Number Vector Art Product - Moozonian Omega

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Running on Titan Engine | Model: llama3.2 | GPU Accelerated

GitHub https://github.com/leenasuva/Topic-Analysis---BERT-Tokenizer

leenasuva/Topic-Analysis---BERT-Tokenizer

The problem highlights the use of machine learning algorithms to categorize different comments scraped from an online platform and make relevant predictions about the topics associated with those comments. There are a total of 40 topics to classify these comments. Even though the problem seems like a simple classification problem, as we dive deeper to understand the data, we realize that the real problem asks us to make sense of the comments mentioned in the dataset and then assign categories. Since the number of topics/classes is much greater than any common classification problem, the expected accuracy won’t be too high. These days, Topic Modeling and Classification have received tremendous popularity when analyzing products and services for various brands, during election times to measure popularity, discover public sentiments around multiple issues, etc. Primarily deriving meaningful topics from these comments is incredibly challenging because of variations in language, insertion of emojis, and use of partial and profane comments. It is essential to choose a scheme that translates the comments to word embeddings to calculate some similarity between those comments to assign relevant topics; it is also imperative to translate the context and meaning of those comments and cluster them to relevant topics. There are multiple approaches to Topic Modeling, such as Latent Dirichlet Analysis (LDA) and Probabilistic Latent Semantic Analysis (LSA). These benchmark techniques utilized for such problems seem to provide viable results. The initial approach was to use Tf-Idf and Word2Vec to vectorize the comments and then use state-of-the-art classification techniques to assign topics to these vectors. When utilized, bag-of-Words with Tf-Idf and Word Embedding with Word2Vec would pose a significant hidden problem. The main problem with these approaches is that they treat the exact words with different meanings identically without adding any context to them. For example, the term “bank” in “Peter is fishing near the bank.” and “Two people robbed the state bank on Monday.” would have the same vectors in this representation. This approach would give us misleading results, and therefore, to improve the performance of our prediction mechanisms, it is essential to switch to a process that finds a way to translate the context of the words. Transformers: a reasonably new modeling technique, presented by Google’s research professionals in their seminal paper “Attention is All You Need,” tackles the exact problem. Google’s BERT (Bidirectional Encoder Representations from Transformers) combines ELMO context embedding and several Transformers, plus it’s bidirectional (which was a big novelty for Transformers). The vector assigned to a word using BERT is a function of the entire sentence; therefore, a word can have different vectors based on the context. ELMO is a word embedding technique that utilizes LSTMs to look at each sentence and then assigns those embeddings.

HackerNews https://news.ycombinator.com/item?id=3567380

Star Trek as a purely symbolic artifact of past times

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NPM Registry https://www.npmjs.com/package/react-art

react-art

React ART is a JavaScript library for drawing vector graphics using React. It provides declarative and reactive bindings to the ART library. Using the same declarative API you can render the output to either Canvas, SVG or VML (IE8).

Dev.to https://dev.to/devteam/congrats-to-the-gemma-4-challenge-winners-4fgc

Congrats to the Gemma 4 Challenge Winners!

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NPM Registry https://www.npmjs.com/package/art

art

Cross-browser Vector Graphics

Dev.to https://dev.to/devteam/congrats-to-the-hermes-agent-challenge-winners-3on0

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GitHub https://github.com/ljinstat/Structured_Data_Random_Features_for_Large-Scale_Kernel_Machines

ljinstat/Structured_Data_Random_Features_for_Large-Scale_Kernel_Machines

Kernel machines such as the Support Vector Machine are widely used in solving machine learning problem, since they can approximate any function or decision boundary arbitrary well with enough training data. However, those methods applied on the kernel matrix (Gram matrix) of the data scale poorly with the size of the training dataset. The kernel trick may become intractable to compute as the computation and storage requirements for the kernel trick are exponentially proportional to the number of samples in the dataset. It takes a long time to train a model when training examples have big volume. For some specialized algorithms for linear Support Vector Machines, they operate much more quickly when the dimensionality of data is small because they operate on the covariance matrix rather than the kernel matrix of the training data. This paper we’ve chosen proposes a way to combine the advantages of the linear and nonlinear approaches. This method transformed the training and evaluation of any kernel machine by mapping the input data to a randomized low-dimensional feature space in order to create corresponding opera- tions of a linear machine. Those randomized features are designed to ensure that the inner products of the transformed data are nearly equal to those in the feature space of a user specific shift-invariant kernel. This method gives competitive results with state-of-the-art kernel-based classification and re- gression algorithms. What’s more, random features fix the problem of large scale of training data when computing the kernel matrix. The results have similar or even better testing error.

HackerNews https://news.ycombinator.com/item?id=30728577

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leenasuva/Topic-Analysis---BERT-Tokenizer

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