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Data Scientology

@datascientology

Hot data science related posts every hour. Chat: https://telegram.me/r_channels Contacts: @lgyanf

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Data Scientology
Would you choose to work as NLP research engineer or PhD starting this year? Hi everyone, I recently graduated from college with a couple of co-authored NLP papers (not first author) and will soon start a one-year MSE program at a top-tier university. I’m currently debating between pursuing a career as a Research Engineer (RE) or going for a PhD after my master’s. Given some financial pressure from my family, the idea of becoming a Research Engineer at companies like Google or Anthropic is increasingly appealing. However, I’m uncertain about the career trajectory of an RE in NLP. Specifically, I’m curious about the potential for Research Engineers to transition into roles focused on research science or product development within major tech companies. I would greatly appreciate any insights or advice from those with experience in the field. What does the career path for Research Engineers typically look like? Is there room for growth and movement into other areas within the industry? Thank you in advance! /r/LanguageTechnology https://redd.it/1dv90hv
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Data Scientology
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I work with models /r/deeplearning https://redd.it/1dt7yzm
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Data Scientology
D Simple Questions Thread Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! /r/MachineLearning https://redd.it/1dh9f6b
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From the MachineLearning community on Reddit

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Data Scientology
How Does Alexa Avoid Interrupting Itself When Saying Its Own Name? Hello r/deeplearning community, I've noticed that Alexa doesn't interrupt itself when it says "Alexa," but it does respond when someone else says it. How does it achieve this? Here are a few questions I have: 1. Self-Recognition: How does Alexa distinguish between its own voice and a user's voice saying "Alexa"? 2. Voice Characteristics: What specific features (e.g., pitch, tone) does Alexa analyze to recognize its own TTS voice? 3. Algorithms and Models: What machine learning models or algorithms are used to handle this task effectively? 4. Implementation: Are there any open-source libraries or best practices for developing a similar functionality? Any insights or resources would be greatly appreciated. Thanks! /r/deeplearning https://redd.it/1do9lcs
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From the deeplearning community on Reddit

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Data Scientology
How do cheap CCTV cameras have good object detection and tracking features? Most of them have extremely low power inputs and comes at very cheap prices. How are they able to do the task so well? Any leads on the tech or algos they use will be very helpful. /r/computervision https://redd.it/1dfhkvk
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Data Scientology
Lightning-Fast Text Classification with LLM Embeddings on CPU I'm happy to introduce fastc, a humble Python library designed to make text classification efficient and straightforward, especially in CPU environments. Whether you’re working on sentiment analysis, spam detection, or other text classification tasks, fastc is oriented for small models and avoids fine-tuning, making it perfect for resource-constrained settings. Despite its simple approach, the performance is quite good. Key Features Focused on CPU execution: Use efficient models like deepset/tinyroberta-6l-768d for embedding generation. Cosine Similarity Classification: Instead of fine-tuning, classify texts using cosine similarity between class embedding centroids and text embeddings. Efficient Multi-Classifier Execution: Run multiple classifiers without extra overhead when using the same model for embeddings. Easy Export and Loading with HuggingFace: Models can be easily exported to and loaded from HuggingFace. Unlike with fine-tuning, only one model for embeddings needs to be loaded in memory to serve any number of classifiers. https://github.com/EveripediaNetwork/fastc /r/LanguageTechnology https://redd.it/1d9g5fa
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GitHub - EveripediaNetwork/fastc: Lightning-Fast Text Classification with LLM Embeddings on CPU

Lightning-Fast Text Classification with LLM Embeddings on CPU - EveripediaNetwork/fastc

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Data Scientology
Tiny Time Mixers(TTMs): Powerful Zero/Few-Shot Forecasting Models by IBM 𝐈𝐁𝐌 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 released 𝐓𝐢𝐧𝐲 𝐓𝐢𝐦𝐞 𝐌𝐢𝐱𝐞𝐫𝐬 (𝐓𝐓𝐌):A lightweight, Zero-Shot Forecasting time-series model that even outperforms larger models. And the interesting part - 𝐓𝐓𝐌 does not use Attention or other Transformer-related stuff! You can find an analysis & tutorial of the model here. /r/deeplearning https://redd.it/1d867c2
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Tiny Time Mixers(TTMs): Powerful Zero/Few-Shot Forecasting Models by IBM

A new lightweight open-source foundation model - tutorial included

Data Scientology
Understanding the Receptive Field in CNNs Hey everyone, I just dropped a new video on my YouTube channel all about the receptive field in Convolutional Neural Networks. I animate everything with Manim. Any feedbacks appreciated. :) Here's the link: https://www.youtube.com/watch?v=ip2HYPC\_T9Q In the video, I break down: What the receptive field is and why it matters How it changes as you add more layers to your network The difference between the theoretical and effective receptive fields Tips on calculating and visualizing the receptive field for your own model /r/computervision https://redd.it/1d6irm9
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CNN Receptive Field | Deep Learning Animated

In this video, we explore the critical concept of the receptive field in convolutional neural networks (CNNs). Understanding the receptive field is essential for grasping how CNNs process images and detect patterns. We will explain both the theoretical and effective receptive fields, highlighting how they influence network performance and design. We start by defining the receptive field and its importance in CNNs, demonstrating how it grows with each convolutional layer. We'll use examples to compute the receptive field for different network configurations and show how pooling layers can significantly expand it. Finally, we'll delve into the differences between theoretical and effective receptive fields, providing insights on how CNNs utilize information during training. If you want to dive deeper into the topic of the receptive field, here are some references that you might find useful: -

https://arxiv.org/abs/1701.04128

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https://theaisummer.com/receptive-field/

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https://distill.pub/2019/computing-receptive-fields/

Chapters: 00:00 Intro 01:10 Receptive Field Basics 03:00 Receptive Field Calculation 05:14 Example Network Analysis 06:12 Pooling Layers 07:18 Effective Receptive Field 10:15 Outro This video is animated using Manim, the Python animation library created by Grant Sanderson from @3blue1brown. Remember to like and subscribe to support the channel. #artificialintelligence #animation #deeplearning #ai #convolutionalneuralnetworks #receptivefield #python #tutorial

Data Scientology
Any lessons to be mindful of building a production-level RAG? I will be working on an RAG system as my graduation project. The plan is to use Amazon Bedrock for the infrastructure while I am scraping for relevant data (documents). For those of you who have had experience working with RAG, are there any lessons/mistakes/tips that you could share? Thanks in advance! /r/LanguageTechnology https://redd.it/1d1fyyq
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From the LanguageTechnology community on Reddit

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Data Scientology
Is there a pre-trained vision model that's good for zero-shot clustering? I have a dataset of images from The Simpsons. I have trained a face-detection model for Simpsons characters with good results, and after lots of experimenting, I have written a script that gives relatively accurate binary images of the faces. I will attach a screenshot as an example. I have also tried using cv2.findContours to find the contours in these images and treat them as matrices to compute the difference between 2 faces, with no luck. My end goal is to be able to cluster these faces by character. I know how to train a classifier for this type of thing, but I want to find a method of clustering faces that the model has not seen before. I have tried some more basic ML algorithms without success, and now I think this task may be too complex for that. I am wondering if there is a vision model that could be well-suited for this? Or if anyone has suggestions for other approaches that would be great too. Here is an example of my processed Simpsons faces that I want to cluster: https://preview.redd.it/xpgnocmuxt2d1.png?width=2476&format=png&auto=webp&s=3028610d60ed09978daf4b6394494385988d7e24 I'm still working on isolating the faces of characters with darker skin colors, but I think this should be good enough to start putting together a clustering method. As a side note, I don’t care that much if, for example, images of Bart Simpson from a front angle end up in a different cluster from images of him from a side angle, as it will be easy enough to manually merge these clusters after the fact. /r/computervision https://redd.it/1d1a8rt
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