Analytics Vidhya

APRIL 28, 2023

Introduction Predicting patient outcomes is critical to healthcare management, enabling hospitals to optimize resources and improve patient care. Machine learning algorithms or deep learning techniques have proven valuable in survival prediction rates, offering insights that can help guide treatment plans and prioritize resources.

Deep Learning 271

Deep Learning Machine Learning Optimization Management 271

Analytics Vidhya

SEPTEMBER 3, 2021

The post Optimize your optimizations using Optuna appeared first on Analytics Vidhya. It is widely and exclusively used by the Kaggle community for the past 2 years and since the platform has such competitiveness, and for it to achieve such domination, […].

Optimization 336

Optimization Data Science Publishing Analytics 336

Analytics Vidhya

DECEMBER 23, 2023

Introduction In deep learning, the Adam optimizer has become a go-to algorithm for many practitioners. Its ability to adapt learning rates for different parameters and its gentle computational requirements make it a versatile and efficient choice.

Optimization 302

Optimization Deep Learning IT Analytics 302

Analytics Vidhya

JUNE 14, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction In Neural Networks, we have the concept of Loss Functions, The post Complete Guide to Gradient-Based Optimizers in Deep Learning appeared first on Analytics Vidhya.

Deep Learning 149

Deep Learning Optimization Data Science Publishing 149

Analytics Vidhya

JULY 25, 2022

Introduction The gradient descent algorithm is an optimization algorithm mostly used in machine learning and deep learning. In linear regression, it finds weight and biases, and deep learning backward propagation uses the […]. This article was published as a part of the Data Science Blogathon.

Deep Learning 364

Deep Learning IT Machine Learning Data Science 364

Analytics Vidhya

NOVEMBER 20, 2024

In machine learning, a similar challenge exists with gradient descent, where using […] The post What is Adaptive Gradient(Adagrad) Optimizer? If you used the same amount of water on all of them every day, some plants would thrive, while others might get overwatered or dry out. appeared first on Analytics Vidhya.

Optimization 288

Optimization Machine Learning Analytics Deep Learning 288

Analytics Vidhya

OCTOBER 30, 2023

The most challenging part of integrating AI into an application is […] The post Mastering AI Optimization and Deployment with Intel’s OpenVINO Toolkit appeared first on Analytics Vidhya. Enterprises and businesses believe in integrating reliable and responsible AI in their application to generate more revenue.

Optimization 347

Optimization Software Enterprise Analytics 347

Analytics Vidhya

SEPTEMBER 16, 2020

Train, Export, Optimize (TensorRT), Infer (Jetson Nano) appeared first on Analytics Vidhya. Part 1 — Detailed steps from training a detector on a custom dataset to inferencing on jetson nano board or cloud using TensorFlow 1.15. The post TensorFlow Object Detection — 1.0 & 2.0:

Optimization 354

Optimization Analytics Unstructured Data Deep Learning 354

Analytics Vidhya

MARCH 22, 2022

Introduction Training a Deep Learning model from scratch can be a tedious task. You have to find the right training weights, get the optimal learning rates, find the best hyperparameters and the architecture that will best suit your data and model.

Deep Learning 356

Deep Learning Optimization Modeling Analytics 356

Analytics Vidhya

JANUARY 29, 2024

But, here’s the problem: this encyclopedia is huge and requires significant time and effort […] The post Optimizing Neural Networks: Unveiling the Power of Quantization Techniques appeared first on Analytics Vidhya. Now, this friend has a precise way of doing things, like he has a dictionary in his head.

Optimization 311

Optimization Analytics IoT Deep Learning 311

Analytics Vidhya

MAY 24, 2021

The post Neural network and hyperparameter optimization using Talos appeared first on Analytics Vidhya. ArticleVideo Book This article was published as a part of the Data Science Blogathon In terms of ML, what neural network means? A neural network.

Optimization 271

Optimization Data Science Publishing Analytics 271

KDnuggets

DECEMBER 1, 2023

Curious about optimizing AI for everyday devices? Dive into the complete overview of MIT's TinyML and Efficient Deep Learning Computing course. Explore strategies to make AI smarter on small devices. Read the full article for an in-depth look!

Deep Learning 154

Deep Learning Optimization Strategy Machine Learning 154

David Menninger's Analyst Perspectives

SEPTEMBER 16, 2021

Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. It can support AI/ML processes with data preparation, model validation, results visualization and model optimization.

Data Science 341

Data Science Data Lake Data mining Deep Learning 341

Analytics Vidhya

MARCH 20, 2024

Introduction Optimizing deep learning is a critical aspect of training efficient and accurate neural networks. Various optimization algorithms have been developed to improve the convergence speed.

Deep Learning 288

Deep Learning Optimization Analytics IT 288

KDnuggets

DECEMBER 5, 2019

Deep learning models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another. Read this post on some of the numerous composite technologies which allow deep learning its complex nonlinearity.

Deep Learning 119

Deep Learning Technology Modeling IT 119

KDnuggets

NOVEMBER 9, 2021

But this format is not optimized for deep learning work. In this article we are discussing that HDF5 is one of the most popular and reliable formats for non-tabular, numerical data. This article suggests what kind of ML native data format should be to truly serve the needs of modern data scientists.

Deep Learning 121

Deep Learning Optimization Management 121

IBM Big Data Hub

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. Deep learning requires a tremendous amount of computing power.

Deep Learning 101

Deep Learning Cost-Benefit Manufacturing Optimization 101

Domino Data Lab

JULY 10, 2019

New tools are constantly being added to the deep learning ecosystem. For example, there have been multiple promising tools created recently that have Python APIs, are built on top of TensorFlow or PyTorch , and encapsulate deep learning best practices to allow data scientists to speed up research.

Deep Learning 110

Deep Learning Visualization Recreation/Entertainment Data Processing 110

Analytics Vidhya

FEBRUARY 27, 2024

In this article, we will explore the features of Ayanza, its benefits, how to […] The post Optimize Project and Team Management With AI appeared first on Analytics Vidhya. With the advancement of technology, various tools have been developed to streamline these processes and enhance productivity.

Optimization 178

Optimization Management Technology Analytics 178

Dataiku

OCTOBER 4, 2021

Whether it is forecasting future sales to optimize inventory, predicting energy consumption to adapt production levels, or estimating the number of airline passengers to ensure high-quality services, time is a key variable.

Forecasting 138

Forecasting Deep Learning IT Sales 138

O'Reilly on Data

SEPTEMBER 12, 2019

The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. Continue reading Understanding deep neural networks.

Deep Learning 41

Deep Learning Statistics Data Processing Optimization 41

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

CIO Business Intelligence

NOVEMBER 27, 2024

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

Data Architecture 128

Data Architecture Cost-Benefit Machine Learning Experimentation 128

CIO Business Intelligence

NOVEMBER 19, 2024

All industries and modern applications are undergoing rapid transformation powered by advances in accelerated computing, deep learning, and artificial intelligence. This architecture aims to optimize the utilization of network and flash resources, allowing a more flexible and cost-effective approach to storage.

Management 116

Management Unstructured Data Deep Learning Metadata 116

O'Reilly on Data

DECEMBER 10, 2019

Those tools are starting to appear, particularly for building deep learning models. At O’Reilly’s AI Conference in Beijing, Tim Kraska of MIT discussed how machine learning models have out-performed standard, well-known algorithms for database optimization, disk storage optimization, basic data structures, and even process scheduling.

Software 335

Software Machine Learning Risk Data-driven 335

Smart Data Collective

MAY 2, 2019

Big data has been especially important for optimizing their marketing campaigns. There are a number of deep learning tools that evaluate social media activity. This is an overlooked benefit of using big data for keyword research and optimization. Large companies around the world are investing in big data.

Big Data 112

Big Data Optimization Modeling Deep Learning 112

Analytics Vidhya

JUNE 21, 2023

Accurate predictions can help businesses make informed decisions, optimize processes, and gain a competitive edge. Introduction Time-series forecasting plays a crucial role in various domains, including finance, weather prediction, stock market analysis, and resource planning.

Forecasting 343

Forecasting Finance Optimization Modeling 343

Analytics Vidhya

APRIL 11, 2024

Employing a tiered approach empowers individuals and organizations to select the optimal model aligning with their specific requirements. Introduction Anthropic’s Claude 3 API ushers in a new era of artificial intelligence, offering a comprehensive suite of AI assistants tailored to diverse user needs.

Optimization 311

Optimization Modeling Analytics Deep Learning 311

O'Reilly on Data

MARCH 7, 2019

Language understanding benefits from every part of the fast-improving ABC of software: AI (freely available deep learning libraries like PyText and language models like BERT ), big data (Hadoop, Spark, and Spark NLP ), and cloud (GPU's on demand and NLP-as-a-service from all the major cloud providers). negation) detection.

Deep Learning 279

Deep Learning Testing Machine Learning Modeling 279

Analytics Vidhya

MAY 22, 2023

Additionally, it runs using software that optimizes programs using Meta’s PyTorch open-source developer […] The post Meta Reveals AI Chips to Revolutionize Computing appeared first on Analytics Vidhya. The chip is the Meta Training and Inference Accelerator (MTIA).

Optimization 327

Optimization Software Analytics IT 327

CIO Business Intelligence

FEBRUARY 6, 2025

Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. In retail, they can personalize recommendations and optimize marketing campaigns. Sustainable IT is about optimizing resource use, minimizing waste and choosing the right-sized solution. Theyre impressive, no doubt.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

Analytics Vidhya

AUGUST 27, 2024

Optimal Character Recognition (OCR) is the foundation of building vast encoder-decoder models. Introduction Diving into the world of AI models, language models and other software that can be applied in real tasks like virtual assistance and content creation are very popular. However, there is still a lot to explore with image-to-text models.

Modeling 289

Modeling Optimization Software Analytics 289

O'Reilly on Data

JULY 28, 2020

If this sounds fanciful, it’s not hard to find AI systems that took inappropriate actions because they optimized a poorly thought-out metric. CTRs are easy to measure, but if you build a system designed to optimize these kinds of metrics, you might find that the system sacrifices actual usefulness and user satisfaction.

Marketing 364

Marketing Experimentation Metrics Testing 364

O'Reilly on Data

JUNE 7, 2022

And granted, a lot can be done to optimize training (and DeepMind has done a lot of work on models that require less energy). That’s an allusion to the debate ( sometimes on Twitter ) between LeCun and Gary Marcus, who has argued many times that combining deep learning with symbolic reasoning is the only way for AI to progress. (In

Modeling 364

Modeling Interactive Optimization Deep Learning 364

Domino Data Lab

APRIL 29, 2021

Deep learning enthusiasts are increasingly putting NVIDIA’s GTC at the top of their gotta-be-there conference list. Three of them were particularly compelling and inspired a new point of view on transfer learning that I feel is important for analytical practitioners and leaders to understand. Deep Learning Trends from GTC21.

Data Science 134

Data Science Deep Learning Modeling Forecasting 134

Analytics Vidhya

FEBRUARY 8, 2024

In addition, mastering bitwise operators is crucial for tasks such as low-level programming, optimization, and implementing specific […] The post 6 Major Bitwise Operators in Python appeared first on Analytics Vidhya.

Optimization 306

Optimization Analytics Deep Learning Machine Learning 306

Rocket-Powered Data Science

DECEMBER 16, 2018

So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. What is missing in the above discussion is the deeper set of unknowns in the learning process. This is the meta-learning phase.

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203