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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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.

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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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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.,

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Unite.AI

NOVEMBER 8, 2023

One-hot encoding is a process by which categorical variables are converted into a binary vector representation where only one bit is “hot” (set to 1) while all others are “cold” (set to 0). Functionality : Each encoder layer has self-attention mechanisms and feed-forward neural networks.

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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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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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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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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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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.

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Heartbeat

FEBRUARY 21, 2024

Photo by Almos Bechtold on Unsplash Deep learning is a machine learning sub-branch that can automatically learn and understand complex tasks using artificial neural networks. Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns.

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Viso.ai

JANUARY 2, 2024

In the following, we will explore Convolutional Neural Networks (CNNs), a key element in computer vision and image processing. Whether you’re a beginner or an experienced practitioner, this guide will provide insights into the mechanics of artificial neural networks and their applications. Howard et al.

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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.

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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!

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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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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.

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Heartbeat

FEBRUARY 15, 2023

As a machine learning engineer, you start with a labeled data set that has vast amounts of text that have already been categorized. Keras provides a simple API to build neural networks and uses Tensorflow as the backend. However, some design decisions need to be made · How many layers will your network have?

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Heartbeat

JUNE 27, 2023

Photo by Resource Database on Unsplash Introduction Neural networks have been operating on graph data for over a decade now. Neural networks leverage the structure and properties of graph and work in a similar fashion. Graph Neural Networks are a class of artificial neural networks that can be represented as graphs.

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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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Pickl AI

FEBRUARY 12, 2024

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. Explain the bias-variance tradeoff in Machine Learning.

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ODSC - Open Data Science

JULY 26, 2023

This step may include handling missing values, outlier detection, feature scaling, encoding categorical variables, and feature engineering. It is particularly popular for deep learning, a subfield of machine learning that focuses on neural networks with many layers.

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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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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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Marktechpost

JULY 16, 2023

Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Recurrent Neural Networks (RNNs) are some of the models utilized in Generative AI. Their RAIC (Rapid Automatic Image Categorization) technology does not require time-consuming human labeling to build the models.

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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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Bugra Akyildiz

NOVEMBER 26, 2022

TabTransformer uses Transformers to generate robust data representations — embeddings — for categorical variables, or variables that take on a finite set of discrete values, such as months of the year. Spotify explains how they build data infrastructure and ML infrastructure for user forecasting. Object descriptions (e.g.,

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Snorkel AI

AUGUST 16, 2023

Neither exhaustive nor comprehensive, this guide aims to explain the most important aspects of genAI that everyone should know in plain and accessible language. These systems predict numeric or categorical values about particular subjects based on their mathematical understanding of the feature space. It’s mostly math.

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Snorkel AI

AUGUST 16, 2023

Neither exhaustive nor comprehensive, this guide aims to explain the most important aspects of genAI that everyone should know in plain and accessible language. These systems predict numeric or categorical values about particular subjects based on their mathematical understanding of the feature space. It’s mostly math.

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Mlearning.ai

APRIL 17, 2023

Even if the dataset is thoroughly balanced, the accuracy metric alone will not comprehensively explain the model’s performance since it is not based on per-class accuracy. The dataset contains 19 mostly categorical features, an ID column, and a target column indicating whether a customer has churned or not. 1466 Yes 0.71

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Heartbeat

JUNE 28, 2023

Image from: [link] Overall, knowledge distillation is a powerful technique that can be used to reduce the size and complexity of deep neural networks, while maintaining or even improving their performance on a given task. We’re committed to supporting and inspiring developers and engineers from all walks of life.

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Becoming Human

MAY 12, 2023

The 1950s saw the development of neural networks that were trained by using hand-labeled images. Highlighting important ideas, identifying patterns, and explaining difficult passages can be done with line annotations. AI and machine learning applications require image annotation partners to label and categorize images.

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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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Marktechpost

DECEMBER 19, 2023

In tackling the intricate task of predicting brain age, researchers introduce a groundbreaking hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Multilayer Perceptron (MLP) architectures. Importantly, the model’s performance includes R-square results, indicating a robust fit to the data.

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The MLOps Blog

FEBRUARY 22, 2024

Methods for continual learning can be categorized as regularization-based, architectural, and memory-based, each with specific advantages and drawbacks. Architectural methods like Progressive Neural Networks could be a good choice if you prioritize preserving past data over learning new concepts.

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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.

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Heartbeat

FEBRUARY 8, 2023

Supported tools include a Name finder, Tokenizer, Document categorization, POS tagger, Parser, Chunker, and Sentence detector. Other languages that can also be used for NLP are: C++: This language, which is an extension of the C programming language, can be used to build neural networks.

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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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