Marktechpost

NOVEMBER 25, 2024

Natural neural systems have inspired innovations in machine learning and neuromorphic circuits designed for energy-efficient data processing. This limits their adaptability, reducing their ability to learn autonomously after deployment. The network used binary activations, discrete weights, and a three-layer feedforward MLP.

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Unite.AI

SEPTEMBER 4, 2024

Our multi-layered approach combines proprietary algorithms with third-party data to stay ahead of evolving fraud tactics. Deep Neural Network (DNN) Models: Our core infrastructure utilizes multi-stage DNN models to predict the value of each impression or user. This resulted in a 75% decrease in Cost Per Acquisition (CPA) and 12.3

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IBM Journey to AI blog

JANUARY 10, 2024

AI operates on three fundamental components: data, algorithms and computing power. Data: AI systems learn and make decisions based on data, and they require large quantities of data to train effectively, especially in the case of machine learning (ML) models. What is artificial intelligence and how does it work?

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Towards AI

OCTOBER 3, 2024

Liquid Neural Networks: Research focuses on developing networks that can adapt continuously to changing data environments without catastrophic forgetting. These networks excel at processing time series data, making them suitable for applications like financial forecasting and climate modeling.

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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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Unite.AI

JULY 25, 2023

Known as “catastrophic forgetting” in AI terms, this phenomenon severely impedes the progress of machine learning , mimicking the elusive nature of human memories. This insight is pivotal in understanding how continual learning can be optimized in machines to closely resemble the cognitive capabilities of humans.

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Unite.AI

JULY 25, 2023

An AI feedback loop is an iterative process where an AI model's decisions and outputs are continuously collected and used to enhance or retrain the same model, resulting in continuous learning, development, and model improvement. Bias & Fairness: AI models can develop bias and fairness issues.

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Pickl AI

NOVEMBER 13, 2024

Summary: Backpropagation in neural network optimises models by adjusting weights to reduce errors. Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems.

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Pickl AI

MARCH 7, 2025

Key Takeaways Neuromorphic systems replicate the human brain’s neural networks. Systems learn dynamically, mimicking the human brain’s synaptic plasticity. These systems use spiking neural networks (SNNs) , where artificial neurons process information only when triggered by electrical signals (spikes).

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The MLOps Blog

FEBRUARY 22, 2024

TL;DR: In many machine-learning projects, the model has to frequently be retrained to adapt to changing data or to personalize it. Continual learning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. What is continual learning?

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Unite.AI

AUGUST 3, 2023

The study of psychology sparked my fascination with the human mind and intelligence, particularly the process of skills learning and expertise development. Meanwhile, statistics provided the mathematical foundation to explore artificial neural networks , inspired by our biological brain. It’s a thrilling journey.

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Unite.AI

AUGUST 28, 2023

Traditional Computing Systems : From basic computing algorithms, the journey began. Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience. These systems could solve pre-defined tasks using a fixed set of rules.

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Unite.AI

JUNE 13, 2023

A lot of the assumptions that you make that these algorithms are based on, when they go to the real world, they don't hold, and then you have to figure out how to deal with that. One thing that I'm particularly excited about is this notion of continual learning. ” We figured out what was going on.

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Unite.AI

DECEMBER 19, 2023

DL is built on a neural network and uses its “brain” to continuously train itself on raw data. During the training phase, we add “noise,” which changes the raw data from the files we feed into our algorithm, in order to automatically generate slight “mutations,” which are fed in each training cycle during our training phase.

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Pickl AI

APRIL 19, 2024

Select the right learning path tailored to your goals and preferences. Continuous learning is critical to becoming an AI expert, so stay updated with online courses, research papers, and workshops. Specialise in domains like machine learning or natural language processing to deepen expertise.

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

The incorporation of continuous learning enables the model training to automatically adapt and learn from new challenging scenarios as they arise. This self-improving capability helps ensure the system maintains high performance, even as shopping environments continue to evolve.

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Flipboard

APRIL 17, 2023

We will put everything we learned so far into gradually building a multilayer perceptron (MLP) with PyTrees. We hope this post will be a valuable resource as you continue learning and exploring the world of JAX. In the context of a neural network, a PyTree can be used to represent the weights and biases of the network.

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Flipboard

FEBRUARY 6, 2023

In high school, he and his friends wired up the school’s computers for machine learning algorithm training, an experience that planted the seeds for Steinberger’s computer science degree and his job at Meta as an AI researcher.

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Google Research AI blog

JUNE 23, 2023

An important algorithmic piece of cache management is the decision policy used for dynamically updating the set of items being stored, which has been extensively optimized over several decades, resulting in several efficient and robust heuristics. This process is based on efficient random sampling that approximates exact priority queues.

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Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field.

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Pickl AI

JULY 10, 2024

Introduction Artificial Intelligence (AI) and Machine Learning are revolutionising industries by enabling smarter decision-making and automation. In this fast-evolving field, continuous learning and upskilling are crucial for staying relevant and competitive. Examination of generative AI and large language models (LLMs).

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Pickl AI

SEPTEMBER 23, 2024

Summary: This guide covers the most important Deep Learning interview questions, including foundational concepts, advanced techniques, and scenario-based inquiries. Gain insights into neural networks, optimisation methods, and troubleshooting tips to excel in Deep Learning interviews and showcase your expertise.

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Pickl AI

OCTOBER 20, 2024

You should have a good grasp of linear algebra (for handling vectors and matrices), calculus (for understanding optimisation), and probability and statistics (for Data Analysis and decision-making in AI algorithms). ML is a specific approach within AI that uses algorithms to identify patterns in data. Deep Learning is a subset of ML.

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Mlearning.ai

JULY 11, 2023

Get familiar with terms like supervised learning (teaching a computer with labeled examples), unsupervised learning (letting a computer learn from unlabeled data), and reinforcement learning (rewarding a computer for making good choices). Also, learn about common algorithms used in machine learning.

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How to Learn Machine Learning

AUGUST 15, 2024

Shows continuous learning : Regular updates to your to portfolio demonstrate your commitment to staying current in this rapidly evolving field. Opens doors to opportunities : A strong portfolio can attract job offers, freelance work, or collaborations in the machine learning community.

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Towards AI

MARCH 3, 2025

Learn and Adapt: World models allow for continuous learning. These models leverage convolutional and recurrent neural networks to capture both spatial features and temporal dynamics. As a robot interacts with its surroundings, it refines its internal model to improve prediction accuracy.

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Unite.AI

JULY 31, 2023

The ultimate goal of the SEER model is to help in developing strategies for the pre-training process that use uncurated data to deliver top-notch state of the art performance in transfer learning. The Sinkhorn regularization parameter is set to 0.05, and it performs 10 iterations of the algorithm.

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Google Research AI blog

NOVEMBER 16, 2022

Posted by Yanqi Zhou, Research Scientist, Google Research, Brain Team The capacity of a neural network to absorb information is limited by the number of its parameters, and as a consequence, finding more effective ways to increase model parameters has become a trend in deep learning research. Expert Gate ). Token Choice Routing.

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Marktechpost

MARCH 3, 2025

An agentic AI is designed to autonomously plan, execute multi-step tasks, and continuously learn from feedback. Some agents may update their policies over time using reinforcement learning, but this learning is often isolated from real-time operation. In contrast, agentic AI systems are built to be adaptive.

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Pickl AI

JUNE 9, 2024

Overcoming challenges through practical applications, continuous learning, and resource utilisation is key to success. Rooted in Mathematical Principles In Data Science, algorithms, models, and analytical frameworks are intricately tied to mathematical concepts, such as statistics, linear algebra, and calculus.

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Viso.ai

JANUARY 16, 2024

Artificial Neural Networks (ANNs) are the cornerstone of modern artificial intelligence (AI). This enables ANNs to learn and make intelligent decisions based on input data. This diagram showcases how various layers interact in a neural network – source. The algorithm creator? The prompt writer?

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Pickl AI

JULY 31, 2023

Examples of Eager Learning Algorithms: Logistic Regression : A classic Eager Learning algorithm used for binary classification tasks. It learns the relationship between features and class labels during training and then predicts the probability of an instance belonging to a specific class.

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Pickl AI

SEPTEMBER 30, 2024

For instance, an ML model can learn to distinguish between spam and non-spam emails by analysing thousands of examples, recognising patterns, and improving its accuracy without additional programming. Key concepts in ML are: Algorithms : Algorithms are the mathematical instructions that guide the learning process.

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How to Learn Machine Learning

JUNE 9, 2024

It enables machines to recognize patterns in training data and learn without human assistance. What makes them fantastic is their ability to learn from their past interactions. This continuous learning enables the ML systems to improve their outcomes and make better predictions on new data over time.

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Pickl AI

DECEMBER 16, 2024

From high-quality data to robust algorithms and infrastructure, each component is critical in ensuring AI delivers accurate and impactful results. Algorithms Algorithms form the core of AI systems. Selecting the right algorithm is crucial as it impacts model accuracy, speed, and scalability.

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Chatbots Life

SEPTEMBER 19, 2024

In the case of chatbots, machine learning enables the chatbot to interact with users, understand their inputs, and respond intelligently. Chatbot machine learning refers to the use of algorithms that allow a chatbot to learn from data. Generative Chatbot: This type of chatbot doesn’t rely on a predefined set of answers.

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Pickl AI

MARCH 20, 2024

Machine Learning algorithms A deep understanding of machine learning algorithms is indispensable for Data Scientists seeking to build predictive models and uncover patterns in data. Leveraging libraries such as Scikit-learn and TensorFlow, Data Scientists can implement machine learning algorithms with ease and precision.

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