Towards AI

SEPTEMBER 10, 2023

What I’ve learned from the most popular DL course Photo by Sincerely Media on Unsplash I’ve recently finished the Practical Deep Learning Course from Fast.AI. I’ve passed many ML courses before, so that I can compare. So you definitely can trust his expertise in Machine Learning and Deep Learning.

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Deep Learning Deep Learning ML ML 124

AWS Machine Learning Blog

NOVEMBER 19, 2024

This year, generative AI and machine learning (ML) will again be in focus, with exciting keynote announcements and a variety of sessions showcasing insights from AWS experts, customer stories, and hands-on experiences with AWS services. Visit the session catalog to learn about all our generative AI and ML sessions.

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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. Below, we highlight a panoply of works that demonstrate Google Research’s efforts in developing new algorithms to address the above challenges.

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Dataconomy

MAY 22, 2023

By dividing the workload and data across multiple nodes, distributed learning enables parallel processing, leading to faster and more efficient training of machine learning models. There are various types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.

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ML ML Machine Learning Machine Learning 233

Data Science Dojo

FEBRUARY 13, 2025

From an enterprise perspective, this conference will help you learn to optimize business processes, integrate AI into your products, or understand how ML is reshaping industries. It offers you: AI in APIs & Development Learn how AI-powered APIs are revolutionizing software development, automation, and user experiences.

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AI AI Big Data Big Data 217

Data Science Dojo

SEPTEMBER 8, 2023

We’ll dive into the core concepts of AI, with a special focus on Machine Learning and Deep Learning, highlighting their essential distinctions. However, with the introduction of Deep Learning in 2018, predictive analytics in engineering underwent a transformative revolution. Streamline operations.

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The MLOps Blog

NOVEMBER 14, 2023

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. This is where visualizations in ML come in.

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Data Science Dojo

JUNE 14, 2023

Learn how the synergy of AI and ML algorithms in paraphrasing tools is redefining communication through intelligent algorithms that enhance language expression. The most revolutionary technology that enables this is called machine learning. So, when you say AI, it automatically includes machine learning as well.

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Data Science Dojo

JUNE 14, 2023

Learn how the synergy of AI and ML algorithms in paraphrasing tools is redefining communication through intelligent algorithms that enhance language expression. The most revolutionary technology that enables this is called machine learning. So, when you say AI, it automatically includes machine learning as well.

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Machine Learning Machine Learning Algorithm AI 195

Data Science Dojo

OCTOBER 31, 2024

Research Data Scientist Description : Research Data Scientists are responsible for creating and testing experimental models and algorithms. Key Skills: Mastery in machine learning frameworks like PyTorch or TensorFlow is essential, along with a solid foundation in unsupervised learning methods.

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Towards AI

JANUARY 29, 2025

Generative AI is powered by advanced machine learning techniques, particularly deep learning and neural networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Programming Languages: Python (most widely used in AI/ML) R, Java, or C++ (optional but useful) 2.

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Deep Learning Deep Learning Machine Learning Machine Learning 98

Dataconomy

MARCH 27, 2023

However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. In this article, we will explore the similarities and differences between RPA and ML and examine their potential use cases in various industries. What is machine learning (ML)?

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Flipboard

NOVEMBER 6, 2024

We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. when cases with a small amount of free air (total volume <10 ml) are excluded. Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity.

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Towards AI

NOVEMBER 16, 2024

Keswani’s Algorithm introduces a novel approach to solving two-player non-convex min-max optimization problems, particularly in differentiable sequential games where the sequence of player actions is crucial. Keswani’s Algorithm: The algorithm essentially makes response function : maxy∈{R^m} f (.,

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Algorithm ML ML AI 105

Towards AI

AUGUST 21, 2023

Machine Learning vs. AI vs. Deep Learning vs. Neural Networks: What’s the Difference? The rapid evolution of technology is molding our everyday existence as businesses turn more and more to sophisticated algorithms for efficiency. Machine Learning (ML): Next, machine learning takes the spotlight.

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Towards AI

JANUARY 31, 2025

Explaining a black box Deep learning model is an essential but difficult task for engineers in an AI project. Explainability leverages user interfaces, charts, business intelligence tools, some explanation metrics, and other methodologies to discover how the algorithms reach their conclusions. This member-only story is on us.

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Deep Learning Deep Learning Machine Learning Machine Learning 95

Towards AI

JANUARY 9, 2024

In other words, we all want to get directly into Deep Learning. But this is really a mistake if you want to take studying Machine Learning seriously and get a job in AI. Machine Learning fundamentals are not 100% the same as Deep Learning fundamentals and are perhaps even more important.

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ML ML Machine Learning Machine Learning 119

Data Science Dojo

SEPTEMBER 25, 2024

By leveraging AI-powered algorithms, media producers can improve production processes and enhance creativity. Some key benefits of integrating the production process with AI are as follows: Personalization AI algorithms can analyze user data to offer personalized recommendations for movies, TV shows, and music.

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Dataconomy

FEBRUARY 6, 2024

With the ability to analyze a vast amount of data in real-time, identify patterns, and detect anomalies, AI/ML-powered tools are enhancing the operational efficiency of businesses in the IT sector. Why does AI/ML deserve to be the future of the modern world? Let’s understand the crucial role of AI/ML in the tech industry.

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ML ML Machine Learning Machine Learning 121

ML @ CMU

JANUARY 2, 2025

In practice, our algorithm is off-policy and incorporates mechanisms such as two critic networks and target networks as in TD3 ( fujimoto et al., 2018 ) to enhance training (see Materials and Methods in Zhang et al.,

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Analytics Vidhya

AUGUST 4, 2020

Object Detection is a computer vision task in which you build ML models to quickly detect various objects in images, and predict a class. The post Playing with YOLO v1 on Google Colab appeared first on Analytics Vidhya.

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PyImageSearch

JANUARY 30, 2023

This last blog of the series will cover the benefits, applications, challenges, and tradeoffs of using deep learning in the education sector. To learn about Computer Vision and Deep Learning for Education, just keep reading. Task Automation AI software can easily handle repetitive, manual tasks (e.g.,

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ODSC - Open Data Science

APRIL 24, 2023

Be sure to check out his talk, “ Fast Option Pricing Using Deep Learning Methods ,” there! ML Based Option Pricing Since neural networks are universal function approximators they can be used to learn the option prices in various models. Editor’s note: Chakri Cherukuri is a speaker for ODSC Europe 2023 this June.

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Deep Learning Deep Learning Data Science ML 90

Flipboard

NOVEMBER 30, 2023

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. Deploy traditional models to SageMaker endpoints In the following examples, we showcase how to use ModelBuilder to deploy traditional ML models.

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ML ML AWS Python 167

Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube. You can find other posts in the series here.)

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Algorithm Clustering ML ML 110

AWS Machine Learning Blog

FEBRUARY 12, 2024

The Ranking team at Booking.com plays a pivotal role in ensuring that the search and recommendation algorithms are optimized to deliver the best results for their users. Essential ML capabilities such as hyperparameter tuning and model explainability were lacking on premises.

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ML ML AWS Machine Learning 128

Dataconomy

MAY 16, 2023

A machine learning engineer focuses on implementing and deploying machine learning models into production systems. They possess strong programming and engineering skills to develop scalable and efficient machine learning solutions.

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Data Scientist ML ML Machine Learning 156

Flipboard

MARCH 7, 2023

This post presents a solution that uses a workflow and AWS AI and machine learning (ML) services to provide actionable insights based on those transcripts. We use multiple AWS AI/ML services, such as Contact Lens for Amazon Connect and Amazon SageMaker , and utilize a combined architecture.

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Dataconomy

MAY 2, 2023

They design, develop, and deploy the machine learning algorithms that power everything from self-driving cars to personalized recommendations. What do machine learning engineers do? In the context of a business, machine learning engineers are responsible for creating bots that are utilized for chat purposes or data collection.

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ML ML Machine Learning Machine Learning 110

Towards AI

MAY 3, 2023

They investigate the most suitable algorithms, identify the best weights and hyperparameters, and might even collaborate with fellow data scientists in the community to develop an effective strategy. This is where ML CoPilot enters the scene. But what if LLMs could also engage in a cooperative approach?

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ML ML Machine Learning Machine Learning 98

Pickl AI

FEBRUARY 7, 2025

Summary: Machine Learning and Deep Learning are AI subsets with distinct applications. ML works with structured data, while DL processes complex, unstructured data. ML requires less computing power, whereas DL excels with large datasets. DL demands high computational power, whereas ML can run on standard systems.

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Deep Learning Deep Learning Machine Learning Machine Learning 52

AWS Machine Learning Blog

OCTOBER 24, 2024

Amazon Rekognition people pathing is a machine learning (ML)–based capability of Amazon Rekognition Video that users can use to understand where, when, and how each person is moving in a video. ByteTrack is an algorithm for tracking multiple moving objects in videos, such as people walking through a store.

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IBM Data Science in Practice

NOVEMBER 21, 2022

Leverage the Watson NLP library to build the best classification models by combining the power of classic ML, Deep Learning, and Transformed based models. In this blog, you will walk through the steps of building several ML and Deep learning-based models using the Watson NLP library. sample(frac=0.8,

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Deep Learning Deep Learning Exploratory Data Analysis ML 130

Heartbeat

FEBRUARY 6, 2023

How do Object Detection Algorithms Work? There are two main categories of object detection algorithms. Two-Stage Algorithms: Two-stage object detection algorithms consist of two different stages. Single-stage object detection algorithms do the whole process through a single neural network model.

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Algorithm Deep Learning Deep Learning ML 90

Data Science Dojo

JUNE 1, 2023

Machine Learning with Python by Andrew Ng This is an intermediate-level course that teaches you more advanced machine-learning concepts using Python. The course covers topics such as deep learning and reinforcement learning. Gain expertise in data analysis, deep learning, neural networks, and more.

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Machine Learning Machine Learning Natural Language Processing Deep Learning 195

Smart Data Collective

NOVEMBER 1, 2020

With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries. What Is Unsupervised Machine Learning? If you’ve ever come across deep learning, you might have heard about two methods to teach machines: supervised and unsupervised. Unsupervised ML: The Basics.

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Towards AI

JANUARY 11, 2024

It is the fundamental optimization algorithm used for training models. The primary application of gradient descent is in training machine learning models. Jump, Leap, or Baby Steps — Learning Rate α The size of the step for the update of parameter values is, perhaps, the most crucial factor in Gradient Descent.

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