Towards AI

AUGUST 29, 2023

This story explores automatic differentiation, a feature of modern Deep Learning frameworks that automatically calculates the parameter gradients during the training loop. The story introduces this technology in conjunction with practical examples using Python and C++. What is the relevance of autodiff?

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Marktechpost

MAY 15, 2024

In the vast world of data science, countless tools are available to help analysts and researchers make sense of data and build powerful machine-learning models. Here are the ten great Python packages that can significantly enhance your workflow. PyCaret : PyCaret is a game-changer for machine learning workflows.

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Analytics Vidhya

NOVEMBER 19, 2019

Overview The attention mechanism has changed the way we work with deep learning algorithms Fields like Natural Language Processing (NLP) and even Computer Vision. The post A Comprehensive Guide to Attention Mechanism in Deep Learning for Everyone appeared first on Analytics Vidhya.

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Analytics Vidhya

AUGUST 6, 2022

Image processing algorithms take a long time to process the data because of the large images and the amount of information available in it. The post Comprehensive Guide to Edge Detection Algorithms appeared first on Analytics Vidhya. Introduction Image processing is a widely used concept to exploit the information from the images.

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Analytics Vidhya

OCTOBER 26, 2022

Introduction In this article, we will learn how to make an object tracker using OpenCV in Python and using, and we will build an object tracker and make a counter system. The post Making Centroid Tracker and Counter System in Python appeared first on Analytics Vidhya.

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Analytics Vidhya

JUNE 15, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon What are Genetic Algorithms? Genetic Algorithms are search algorithms inspired by. The post Genetic Algorithms and its use-cases in Machine Learning appeared first on Analytics Vidhya.

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ODSC - Open Data Science

OCTOBER 17, 2023

But without a strong understanding of deep learning, you’ll have a difficult time getting the most out of the cutting-edge developments in the industry. At ODSC West this October 30th to November 2nd, you’ll build the core knowledge and skills you need with the sessions in the deep learning track , such as the ones listed below.

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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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Analytics Vidhya

APRIL 23, 2022

Introduction In this section, we will build a face detection algorithm using Caffe model, but only OpenCV is not involved this time. Instead, along with the computer vision techniques, deep learning skills will also be required, i.e. We will use the deep learning […].

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Marktechpost

JULY 23, 2023

One key area of development is deep learning, where neural networks are trained on huge datasets of images to recognize and classify objects, scenes, and events. In the latest innovation, Jalali-Lab @ UCLA has developed a new Python library called PhyCV, which is the first Physics-based Computer vision Python library.

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Mlearning.ai

JULY 11, 2023

Deep learning has transformed artificial intelligence, allowing machines to learn and make smart decisions. If you’re interested in exploring deep learning, this step-by-step guide will help you learn the basics and develop the necessary skills. Also, learn about common algorithms used in machine learning.

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Marktechpost

MARCH 31, 2024

It covers topics like calculus, probability, linear algebra, and statistics and how to apply them to algorithms like linear regression and logistic regression. The book also provides Python code to explain these concepts. It provides an introduction to data science tools in Python and also provides real-world data analysis problems.

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Analytics Vidhya

FEBRUARY 22, 2023

The ultimate goal of these deep learning algorithms is to mimic the human eye’s capacity to perceive the surrounding environment. Introduction From the 2000s onward, Many convolutional neural networks have been emerging, trying to push the limits of their antecedents by applying state-of-the-art techniques.

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Marktechpost

MAY 10, 2024

This course recommends top machine learning courses for finance professionals aiming to harness these techniques for better decision-making and performance. Machine Learning for Finance in Python This course teaches how to use Python to predict stock values with machine learning.

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Analytics Vidhya

APRIL 14, 2019

There are so many things we can do using computer vision algorithms: Object detection Image segmentation Image. The post Build your First Multi-Label Image Classification Model in Python appeared first on Analytics Vidhya. Introduction Are you working with image data?

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Analytics Vidhya

JULY 23, 2020

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

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ODSC - Open Data Science

APRIL 20, 2023

Deep Learning Approaches to Sentiment Analysis (with spaCy!) In this post, we’ll be demonstrating two deep learning approaches to sentiment analysis, specifically using spaCy. Six Core Competencies Data Scientists Need to Succeed in Their Careers Data scientists need to know more than just algorithms to succeed.

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Analytics Vidhya

JUNE 15, 2021

The post Guide to Gradient Descent and Its Variants with Python Implementation appeared first on Analytics Vidhya. ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction In this article, we’ll cover Gradient Descent along with its.

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IBM Journey to AI blog

DECEMBER 15, 2023

When AI algorithms, pre-trained models, and data sets are available for public use and experimentation, creative AI applications emerge as a community of volunteer enthusiasts builds upon existing work and accelerates the development of practical AI solutions. Morgan’s Athena uses Python-based open-source AI to innovate risk management.

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Towards AI

DECEMBER 28, 2023

Black box algorithms such as xgboost emerged as the preferred solution for a majority of classification and regression problems. Later, Python gained momentum and surpassed all programming languages, including Java, in popularity around 2018–19. CS6910/CS7015: Deep Learning Mitesh M. Khapra Homepage www.cse.iitm.ac.in

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Large Language Models Natural Language Processing Prompt Engineer Prompt Engineering 109

ODSC - Open Data Science

APRIL 18, 2023

In particular, finance has seen some of the strongest benefits from automation and analysis thanks to AI and machine learning. Now, we’d like to go a bit deeper and specifically examine the role of machine learning in algorithmic trading, including portfolio optimization and pattern recognition.

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NVIDIA Developer

MAY 21, 2021

Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. You’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results.

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Marktechpost

APRIL 28, 2024

and also allows the students to build an understanding of machine learning algorithms, including supervised, unsupervised, reinforcement, etc. Introduction to Artificial Intelligence with Python This course has been designed by Harvard University and explores the foundational concepts and algorithms of modern artificial intelligence.

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Artificial Intelligence Artificial Intelligence Prompt Engineer Prompt Engineering 106

Pickl AI

MAY 24, 2023

Introduction One of the most widely used and highly popular programming languages in the technological world is Python. Significantly, despite being user-friendly and easy to learn, one of Python’s many advantages is that it has large collection of libraries. What is a Python Library? What version of Python are you using?

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ODSC - Open Data Science

MAY 25, 2023

sktime — Python Toolbox for Machine Learning with Time Series Editor’s note: Franz Kiraly is a speaker for ODSC Europe this June. Be sure to check out his talk, “ sktime — Python Toolbox for Machine Learning with Time Series ,” there! Welcome to sktime, the open community and Python framework for all things time series.

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Analytics Vidhya

SEPTEMBER 15, 2020

Introduction In this article, we will be taking a deep dive into an interesting algorithm known as “Seam Carving”. The post Seam Carving Algorithm : A Seemingly Impossible Way of Resizing An Image appeared first on Analytics Vidhya. It does a seemingly impossible.

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Analytics Vidhya

JULY 25, 2022

It uses Machine Learning-based Model Algorithms and Deep Learning-based Neural Networks for its implementation. […]. The post YOLO: An Ultimate Solution to Object Detection and Classification appeared first on Analytics Vidhya.

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Analytics Vidhya

JANUARY 12, 2021

ArticleVideos Two different image search engines developed with Deep Learning algorithms Photo by Geran de Klerk on Unsplash Introduction Imagine that you want to. The post Querying Similar Images with TensorFlow appeared first on Analytics Vidhya.

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Marktechpost

JULY 5, 2023

This is where machine learning techniques are proven to be useful. Particularly in “weather forecasting” and “spatial downscaling,” these algorithms have proven to be competitive with more established climate models. When it comes to applying machine learning, the two take contrasting approaches.

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Heartbeat

AUGUST 15, 2023

Advanced techniques have been devised to address these challenges, such as deep learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Python, a widely-used programming language for data science and machine learning, offers numerous libraries and frameworks to implement these techniques.

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Analytics Vidhya

SEPTEMBER 6, 2021

This article was published as a part of the Data Science Blogathon Introduction: Artificial Neural Networks (ANN) are algorithms based on brain function and are used to model complicated patterns and forecast issues. The […]. The post Introduction to Artificial Neural Networks appeared first on Analytics Vidhya.

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Towards AI

MAY 14, 2024

Created by the author with DALL E-3 Machine learning has become very popular in the world of technology, this is evidenced as witnessed in social media with topics like deep learning, artificial intelligence and machine learning dominating the conversation when it comes to technology-related topics.

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Analytics Vidhya

NOVEMBER 1, 2021

Deep learning algorithms can have huge functional uses when provided with quality data to sort through. This article was published as a part of the Data Science Blogathon Introduction In this article, we will cover everything from gathering data to preparing the steps for model training and evaluation.

Deep Learning 301

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Analytics Vidhya

SEPTEMBER 7, 2021

This article was published as a part of the Data Science Blogathon Introduction The deep learning algorithms required the data in a specific order or shape. You will learn through this article […]. First, we have to arrange the data in batches, then we have to feed the batched data to the model in the epoch loop.

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Analytics Vidhya

NOVEMBER 27, 2021

It is a significant step in the process of decision making, powered by Machine Learning or Deep Learning algorithms. This article was published as a part of the Data Science Blogathon. Statistics plays an important role in the domain of Data Science.

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Marktechpost

MARCH 16, 2024

This implies that computations created with FAX, whether they include data center servers or on-device clients, can be quickly and simply deployed and carried out in real-world federated learning contexts. In conclusion, FAX is flexible and can be used for various ML computations in data centers. Check out the Paper and Github.

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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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