value as an output. It is because the probability of any event always exists between 0 and 1. 3.4, -2.1,

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The MLOps Blog

APRIL 5, 2023

In this comprehensive guide, we’ll explore the key concepts, challenges, and best practices for ML model packaging, including the different types of packaging formats, techniques, and frameworks. So, let’s dive in and discover everything you need to know about model packaging in machine learning.

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Heartbeat

MARCH 14, 2024

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionary technologies with the potential to transform the field of engineering. The synergy between engineering and AI/ML creates unprecedented opportunities for efficiency, cost reduction, and innovation. Indium Software Why AI and ML in Engineering?

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PyImageSearch

DECEMBER 4, 2023

Home Table of Contents ML Days in Tashkent — Day 1: City Tour Arriving at Tashkent! This blog is the 1st of a 3-part series: ML Days in Tashkent — Day 1: City Tour (this tutorial) ML Days in Tashkent — Day 2: Sprints and Sessions ML Days in Tashkent — Day 3: Demos and Workshops ML Days in Tashkent — Day 1: City Tour Arriving at Tashkent!

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Heartbeat

JUNE 26, 2023

A guide to performing end-to-end computer vision projects with PyTorch-Lightning, Comet ML and Gradio Image by Freepik Computer vision is the buzzword at the moment. Today, I’ll walk you through how to implement an end-to-end image classification project with Lightning , Comet ML, and Gradio libraries.

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Dataconomy

MAY 2, 2023

The machine learning systems developed by Machine Learning Engineers are crucial components used across various big data jobs in the data processing pipeline. Additionally, Machine Learning Engineers are proficient in implementing AI or ML algorithms. Is ML engineering a stressful job?

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Heartbeat

FEBRUARY 28, 2023

The past few years have witnessed exponential growth in medical image analysis using deep learning. In this article we will look into medical image segmentation and see how deep learning can be helpful in these cases. This can be further classified as supervised and unsupervised learning. Image by author.

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Heartbeat

JULY 25, 2023

Introduction When it comes to practicing deep learning at home vs. industry, there’s a huge disconnect. If so, how much effort does it take to go from that system to a deep learning framework-ready system? It’s easy for ML teams to rack-up their monthly cloud training bills. Learn how Comet can help you do this.

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Heartbeat

SEPTEMBER 18, 2023

Unleashing Innovation and Success: Comet — The Trusted ML Platform for Enterprise Environments Machine learning (ML) is a rapidly developing field, and businesses are increasingly depending on ML platforms to fuel innovation, improve efficiency, and mine data for insights.

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Heartbeat

JUNE 5, 2023

However, managing machine learning projects can be challenging, especially as the size and complexity of the data and models increase. Without proper tracking, optimization, and collaboration tools, ML practitioners can quickly become overwhelmed and lose track of their progress. This is where Comet comes in.

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Heartbeat

MARCH 6, 2023

model %>% compile( optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = c('accuracy') ) Monitoring Before you start training, you’ll need to integrate Comet ML’s R package by adding the following code block. Ensure you have your API key from your Comet ML account, then create a .comet.yml

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The MLOps Blog

AUGUST 11, 2023

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. What are ML pipeline architecture design patterns?

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Heartbeat

JANUARY 24, 2023

I will now get to the technical details of hyperparameter tuning and experiment tracking on comet ml by making parallels with Optuna for simple migration. Requirements There are a few requirements that you’d need before you begin: A Comet ML account An installation of Jupyter Notebook or a Google Colab account Python 3.9.X

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Heartbeat

DECEMBER 21, 2023

Uncovering the Power of Comet Across the Data Science Journey Photo by Nguyen Le Viet Anh on Unsplash Machine learning (ML) projects are usually complicated and include several stages, from data discovery to model implementation. This article will dive into steps to see how you can use Comet at different stages of ML projects.

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Heartbeat

FEBRUARY 15, 2023

exp <- create_experiment( keep_active = TRUE, log_output = TRUE, log_error = FALSE, log_code = TRUE, log_system_details = TRUE, log_git_info = TRUE ) Next, you need to specify the number of epochs you want to train the network and log it using Comet ML. plot(history) Make sure you log the training loss and accuracy metrics to Comet ML.

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ML @ CMU

NOVEMBER 7, 2024

Since landmines are not used randomly but under war logic , Machine Learning can potentially help with these surveys by analyzing historical events and their correlation to relevant features. For the Risk Modeling component, we designed a novel interpretable deep learning tabular model extending TabNet.

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AWS Machine Learning Blog

JUNE 22, 2023

Over 500 machine events are monitored in near-real time to give a full picture of machine conditions and their operating environments. Light & Wonder teamed up with the Amazon ML Solutions Lab to use events data streamed from LnW Connect to enable machine learning (ML)-powered predictive maintenance for slot machines.

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