Towards AI

AUGUST 5, 2024

comparison method, cost approach or expert evaluation), machine learning and deep learning models offer new alternatives. In this article, I will give you a simple 10-minute introduction to the most important deep learning models that are frequently used in recent research (see reference) to predict the prices of used cars.

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Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms.

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Marktechpost

NOVEMBER 30, 2023

Researchers from Lund University and Halmstad University conducted a review on explainable AI in poverty estimation through satellite imagery and deep machine learning. The review underscores the significance of explainability for wider dissemination and acceptance within the development community.

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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

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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Pickl AI

OCTOBER 8, 2024

Summary: This blog discusses Explainable Artificial Intelligence (XAI) and its critical role in fostering trust in AI systems. One of the most effective ways to build this trust is through Explainable Artificial Intelligence (XAI). What is Explainable AI (XAI)? What is Explainable AI (XAI)?

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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. Overfitting: When a machine learning model excels on the training data but fails to generalize to new data, overfitting has taken place. What is Deep Learning?

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Heartbeat

NOVEMBER 14, 2023

For many years, gradient-boosting models and deep-learning solutions have won the lion's share of Kaggle competitions. XGBoost is not limited to machine learning tasks, as its incredible power can be harnessed when harmonized with deep learning algorithms. " Nuclear Engineering and Technology 53, no.

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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. This series is about CV and DL for Industrial and Big Business Applications.

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Heartbeat

NOVEMBER 29, 2023

Two names stand out prominently in the wide realm of deep learning: TensorFlow and PyTorch. These strong frameworks have changed the field, allowing researchers and practitioners to create and deploy cutting-edge machine learning models. TensorFlow and PyTorch present distinct routes to traverse.

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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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Viso.ai

MARCH 26, 2024

The introduction of the Transformer model was a significant leap forward for the concept of attention in deep learning. Furthermore, attention mechanisms work to enhance the explainability or interpretability of AI models. Vaswani et al. without conventional neural networks.

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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

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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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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IBM Journey to AI blog

AUGUST 9, 2024

Most generative AI models start with a foundation model , a type of deep learning model that “learns” to generate statistically probable outputs when prompted. Conversely, predictive AI estimates are more explainable because they’re grounded on numbers and statistics. appeared first on IBM Blog.

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Viso.ai

FEBRUARY 12, 2024

True to its name, Explainable AI refers to the tools and methods that explain AI systems and how they arrive at a certain output. Artificial Intelligence (AI) models assist across various domains, from regression-based forecasting models to complex object detection algorithms in deep learning.

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Pickl AI

JANUARY 15, 2023

With the speedy evolution of technologies, Machine Learning, Artificial Intelligence and Deep learning meaning might baffle you. This blog would act as a guide for you to understand the concept- What is Deep Learning?- What is Deep Learning in AI? How Deep Learning works?

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Heartbeat

SEPTEMBER 13, 2023

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?

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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

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

MARCH 14, 2023

We describe this feature and evaluate its use by pathologists in our recently published paper, “ Pathologist validation of a machine-learned feature for colon cancer risk stratification ”. Putting things in context: learning from deep learning as a paradigm Our work is an example of people “learning from deep learning”.

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Towards AI

MAY 13, 2024

So, in this blog post, let’s take a look at what exactly an AI supercomputer is and how it trains large AI models such as GPT3, GPT4, and even the latest GPT-4o, that power ChatGPT and BingChat. Supercomputers are the most powerful and… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter.

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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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John Snow Labs

APRIL 30, 2024

The ImageTableDetector is a deep-learning model that identifies tables within images. Let me explain what is happening in the pipeline. Summary In this blog post, we’ve embarked on an in-depth exploration of constructing a pipeline for table extraction utilizing John Snow Labs NLP with visual features installed.

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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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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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PyImageSearch

JANUARY 2, 2023

This blog will cover the benefits, applications, challenges, and tradeoffs of using deep learning in healthcare. Computer Vision and Deep Learning for Healthcare Benefits Unlocking Data for Health Research The volume of healthcare-related data is increasing at an exponential rate.

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Heartbeat

SEPTEMBER 7, 2023

In the ever-evolving landscape of machine learning and artificial intelligence, understanding and explaining the decisions made by models have become paramount. Enter Comet , that streamlines the model development process and strongly emphasizes model interpretability and explainability. Why Does It Matter?

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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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Towards AI

SEPTEMBER 7, 2023

An analogy to explain how deep learning works… This member-only story is on us. Originally published on louisbouchard.ai, read it 2 days before on my blog! link] When we talk about artificial intelligence, or AI, we tend to mean deep learning. Upgrade to access all of Medium.

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Mlearning.ai

AUGUST 4, 2023

In this blog, we will try to deep dive into the concept of 1x1 convolution operation which appeared in the paper ‘Network in Network’ by Lin et al in (2013) and ‘Going Deeper with Convolutions’ by Szegedy et al (2014) that proposed the GoogLeNet architecture. References: [link] [link] [link] WRITER at MLearning.ai // Control AI Video

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AI Weekly

APRIL 11, 2024

Read the blog] global.ntt In The News When an antibiotic fails: MIT scientists are using AI to target “sleeper” bacteria mit.edu Microsoft AI opens London hub to access ‘enormous pool’ of talent Microsoft is doubling down on its AI efforts in the UK with the opening of a major new AI hub in London. No legacy process is safe.

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Heartbeat

MARCH 28, 2023

A complete guide to building a deep learning project with PyTorch, tracking an Experiment with Comet ML, and deploying an app with Gradio on HuggingFace Image by Freepik AI tools such as ChatGPT, DALL-E, and Midjourney are increasingly becoming a part of our daily lives. These tools were developed with deep learning techniques.

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Analytics Vidhya

SEPTEMBER 27, 2022

Introduction My last blog discussed the “Training of a convolutional neural network from scratch using the custom dataset.” ” In that blog, I have explained: how to create a dataset directory, train, test and validation dataset splitting, and training from scratch. This blog is […].

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Heartbeat

JANUARY 31, 2023

In deep learning, activation functions add non-linearity to the neural networks. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

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