Analytics Vidhya

JUNE 18, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon This article explains the problem of exploding and vanishing gradients while. The post The Challenge of Vanishing/Exploding Gradients in Deep Neural Networks appeared first on Analytics Vidhya.

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AI News

JULY 24, 2023

AI News spoke with Damian Bogunowicz, a machine learning engineer at Neural Magic , to shed light on the company’s innovative approach to deep learning model optimisation and inference on CPUs. One of the key challenges in developing and deploying deep learning models lies in their size and computational requirements.

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Analytics Vidhya

NOVEMBER 6, 2020

Today I am going to try my best in explaining. The post A Short Intuitive Explanation of Convolutional Recurrent Neural Networks appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. Introduction Hello!

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Analytics Vidhya

MAY 5, 2019

Introduction “How did your neural network produce this result?” It’s easy to explain how. The post A Guide to Understanding Convolutional Neural Networks (CNNs) using Visualization appeared first on Analytics Vidhya. ” This question has sent many data scientists into a tizzy.

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Towards AI

JANUARY 27, 2023

Deep Learning Explained: Perceptron The key concept behind every neural network. Source: Image by Gerd Altmann from Pixabay Nowadays, frameworks such as Keras, TensorFlow, or PyTorch provide turnkey access to most deep learning solutions without necessarily having to understand them in depth.

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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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Marktechpost

JANUARY 19, 2024

Neural network-based methods in estimating biological age have shown high accuracy but lack interpretability, prompting the development of a biologically informed tool for interpretable predictions in prostate cancer and treatment resistance. The most noteworthy result was probably obtained for the pan-tissue dataset.

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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

OCTOBER 8, 2023

By utilizing finely developed neural network architectures, we have models that are distinguished by extraordinary accuracy within their respective sectors. Despite their accurate performance, we must still fully understand how these neural networks function. We have new advancements that have been there with each day.

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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. Neural Network is a combination of linear functions and activations.

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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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Marktechpost

SEPTEMBER 30, 2023

This redundancy consumes extra energy and leads to an expanded chip footprint, raising concerns about space efficiency and scalability in large-scale optical neural networks (ONNs) and optimization problem solvers. Efforts to address this issue have been made, with solutions such as a pseudo-real-value MZI mesh.

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Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

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Heartbeat

NOVEMBER 14, 2023

Integrating XGboost with Convolutional Neural Networks Photo by Alexander Grey on Unsplash XGBoost is a powerful library that performs gradient boosting. It has an excellent reputation as a tool for predicting many kinds of problems in data science and machine learning. It was envisioned by Thongsuwan et al.,

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Marktechpost

JANUARY 28, 2024

A researcher from the University of Zurich has turned to deep learning as a potent tool. Deep learning models, such as multilayer perceptrons, recurrent neural networks, and transformers, have been employed to forecast the fitness of genotypes based on experimental data.

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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

AUGUST 21, 2023

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs are artificial neural networks built to handle data having a grid-like architecture, such as photos or movies.

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Marktechpost

DECEMBER 15, 2023

This method involves the application of a generative neural network, specifically a Generative Adversarial Network (GAN), a form of AI. This GAN is trained using high-resolution radar precipitation data, allowing it to learn and mimic realistic precipitation fields with significantly higher spatial and temporal resolutions.

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Marktechpost

MAY 5, 2024

Machine Learning Specialization “Machine Learning Specialization” teaches the core concepts of machine learning and how to build real-world AI applications using the same. The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow.

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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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Unite.AI

OCTOBER 5, 2023

This is your third AI book, the first two being: “Practical Deep Learning: A Python-Base Introduction,” and “Math for Deep Learning: What You Need to Know to Understand Neural Networks” What was your initial intention when you set out to write this book? Different target audience.

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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

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. Here, TensorFlow and PyTorch reveal their distinctive approaches.

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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

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 8, 2024

introduced deep belief networks (DBNs) in 2006. These deep learning algorithms consist of latent variables and use them to learn underlying patterns within the data. The underlying nodes are linked as a directed acyclic graph (DAG), giving the network generative and discriminative qualities. Get a demo here.

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Marktechpost

SEPTEMBER 26, 2023

Microsoft researchers propose a groundbreaking solution to these challenges in their recent “Neural Graphical Models” paper presented at the 17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023).

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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

JANUARY 31, 2023

Non-linear activation functions that you need to know Photo by Ricardo Gomez Angel on Unsplash Activation functions are equations or mathematical formulas that help us determine the output of a neural network. In deep learning, activation functions add non-linearity to the neural networks.

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Unite.AI

JUNE 25, 2023

However, assimilating the understanding of physics into the realm of neural networks has proved challenging. In a significant breakthrough, the UCLA study intends to combine the deep understanding from data and the real-world know-how of physics, thereby creating a hybrid AI with augmented capabilities.

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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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Marktechpost

MAY 1, 2024

While human intelligence relies on biological neural networks and operates at slower speeds, AI systems leverage digital processors for rapid data processing and seamless communication. Human introspection is limited, making it challenging to explain unconscious thoughts.

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Heartbeat

MARCH 1, 2023

Tracking your image classification experiments with Comet ML Photo from nmedia on Shutterstock.com Introduction Image classification is a task that involves training a neural network to recognize and classify items in images. A dataset of labeled images is used to train the network, with each image given a particular class or label.

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Mlearning.ai

APRIL 24, 2023

One of the most popular and efficient approaches for object detection is the You Only Look Once version 5 (YOLOv5) neural network architecture. However, the YOLOv5 neural network architecture has several hyperparameters that can significantly affect its performance. BECOME a WRITER at MLearning.ai

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Marktechpost

JULY 6, 2023

This is where Interpretable (IAI) and Explainable (XAI) Artificial Intelligence techniques come into play, and the need to understand their differences become more apparent. On the other hand, explainable AI models are very complicated deep learning models that are too complex for humans to understand without the aid of additional methods.

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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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