ML Review

MARCH 31, 2019

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% Add a little bit of carefully constructed noise and the same neural network now thinks this is an image of a gibbon with 99.3%

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Heartbeat

AUGUST 21, 2023

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs are artificial neural networks built to handle data having a grid-like architecture, such as photos or movies.

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Snorkel AI

JULY 2, 2024

Snorkel AI has thoroughly explained weak supervision elsewhere, but I will explain the concept briefly here. To identify the overlap densities within datasets, we developed an overlap detection algorithm leveraging the simplicity bias in neural network learning. I have also summarized the presentation’s main points here.

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Categorization Neural Network Explainability Large Language Models 52

Snorkel AI

JULY 2, 2024

Snorkel AI has thoroughly explained weak supervision elsewhere, but I will explain the concept briefly here. To identify the overlap densities within datasets, we developed an overlap detection algorithm leveraging the simplicity bias in neural network learning. I have also summarized the presentation’s main points here.

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Categorization Neural Network Explainability Large Language Models 52

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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Marktechpost

MAY 5, 2024

Existing surveys detail a range of techniques utilized in Explainable AI analyses and their applications within NLP. The LM interpretability approaches discussed are categorized based on two dimensions: localizing inputs or model components for predictions and decoding information within learned representations.

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Heartbeat

MARCH 1, 2023

Tracking your image classification experiments with Comet ML Photo from nmedia on Shutterstock.com Introduction Image classification is a task that involves training a neural network to recognize and classify items in images. A dataset of labeled images is used to train the network, with each image given a particular class or label.

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Marktechpost

OCTOBER 15, 2024

XAI, or Explainable AI, brings about a paradigm shift in neural networks that emphasizes the need to explain the decision-making processes of neural networks, which are well-known black boxes. Quanda differs from its contemporaries, like Captum, TransformerLens, Alibi Explain, etc.,

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IBM Journey to AI blog

AUGUST 1, 2023

A foundation model is built on a neural network model architecture to process information much like the human brain does. Foundation models: The driving force behind generative AI Also known as a transformer, a foundation model is an AI algorithm trained on vast amounts of broad data.

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IBM Journey to AI blog

APRIL 18, 2024

Most experts categorize it as a powerful, but narrow AI model. ” AGI analyzes relevant code, generates a draft function with comments explaining its logic and allows the programmer to review, optimize and integrate it. Some, like Goertzel and Pennachin , suggest that AGI would possess self-understanding and self-control.

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Towards AI

FEBRUARY 20, 2024

If you want an overview of the Machine Learning Process, it can be categorized into 3 wide buckets: Collection of Data: Collection of Relevant data is key for building a Machine learning model. Linear Regression Decision Trees Support Vector Machines Neural Networks Clustering Algorithms (e.g., How Machine Learning Works?

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Mlearning.ai

JANUARY 26, 2023

When it comes to implementing any ML model, the most difficult question asked is how do you explain it. Suppose, you are a data scientist working closely with stakeholders or customers, even explaining the model performance and feature selection of a Deep learning model is quite a task. How can we explain it in simple terms?

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ODSC - Open Data Science

OCTOBER 24, 2023

It easily handles a mix of categorical, ordinal, and continuous features. Yet, I haven’t seen a practical implementation tested on real data in dimensions higher than 3, combining both numerical and categorical features. All categorical features are jointly encoded using an efficient scheme (“smart encoding”).

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Marktechpost

OCTOBER 18, 2023

After these accomplishments, other research has examined the advantages of pre-training large molecular graph neural networks for low-data molecular modeling. They comprise jobs at the graph level and node level, as well as quantum, chemical, and biological aspects, categorical and continuous data points.

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Mlearning.ai

JULY 23, 2023

Text Classification : Categorizing text into predefined categories based on its content. It is used to automatically detect and categorize posts or comments into various groups such as ‘offensive’, ‘non-offensive’, ‘spam’, ‘promotional’, and others. It’s ‘trained’ on labeled data and then used to categorize new, unseen data.

Explainability 52

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Artificial Corner

JULY 30, 2023

In this world of complex terminologies, someone who wants to explain Large Language Models (LLMs) to some non-tech guy is a difficult task. So that’s why I tried in this article to explain LLM in simple or to say general language. Machine translation, summarization, ticket categorization, and spell-checking are among the examples.

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PyImageSearch

NOVEMBER 13, 2023

For example, image classification, image search engines (also known as content-based image retrieval, or CBIR), simultaneous localization and mapping (SLAM), and image segmentation, to name a few, have all been changed since the latest resurgence in neural networks and deep learning. Object detection is no different. 2015 ; He et al.,

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Towards AI

JUNE 4, 2024

Google Deepmind is also working on AI weather models, including graphcast based on graph neural networks last year. This article explains linear regression in the context of spatial analysis and shows a practical example of its use in GIS. higher resolution images than current numerical models 1,000x faster. Why should you care?

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AWS Machine Learning Blog

JANUARY 17, 2023

In addition to textual inputs, this model uses traditional structured data inputs such as numerical and categorical fields. We show you how to train, deploy and use a churn prediction model that has processed numerical, categorical, and textual features to make its prediction. For more details, refer to the GitHub repo.

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PyImageSearch

FEBRUARY 6, 2023

This is the 3rd lesson in our 4-part series on OAK 101 : Introduction to OpenCV AI Kit (OAK) OAK-D: Understanding and Running Neural Network Inference with DepthAI API Training a Custom Image Classification Network for OAK-D (today’s tutorial) OAK 101: Part 4 To learn how to train an image classification network for OAK-D, just keep reading.

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IBM Journey to AI blog

OCTOBER 16, 2023

For instance, email management automation tools such as Levity use ML to identify and categorize emails as they come in using text classification algorithms. This allows you to craft personalized responses based on category, which saves time, and such customization can help improve your conversion rate.

Machine Learning 131

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Pickl AI

AUGUST 28, 2024

Unlike regression, which deals with continuous output variables, classification involves predicting categorical output variables. Examples include Logistic Regression, Support Vector Machines (SVM), Decision Trees, and Artificial Neural Networks. They are easy to interpret and can handle both categorical and numerical data.

Machine Learning 52

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Heartbeat

NOVEMBER 14, 2023

Integrating XGboost with Convolutional Neural Networks Photo by Alexander Grey on Unsplash XGBoost is a powerful library that performs gradient boosting. One robust use case for XGBoost is integrating it with neural networks to perform a given task. It was envisioned by Thongsuwan et al.,

Deep Learning 52

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Towards AI

JULY 22, 2023

We’ll start with a simple explainer of how Machine Learning models work — let’s say you want to predict how late your upcoming flight’s arrival time will be. Broadly, data sources can be broadly categorized into: Open Source Data: These are high volume data sources that are typically available for commercial purposes, including LLM training.

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Pickl AI

SEPTEMBER 17, 2024

Deep Learning (DL) is a more advanced technique within Machine Learning that uses artificial neural networks with multiple layers to learn from and make predictions based on data. Explain The Concept of Supervised and Unsupervised Learning. Explain The Concept of Overfitting and Underfitting In Machine Learning Models.

Artificial Intelligence 52

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Marktechpost

JULY 4, 2023

Natural language processing (NLP) activities, including speech-to-text, sentiment analysis, text summarization, spell-checking, token categorization, etc., Unigrams, N-grams, exponential, and neural networks are valid forms for the Language Model. rely on Language Models as their foundation.

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Large Language Models BERT Natural Language Processing Categorization 105

Viso.ai

MAY 24, 2024

This technique combines learning capabilities and logical reasoning from neural networks and symbolic AI. This ability to trace outputs to the rules and knowledge within the program makes the symbolic AI model highly interpretable and explainable. Extracting information from the patterns learned by neural networks.

Neural Network 52

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Heartbeat

OCTOBER 11, 2023

Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach. Interpretability and Explainability: Deep neural networks are one machine learning model that can be very complex and difficult to assess. What is Deep Learning?

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Heartbeat

AUGUST 29, 2023

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. Deep learning models use artificial neural networks to learn from data.

Software Development 52

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Heartbeat

FEBRUARY 23, 2023

Curious how to design your deep neural networks’ depth and shape? But instead of just tuning specific hyperparameters, we can also decide how our network is shaped. But instead of just tuning specific hyperparameters, we can also decide how our network is shaped. Well, here’s the guide for you.

Neural Network 52

Neural Network Categorization Data Science Deep Learning 52

PyImageSearch

SEPTEMBER 25, 2023

In this lesson, we will answer this question by explaining the machine learning behind YouTube video recommendations. The overall system ( Figure 2 ) consists of two neural networks for candidate generation and ranking. Hence, the ranking network takes in many more features describing the video (e.g., RecSys’16 ).

Computer Vision 114

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AWS Machine Learning Blog

JANUARY 30, 2023

In recent years, distributed training has been a popular mechanism in training deep neural networks for use cases such as large language models (LLMs), image generation and classification, and text generation tasks using frameworks like PyTorch, TensorFlow, and MXNet. The data statistics are shown in the table.

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Viso.ai

DECEMBER 18, 2023

TensorFlow is an open-source software library for AI and machine learning with deep neural networks. TensorFlow Lite also optimizes the trained model using quantization techniques (discussed later in this article), which consequently reduces the necessary memory usage as well as the computational cost of utilizing neural networks.

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AWS Machine Learning Blog

FEBRUARY 28, 2023

Graph neural networks (GNNs) have shown great promise in tackling fraud detection problems, outperforming popular supervised learning methods like gradient-boosted decision trees or fully connected feed-forward networks on benchmarking datasets. The RGCN model learns an embedding for each non-target node type.

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Mlearning.ai

FEBRUARY 9, 2023

I managed to happily run two of the Perceptron Neural Network models correctly, but with the syntax reproducability problems, hyperparameter testing was a long process. for only 3 features; adding 4 additional categorical features should increase training accuracy, as it did. rmd file to see the html output. Happy Practicing!

Neural Network 52

Neural Network Data Analysis Machine Learning Categorization 52

Heartbeat

MARCH 6, 2023

Keras provides a simple API to build neural networks and uses Tensorflow as the backend. Before feeding these values to a neural network, the pixel values need to range from 0 to 1. Training the Neural Network In this section, you will build a neural network that requires configuring the layers of the model.

ML 52

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Viso.ai

MARCH 26, 2024

Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. Furthermore, attention mechanisms work to enhance the explainability or interpretability of AI models. Vaswani et al.

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