Towards AI

FEBRUARY 3, 2024

(Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. A few weeks ago, I needed an explainability algorithm for a YoloV8 model. The truth is, I couldn’t find anything.

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Pickl AI

AUGUST 23, 2024

Summary: This blog post delves into the importance of explainability and interpretability in AI, covering definitions, challenges, techniques, tools, applications, best practices, and future trends. For instance, if a model predicts that a loan application should be denied, explainability seeks to clarify the rationale behind this decision.

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Pickl AI

SEPTEMBER 23, 2024

Summary: This guide covers the most important Deep Learning interview questions, including foundational concepts, advanced techniques, and scenario-based inquiries. Gain insights into neural networks, optimisation methods, and troubleshooting tips to excel in Deep Learning interviews and showcase your expertise.

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Marktechpost

NOVEMBER 20, 2024

The Semantic Re-encoding Deep Learning Model (SRDLM) can also be used to improve traffic distinguishability and algorithmic generalization, as presented by the prior researchers. By hybridizing optimization techniques with deep belief networks, the method aims to enhance DDoS attack detection accuracy, speed, and scalability.

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Marktechpost

NOVEMBER 29, 2023

While classical cognitive models explain many psychological features of speech perception, these models fall short in explaining brain coding and natural speech recognition. Deep learning models are getting close to human performance in automated speech recognition.

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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. The reasons for this range from wrongly connected model components to misconfigured optimizers.

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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. So you definitely can trust his expertise in Machine Learning and Deep Learning. Luckily, there’s a handy tool to pick up Deep Learning Architecture.

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Marktechpost

MARCH 31, 2024

Solving partial differential equations (PDEs) is complex, just like the events they explain. Deep learning, using designs like U-Nets, is popular for working with information at multiple levels of detail. Earlier methods of solving these equations struggled with the challenge of changes happening over time.

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Marktechpost

JANUARY 28, 2024

These studies, while informative, face significant limitations due to the high-dimensional nature of genotypes and the intricate, non-linear interactions of genetic components in determining an organism’s fitness. A researcher from the University of Zurich has turned to deep learning as a potent tool.

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Towards AI

NOVEMBER 19, 2023

Photo by Pietro Jeng on Unsplash Deep learning is a type of machine learning that utilizes layered neural networks to help computers learn from large amounts of data in an automated way, much like humans do. We will explain intuitively what each one means and how it contributes to the deep learning process.

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Marktechpost

JULY 22, 2024

With deep learning models like BERT and RoBERTa, the field has seen a paradigm shift. This lack of explainability is a gap in academic interest and a practical concern. Existing methods for AV have advanced significantly with the use of deep learning models.

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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

NOVEMBER 27, 2023

Medical image analysis involves extracting valuable information from various imaging modalities like X-rays, CT scans, MRI, ultrasound, and PET scans. Deep learning automates and improves medical picture analysis. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs.

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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. Semi-Supervised Learning : Training is done using both labeled and unlabeled data.

Software Development 52

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PyImageSearch

SEPTEMBER 23, 2024

Home Table of Contents Deploying a Vision Transformer Deep Learning Model with FastAPI in Python What Is FastAPI? You’ll learn how to structure your project for efficient model serving, implement robust testing strategies with PyTest, and manage dependencies to ensure a smooth deployment process. Testing main.py Testing main.py

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Flipboard

NOVEMBER 11, 2024

In this research study, we present an innovative approach to predict Alzheimer’s disease (AD) and mild cognitive impairment (MCI) using MRI data, which integrates regional interest (ROI)-based methodology and deep learning within a comprehensible framework.

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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. As soon as the system adapts to human wants, it automates the learning process accordingly.

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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. Handling Complex and Large Datasets: Machine learning algorithms can handle vast amounts of data and extract meaningful information.

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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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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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Mlearning.ai

DECEMBER 13, 2023

Mastering Deep Learning and AI Interview Questions: What You Need to Know Image created by the author on Canva Knowledge is power, but enthusiasm pulls the switch.” Ever wondered what it takes to excel in deep learning interviews? Explain the vanishing and exploding gradient problems. said Ivern Ball.

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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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Analytics Vidhya

AUGUST 26, 2021

This article was published as a part of the Data Science Blogathon “You can have data without information but you cannot have information without data” – Daniel Keys Moran Introduction If you are here then you might be already interested in Machine Learning or Deep Learning so I need not explain what it is?

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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

NOVEMBER 21, 2023

In a world where visual data surrounds us, the ability to extract meaningful information from images and videos is more crucial than ever. 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.

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AssemblyAI

DECEMBER 7, 2023

A joint audio-language model trained on suitably expansive datasets of audio and text could learn more universal representations to transfer robustly across both modalities.    This problem is harder for audio because audio data is far more information-dense than text.

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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. In effect, increasing the capacity to learn from the input data. Vaswani et al.

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Marktechpost

NOVEMBER 16, 2024

Deep Learning (Adaptive Computation and Machine Learning series) This book covers a wide range of deep learning topics along with their mathematical and conceptual background. It also provides information on the different deep learning techniques used in various industrial applications.

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Google Research AI blog

MARCH 14, 2023

This information is central to understanding clinical prognosis (i.e., The discovery of new features could in turn further improve cancer prognostication and treatment decisions for patients by extracting information that isn’t yet considered in current workflows.

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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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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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Mlearning.ai

AUGUST 11, 2023

Introduction GPUs as main accelerators for deep learning training tasks suffer from under-utilization. Authors of AntMan [1] propose a deep learning infrastructure, which is a co-design of cluster schedulers (e.g., with deep learning frameworks (e.g., with deep learning frameworks (e.g.,

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Mlearning.ai

JANUARY 3, 2024

word2vec dl4ee: deep learning for electrical engineers Why another article for word2vec? We will use the simplest concept of Linear Algebra, and Stochastic Process to explain it. However, the approximation can further reduce the computation complexity without losing its fidelity in the deep learning models later.

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Unite.AI

APRIL 9, 2024

These techniques include Machine Learning (ML), deep learning , Natural Language Processing (NLP) , Computer Vision (CV) , descriptive statistics, and knowledge graphs. Explainability is essential for accountability, fairness, and user confidence. Explainability also aligns with business ethics and regulatory compliance.

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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

OCTOBER 3, 2023

One way we can do this is by taking the binary with information of an “X_train” array in the previous article. Screenshot by author Now, it is identical to the information we had initially stored. Til we meet again in the next tutorial. We’re committed to supporting and inspiring developers and engineers from all walks of life.

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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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