Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

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Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Towards AI

OCTOBER 3, 2024

Neuroplasticity in AI Promising Research: a. Liquid Neural Networks: Research focuses on developing networks that can adapt continuously to changing data environments without catastrophic forgetting. By adjusting their parameters in real-time, liquid neural networks handle dynamic and time-varying data efficiently.

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Unite.AI

JANUARY 12, 2024

The Evolution of AI Research As capabilities have grown, research trends and priorities have also shifted, often corresponding with technological milestones. The rise of deep learning reignited interest in neural networks, while natural language processing surged with ChatGPT-level models.

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Marktechpost

JULY 6, 2023

As a result, it becomes necessary for humans to comprehend these algorithms and their workings on a deeper level. 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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IBM Journey to AI blog

JULY 6, 2023

Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention. Deep learning algorithms are neural networks modeled after the human brain. Some people worry that AI and machine learning will eliminate jobs.

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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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Unite.AI

NOVEMBER 22, 2024

It helps explain how AI models, especially LLMs, process information and make decisions. By using a specific type of neural network called sparse autoencoders (SAEs) , Gemma Scope breaks down these complex processes into simpler, more understandable parts. Finally, Gemma Scope plays a role in improving AI safety.

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Pickl AI

AUGUST 28, 2024

Summary: This comprehensive guide covers the basics of classification algorithms, key techniques like Logistic Regression and SVM, and advanced topics such as handling imbalanced datasets. Classification algorithms are crucial in various industries, from spam detection in emails to medical diagnosis and customer segmentation.

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Dlabs.ai

AUGUST 16, 2022

This is the challenge that explainable AI solves. Explainable artificial intelligence shows how a model arrives at a conclusion. What is explainable AI? Explainable artificial intelligence (or XAI, for short) is a process that helps people understand an AI model’s output. Let’s begin.

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Towards AI

JULY 22, 2023

Last Updated on July 24, 2023 by Editorial Team Author(s): Data Science meets Cyber Security Originally published on Towards AI. Now Algorithms know what they are doing and why! Let us go further into the enigmas of Artificial Intelligence, where AI is making waves like never before! SOURCE: [link] A.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

Large language models (LLMs) are a class of foundational models (FM) that consist of layers of neural networks that have been trained on these massive amounts of unlabeled data. IBM watsonx.data is a fit-for-purpose data store built on an open lakehouse architecture to scale AI workloads for all of your data, anywhere.

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Mlearning.ai

DECEMBER 31, 2023

Principles of Explainable AI( Source ) Imagine a world where artificial intelligence (AI) not only makes decisions but also explains them as clearly as a human expert. This isn’t a scene from a sci-fi movie; it’s the emerging reality of Explainable AI (XAI). What is Explainable AI?

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Marktechpost

MAY 12, 2024

It is based on adjustable and explainable AI technology. Fraud.net Fraud.net’s AI and Machine Learning Models use deep learning, neural networks, and data science methodologies to improve insights for various industries, including financial services, e-commerce, travel and hospitality, insurance, etc.

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ODSC - Open Data Science

OCTOBER 24, 2023

Then, how to essentially eliminate training, thus speeding up algorithms by several orders of magnitude? My NoGAN algorithm, probably for the first time, comes with the full multivariate KS distance to evaluate results. Indeed, the whole technique epitomizes explainable AI. It is adjusted for dimension.

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Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms.

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Marktechpost

AUGUST 24, 2023

r/computervision Computer vision is the branch of AI science that focuses on creating algorithms to extract useful information from raw photos, videos, and sensor data. r/learnmachinelearning The subreddit is dedicated to learning the latest machine-learning algorithms. There are about 68k members. It has over 37.4k

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ODSC - Open Data Science

DECEMBER 17, 2024

But the growing role of AI is sparking debates about its fairness, transparency, and long-term implications. How AI Shapes Loan Decisions AI algorithms analyze vast amounts of data, including credit histories, employment records, and spending habits, to predict the likelihood of repayment.

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Pickl AI

JULY 14, 2023

The following blog will emphasise on what the future of AI looks like in the next 5 years. Evolution of AI The evolution of Artificial Intelligence (AI) spans several decades and has witnessed significant advancements in theory, algorithms, and applications.

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Viso.ai

MARCH 26, 2024

With advancements in machine learning (ML) and deep learning (DL), AI has begun to significantly influence financial operations. Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. 4: Algorithmic Trading and Market Analysis No.5:

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Ofemwire

MARCH 1, 2024

What is AI Artificial Intelligence, commonly referred to as AI, embodies the simulation of human intelligence processes by machines, especially computer systems. If you dont get that, let me explain what AI is, like I would do to a fifth grader. These processes include learning, reasoning, and self-correction.

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Pickl AI

SEPTEMBER 17, 2024

Machine Learning (ML) is a subset of AI that focuses on developing algorithms and statistical models that enable systems to perform specific tasks effectively without being explicitly programmed. Explain The Concept of Supervised and Unsupervised Learning. What Is the Role of Explainable AI (XAI) In Machine Learning?

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How to Learn Machine Learning

JUNE 9, 2024

This technology streamlines the model-building process while simultaneously increasing productivity by determining the best algorithms for specific data sets. It is quite beneficial for organizations looking to capitalize on the potential of AI without making significant investments.

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Pickl AI

JULY 24, 2024

The pivotal moment in AI’s history occurred with the work of Alan Turing in the 1930s and 1940s. Turing proposed the concept of a “universal machine,” capable of simulating any algorithmic process. During this period, optimism about AI’s potential led to substantial funding and research initiatives.

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Pickl AI

JANUARY 29, 2024

Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. The collective strength of both forms the groundwork for AI and Data Science, propelling innovation. Key takeaways Data Science lays the groundwork for Machine Learning, providing curated datasets for ML algorithms to learn and make predictions.

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Viso.ai

DECEMBER 6, 2023

Computer vision (CV) is a rapidly evolving area in artificial intelligence (AI), allowing machines to process complex real-world visual data in different domains like healthcare, transportation, agriculture, and manufacturing. The purpose is to give you an idea of modern computer vision algorithms and applications. Get a demo here.

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Dlabs.ai

MAY 22, 2022

It’ll help you get to grips with the fundamentals of ML and its respective algorithms, including linear regression and supervised and unsupervised learning, among others. Here, we’ll focus more on his AI courses, particularly the one on ML (one of the most popular and highly-rated Machine Learning online courses around).

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Defined.ai blog

AUGUST 10, 2023

AI refers to computer systems capable of executing tasks that typically require human intelligence. On the other hand, ML, a subset of AI, involves algorithms that improve through experience. These algorithms learn from data, making the software more efficient and accurate in predicting outcomes without explicit programming.

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Pickl AI

SEPTEMBER 17, 2024

This is where AI steps in, offering advanced capabilities in threat detection, prevention, and response. By leveraging Machine Learning algorithms and predictive analytics, AI-powered cybersecurity solutions can proactively identify and mitigate risks, providing a more robust and adaptive defence against cyber criminals.

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Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Basics of Machine Learning Machine Learning is a subset of Artificial Intelligence (AI) that allows systems to learn from data, improve from experience, and make predictions or decisions without being explicitly programmed.

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Pickl AI

NOVEMBER 5, 2024

Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. For example, PayPal uses Machine Learning algorithms to analyse transaction patterns and identify anomalies that may indicate fraudulent activity.

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Heartbeat

NOVEMBER 27, 2023

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. Deep learning automates and improves medical picture analysis.

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Heartbeat

SEPTEMBER 13, 2023

Through several " Explainable Models ," explainability enables users to achieve insights into the inner workings of the models and understand the factors that influence their output. Algorithmic Accountability: Explainability ensures accountability in machine learning and AI systems.

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Bugra Akyildiz

MAY 25, 2024

Model Selection and Tuning: ChatGPT could guide users through the process of selecting appropriate machine learning algorithms, tuning hyperparameters, and evaluating model performance using techniques like cross-validation or holdout sets. The BPE algorithm is "byte-level" because it runs on UTF-8 encoded strings. Sennrich et al.

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The MLOps Blog

JUNE 27, 2023

For example, if your team works on recommender systems or natural language processing applications, you may want an MLOps tool that has built-in algorithms or templates for these use cases. Scale AI combines human annotators and machine learning algorithms to deliver efficient and reliable annotations for your team.

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Viso.ai

MAY 10, 2024

Basic types of machine learning models Some common types of machine learning algorithms include: Regression models (e.g. Training procedure The high-level training procedure for building an AI model is pretty much the same regardless of the type of model. text vs images) and (2) the desired output (e.g.

Machine Learning 52

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IBM Journey to AI blog

SEPTEMBER 3, 2024

Artificial intelligence (AI) has enormous value but capturing the full benefits of AI means facing and handling its potential pitfalls. Here’s a closer look at 10 dangers of AI and actionable risk management strategies. Bias Humans are innately biased, and the AI we develop can reflect our biases.

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