Analytics Vidhya

MARCH 10, 2022

Apple’s Siri and Google’s voice search both use Recurrent Neural Networks (RNNs), which are the state-of-the-art method for sequential data. It’s the first algorithm with an internal memory that remembers its input, making it perfect for problems involving sequential data in machine learning.

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Marktechpost

MAY 14, 2024

Understanding deep learning equips individuals to harness its potential, driving innovation and solving complex problems across various industries. This article lists the top Deep Learning and Neural Networks books to help individuals gain proficiency in this vital field and contribute to its ongoing advancements and applications.

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Analytics Vidhya

AUGUST 23, 2021

This article was published as a part of the Data Science Blogathon Building a simple Machine Learning model using Pytorch from scratch. Image by my great learning Introduction Gradient descent is an optimization algorithm that is used to train machine learning models and is now used in a neural network.

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Marktechpost

DECEMBER 15, 2023

Complex tasks like text or picture synthesis, segmentation, and classification are being successfully handled with the help of neural networks. However, it can take days or weeks to obtain adequate results from neural network training due to its computing demands. If you like our work, you will love our newsletter.

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Marktechpost

MAY 5, 2024

In recent years, the demand for AI and Machine Learning has surged, making ML expertise increasingly vital for job seekers. Machine Learning with Python This course covers the fundamentals of machine learning algorithms and when to use each of them. and evaluating the same.

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Marktechpost

DECEMBER 23, 2023

Deep Neural Networks (DNNs) represent a powerful subset of artificial neural networks (ANNs) designed to model complex patterns and correlations within data. These sophisticated networks consist of multiple layers of interconnected nodes, enabling them to learn intricate hierarchical representations.

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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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Analytics Vidhya

JULY 23, 2020

Note: This article was originally published on May 29, 2017, and updated on July 24, 2020 Overview Neural Networks is one of the most. The post Understanding and coding Neural Networks From Scratch in Python and R appeared first on Analytics Vidhya.

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Marktechpost

APRIL 6, 2024

One recent application consists of developing heuristic greedy algorithms for NP-hard problems and creating reward functions for robotics use. Also, researchers use the power of LLMs to develop innovative networking algorithms. Still, it is challenging for LLMs to rank these algorithms and pick the best one.

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Analytics Vidhya

JANUARY 30, 2022

The post Activation Functions for Neural Networks and their Implementation in Python appeared first on Analytics Vidhya.

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Analytics Vidhya

DECEMBER 12, 2022

Introduction In machine learning and deep learning, the amount of data fed to the algorithm is one of the most critical factors affecting the model’s performance. However, in every machine learning or deep learning problem, it is impossible to have enough data to […].

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Marktechpost

MARCH 30, 2024

In 2024, the landscape of Python libraries for machine learning and deep learning continues to evolve, integrating more advanced features and offering more efficient and easier ways to build, train, and deploy models. PyTorch PyTorch is a widely used open-source machine learning library based on the Torch library.

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Marktechpost

FEBRUARY 4, 2024

Meta-learning, a burgeoning field in AI research, has made significant strides in training neural networks to adapt swiftly to new tasks with minimal data. This technique centers on exposing neural networks to diverse tasks, thereby cultivating versatile representations crucial for general problem-solving.

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Marktechpost

MAY 10, 2024

Machine learning is widely applied in finance for tasks like credit scoring, fraud detection, and trading. This course recommends top machine learning courses for finance professionals aiming to harness these techniques for better decision-making and performance.

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Marktechpost

JANUARY 24, 2024

However, deep neural networks are inaccurate and can produce unreliable outcomes. It is found that incorporating uncertainty quantification (UQ) into deep learning models gauges their confidence level regarding predictions. It can improve deep neural networks’ reliability in inverse imaging issues.

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Pickl AI

AUGUST 3, 2023

Introduction to Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning techniques that are designed to automatically learn and represent data in multiple layers of abstraction. How Deep Learning Algorithms Work?

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Machine Learning Research at Apple

DECEMBER 6, 2023

Parallelization techniques have become ubiquitous for accelerating inference and training of deep neural networks. In this work, we introduce DeepPCR, a novel algorithm which parallelizes… Despite this, several operations are still performed in a sequential manner.

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IBM Journey to AI blog

DECEMBER 19, 2023

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

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Towards AI

FEBRUARY 16, 2024

Image adapted from Adobe Stock Machine learning is becoming a significant tool in the field of chemistry, providing new opportunities in various areas such as drug discovery and materials science. Recently, the combination of machine learning and chemistry has made significant progress.

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Analytics Vidhya

SEPTEMBER 29, 2023

Introduction In deep learning, optimization algorithms are crucial components that help neural networks learn efficiently and converge to optimal solutions.

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NYU Center for Data Science

JANUARY 31, 2024

The idea that neural networks undergo distinct developmental phases during training has long been a subject of debate and fascination. Published in the Transactions on Machine Learning Research (TMLR) , Hu’s work offers new insights into the training dynamics of neural networks.

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Google Research AI blog

AUGUST 17, 2023

Posted by Hussein Hazimeh, Research Scientist, Athena Team, and Riade Benbaki, Graduate Student at MIT Modern neural networks have achieved impressive performance across a variety of applications, such as language, mathematical reasoning , and vision. In standard neural networks, each weight defines a connection between two neurons.

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Towards AI

APRIL 7, 2024

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? You just want to create and analyze simple maps not to learn algebra all over again.

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Marktechpost

FEBRUARY 8, 2024

With the world of computational science continually evolving, physics-informed neural networks (PINNs) stand out as a groundbreaking approach for tackling forward and inverse problems governed by partial differential equations (PDEs). Despite these efforts, the search for an optimal solution remains ongoing.

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Analytics Vidhya

APRIL 24, 2023

Introduction We live in a world where social media platforms shape our interests, tailor our news feeds, and provide customized content, all thanks to machine learning! With machine learning (ML), a branch of artificial intelligence (AI), software programs can predict outcomes more accurately without being explicitly instructed.

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Marktechpost

OCTOBER 16, 2023

In a recent paper, “Towards Monosemanticity: Decomposing Language Models With Dictionary Learning,” researchers have addressed the challenge of understanding complex neural networks, specifically language models, which are increasingly being used in various applications. We are also on WhatsApp.

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IBM Journey to AI blog

OCTOBER 16, 2023

Machine learning (ML)—the artificial intelligence (AI) subfield in which machines learn from datasets and past experiences by recognizing patterns and generating predictions—is a $21 billion global industry projected to become a $209 billion industry by 2029.

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Marktechpost

JANUARY 19, 2024

Epigenetic clocks accurately estimate biological age based on DNA methylation, but their underlying algorithms and key aging processes must be better understood. Epigenetic clocks estimate chronological age using supervised machine learning and CpG combinations. Check out the Paper.

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IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

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Marktechpost

APRIL 21, 2024

Integrating machine learning into PDE research has revolutionized both fields, offering new avenues to tackle PDE complexities. ML’s ability to approximate complex functions has led to algorithms that can solve, simulate, and even discover PDEs from data.

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IBM Journey to AI blog

MAY 10, 2024

Underpinning most artificial intelligence (AI) deep learning is a subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain. GPUs can rapidly process large datasets and greatly decrease time spent training machine learning models.

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Marktechpost

JANUARY 25, 2024

It relies on the distortion of objects in a density-varying medium due to light refraction, with digital image correlation or optical flow algorithms used for analysis. Existing algorithms, mostly based on cross-correlation, are optimized for PIV and provide sparse velocity vectors. Michigan Ave.,

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Marktechpost

SEPTEMBER 1, 2023

Incorporating machine learning and deep learning algorithms has shown promise in bolstering security. In addition, the study highlights that hybrid schemes combining deep learning features with deep learning/machine learning classification can significantly improve authentication performance.

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Analytics Vidhya

APRIL 9, 2023

Geoffrey Hinton: Godfather of AI Geoffrey Hinton, often considered the “godfather of artificial intelligence,” has been pioneering machine learning since before it became a buzzword. Hinton has made significant contributions to the development of artificial neural networks and machine learning algorithms.

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Marktechpost

MARCH 21, 2024

The fields of Machine Learning (ML) and Artificial Intelligence (AI) are significantly progressing, mainly due to the utilization of larger neural network models and the training of these models on increasingly massive datasets. If you like our work, you will love our newsletter.

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Marktechpost

MARCH 8, 2024

Within this broad field, the challenge of developing algorithms that learn effectively and efficiently from limited interactions with their environment remains paramount. Sample efficiency refers to an algorithm’s ability to learn effective behaviors from a minimal number of interactions with the environment.

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Marktechpost

JULY 15, 2023

Since the emergence of large-scale OT and Wasserstein GANs, machine learning has increasingly embraced using neural networks to solve optimum transport (OT) issues. The theoretical underpinnings of the algorithm make its output more easily understood than competing methods.

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