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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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. To conclude, the researchers have introduced a precise and interpretable neural network architecture based on DNA methylation for age estimation. Check out the Paper.

Neural Network 112

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Towards AI

MARCH 17, 2024

Alongside this, there is a second boom in XAI or Explainable AI. Explainable AI is focused on helping us poor, computationally inefficient humans understand how AI “thinks.” We will then explore some techniques for building glass-box or explainable models. Ultimately these definitions end up being almost circular!

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Unite.AI

NOVEMBER 6, 2023

Traditional machine learning is a broad term that covers a wide variety of algorithms primarily driven by statistics. The two main types of traditional ML algorithms are supervised and unsupervised. These algorithms are designed to develop models from structured datasets. Do We Still Need Traditional Machine Learning Algorithms?

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Mlearning.ai

JULY 15, 2023

You have certainly seen it on Twitter, everyone is talking about this simple algorithm that beats powerful neural networks in… Continue reading on MLearning.ai »

Algorithm 52

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Marktechpost

MAY 5, 2024

Machine Learning with Python This course covers the fundamentals of machine learning algorithms and when to use each of them. The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow. and evaluating the same.

Machine Learning 117

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Viso.ai

FEBRUARY 8, 2024

introduced deep belief networks (DBNs) in 2006. These deep learning algorithms consist of latent variables and use them to learn underlying patterns within the data. The underlying nodes are linked as a directed acyclic graph (DAG), giving the network generative and discriminative qualities. Geoffrey Hinton et al.

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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. This is where Interpretable (IAI) and Explainable (XAI) Artificial Intelligence techniques come into play, and the need to understand their differences become more apparent.

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NVIDIA

JANUARY 17, 2024

That’s because, he explained, it’s unlikely that humans will be entirely out of the loop in the detection process for the foreseeable future. The company uses various AI algorithms to classify images based on whether they should be reviewed or acted upon — if so, an alert is sent out to a CAL FIRE command center.

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Marktechpost

OCTOBER 8, 2023

By utilizing finely developed neural network architectures, we have models that are distinguished by extraordinary accuracy within their respective sectors. Despite their accurate performance, we must still fully understand how these neural networks function. We have new advancements that have been there with each day.

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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.

Artificial Intelligence 109

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Towards AI

MAY 23, 2023

RL algorithms can be generally categorized into two groups i.e., value-based and policy-based methods. We will discuss the intuition behind PG, how it works, and also provide a code implementation of the algorithm. a neural network), so we can control its outputs by changing its parameters. the return).

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Towards AI

JULY 22, 2023

Our job is to explain complicated tech terms in a simple day-to-day language, so everyone can understand. The algorithm learns from this data, understanding the relationship between the input and the output. Neural Networks: The Brainy Network Picture your brain. But don’t worry! That’s why we’re here.

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Marktechpost

MAY 1, 2024

In contrast, artificial intelligence represents a different paradigm, focusing on specific tasks performed through algorithms, data processing, and machine learning techniques. Human introspection is limited, making it challenging to explain unconscious thoughts.

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AI News

JULY 24, 2023

Compound sparsity combines techniques such as unstructured pruning, quantisation, and distillation to significantly reduce the size of neural networks while maintaining their accuracy. “We This approach challenges the notion that GPUs are necessary for efficient deep learning,” explains Bogunowicz.

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ML Review

MARCH 31, 2019

Up to this point, machine learning algorithms simply didn’t work well enough for anyone to be surprised when it failed to do the right thing. Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. We start with an image of a panda, which our neural network correctly recognizes as a “panda” with 57.7%

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Unite.AI

DECEMBER 1, 2023

Machine learning , a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). The culmination of this training is a machine-learning model.

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Unite.AI

FEBRUARY 16, 2024

Despite achieving remarkable results in areas like computer vision and natural language processing , current AI systems are constrained by the quality and quantity of training data, predefined algorithms, and specific optimization objectives.

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Google Research AI blog

AUGUST 24, 2023

While neural networks have powerful pattern matching capabilities , they are prone to overfitting to spurious statistical patterns in the data. Thus, an important question is whether LLMs are capable of algorithmic reasoning, which involves solving a task by applying a set of abstract rules that define the algorithm.

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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. Explainable AI not only resolves this for us.

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Marktechpost

APRIL 21, 2024

Introduction to Generative AI: This course provides an introductory overview of Generative AI, explaining what it is and how it differs from traditional machine learning methods. It introduces learners to responsible AI and explains why it is crucial in developing AI systems.

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Marktechpost

MARCH 31, 2024

The book covers topics like supervised learning, unsupervised learning, neural networks, support vector machines, etc. It covers topics like calculus, probability, linear algebra, and statistics and how to apply them to algorithms like linear regression and logistic regression. It is an ideal book for picking up coding in R.

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Unite.AI

JULY 25, 2023

He continues, “Our research delves into the complexities of continuous learning in these artificial neural networks , and what we found are insights that begin to bridge the gap between how a machine learns and how a human learns.”

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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? Complexity vs.

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Heartbeat

SEPTEMBER 13, 2023

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?

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Unite.AI

MAY 8, 2024

During my school years, I spent a lot of time studying math, probability theory, and statistics, and got an opportunity to play with classical machine learning algorithms such as linear regression and KNN. There, I learned a lot about more advanced machine learning algorithms and built my intuition.

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Mlearning.ai

JULY 23, 2023

In the training phase, a machine learning algorithm is fed a large amount of labeled data — text documents already assigned to specific categories. The algorithm learns from this data, understanding the distinguishing features of each category. Still confused? ELI5 please. This is like the training phase.

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Marktechpost

MAY 8, 2024

DRL models, such as Deep Q-Networks (DQN), estimate optimal action policies by training neural networks to approximate the maximum expected future rewards. Extensions to the base DQN algorithm, like Double Q Learning and Prioritized replay, enhance its performance, offering promising avenues for autonomous driving applications.

Deep Learning 109

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Unite.AI

JANUARY 19, 2024

Traditionally, models for single-view object reconstruction built on convolutional neural networks have shown remarkable performance in reconstruction tasks. Moreover, the performance of modern convolutional neural networks in single-view 3D object reconstruction can be surpassed without explicitly inferring the 3D object structure.

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ODSC - Open Data Science

OCTOBER 13, 2023

Geoffrey Hinton is a computer scientist and cognitive psychologist known for his work with neural networks who spent the better part of a decade working with Google. Geoffrey continued to explain that in his view, most of the advanced AI systems have some understanding and are making decisions based on their own experiences.

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Towards AI

JULY 22, 2023

Now Algorithms know what they are doing and why! So, don’t worry, this is where Explainable AI, also known as XAI, comes in. Explainable AI can join the watch party and also assist you understand why the algorithm thinks you’d appreciate that criminal thriller or rom-com you’ve never heard of. SOURCE: [link] A.

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Towards AI

FEBRUARY 16, 2024

Machine learning algorithms, especially neural networks, are effective at identifying complex patterns in chemical data, which can lead to new insights and speed up processes that were previously dependent on traditional, more time-consuming methods.

Machine Learning 90

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Mlearning.ai

FEBRUARY 24, 2023

In this blog post, we will explain what PCA is and how it works and present five examples of PCA analysis. This is done by finding the directions (the principal components) that explain the most variance in the data set. The eigenvalues represent the amount of variance explained by the corresponding eigenvector. What is PCA?

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Towards AI

NOVEMBER 19, 2023

Photo by Pietro Jeng on Unsplash Deep learning is a type of machine learning that utilizes layered neural networks to help computers learn from large amounts of data in an automated way, much like humans do. Weights and biases determine how strongly inputs influence the network’s predictions.

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NYU Center for Data Science

NOVEMBER 3, 2023

Self-supervised learning (SSL) has emerged as a powerful technique for training deep neural networks without extensive labeled data. In an interview, Shwartz-Ziv explains his motivation: “I truly believe SSL is the way to make better algorithms. The former was recently accepted to ​​NeurIPS, and the latter to JMLR.

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Unite.AI

NOVEMBER 14, 2023

The Challenges of Current AI Hardware The rapid advancements in AI have ushered in complex algorithms and models, demanding an unprecedented level of computational power. She explains, “The brain-inspired codes of the AI revolution are largely being run on conventional silicon computer architectures which were not designed for it.”

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Towards AI

AUGUST 11, 2023

Synthetic data is data that is not genuine but is manufactured by algorithms when actual data is unavailable. Did you say the algorithm? What exactly is the algorithm? IMAGE SOURCE: [link] GANs are made up of two major neural networks: THE GENERATOR 2. No concerns if the answer is no. what is the procedure?

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