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Deep Learning News
Medium
4w
35
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How 0.15 BTC Helped Me Move to a Tax-Free Crypto Haven!
- High taxes on crypto gains led me to explore tax-free crypto countries.
- Costs of relocating to tax havens were a barrier until I received 0.15 BTC from https://swapx.one/ using a promo code.
- With the extra funds, I moved to a tax-free crypto country, enjoying a stress-free life.
- If you're tired of losing gains to taxes, use promo code MYBTC24 to get 0.15 BTC and pursue your dream.
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Medium
4w
146
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Untangling Transformers: A Party Guide to NLP’s Core Breakthrough
- Transformers are essential for modern NLP breakthroughs and are important to learn.
- Transformers can be understood using a metaphor of a party where each person represents a word.
- The self-attention mechanism in Transformers helps determine which words to focus on.
- Self-attention takes inputs such as Query, Key, and Value.
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Medium
4w
394
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How to Tackle the Alarming Rise in Pedestrian Fatalities
- Pedestrian fatalities have increased by 83% since 2009, with urban areas seeing a 61% rise in deaths.
- The “Dangerous by Design 2024” report by Smart Growth America emphasizes the need for urgent action.
- Engineering measures like sidewalks, refuge islands, and improved signal timing can significantly reduce pedestrian crashes.
- Community-based initiatives, adopting the Safe System approach, focus on creating environments where safety is prioritized over speed.
- Technology, such as machine learning and video data analysis, enhances our understanding of pedestrian behavior and crash risks, leading to more effective safety measures.
- The integration of public health data with traffic safety measures is one of the most effective approaches to pedestrian safety.
- Community engagement is a powerful tool in the fight for pedestrian safety.
- Experts are working on hybrid models for crash prediction, which offer the potential of technology to transform our approach to safety.
- The success of implemented safety measures underscores the importance of continued research and collaboration among experts, policymakers, and communities.
- Creating safer streets requires a collective effort involving advanced technologies, community engagement, and evidence-based design strategies.
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Marktechpost
4w
359
CelloType: A Transformer-Based AI Framework for Multitask Cell Segmentation and Classification in Spatial Omics
- CelloType is a transformer-based AI framework for multitask cell segmentation and classification in spatial omics data.
- It integrates DINO and MaskDINO modules for object detection, instance segmentation, and classification.
- CelloType demonstrates superior performance on diverse datasets, including multiplexed fluorescence and spatial transcriptomic images.
- It supports multiscale segmentation and achieves high precision in simultaneous segmentation and classification tasks.
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Medium
4w
173
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core AI is boring. it might still be worth it.
- The main problem with LLM is their sophistication.
- LLM models are trained on large volumes of data, making them accurate and versatile.
- Low-cost neural networks are volatile and easily tampered with.
- Dependency on generative AI models has affected the community, leading to synthetic data production.
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Semiengineering
4w
252
Image Credit: Semiengineering
Monitor Etch Defects on Dies in the Outer Regions Of The Wafer Using ISR
- Researchers at Samsung Electronics have developed an imaging spectroscopic reflectometry (ISR) method for detecting defects in the bottom region of high-aspect-ratio nanostructures.
- ISR enables fast and non-destructive imaging of the bottom critical dimension (BCD) of channel holes (CHH) on a chip die of vertical NAND (V-NAND).
- A supervised learning model is built to predict the BCD using hyperspectral imaging and deep learning, showing a high correlation with the actual BCD.
- ISR can optimize the etch process for weak wafer edge regions and detect defective etch equipment.
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Medium
4w
102
Mastering PDF Data Extraction with Python: A Comprehensive Guide for Beginners
- Python’s rich ecosystem of libraries makes it an excellent tool for PDF data extraction.
- Key benefits of using Python for PDF data extraction include flexibility, efficiency, accuracy, cost-effectiveness, and integration with other data analysis tools.
- Python libraries such as PyPDF2, pdfminer.six, tabula-py, and pdfplumber offer various techniques for extracting text and tables from PDFs, handling encrypted or scanned PDFs, and structuring extracted data.
- Advanced topics in PDF data extraction include regex-based extraction, machine learning approaches, and integration with OCR and NLP technologies.
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Hackernoon
4w
350
Image Credit: Hackernoon
Understanding GAN Mode Collapse: Causes and Solutions
- Generative Adversarial Networks (GANs) are a type of deep learning model that has gained a lot of attention in recent years due to their ability to generate realistic images, videos, and other types of data.
- One of the most significant challenges GANs face is mode collapse, where a GAN generates only a limited set of output examples instead of exploring the entire distribution of the training data.
- There are several causes of mode collapse in GANs, including catastrophic forgetting and discriminator overfitting, both of which lead to the generator getting stuck in a particular mode or pattern.
- Catastrophic forgetting refers to the phenomenon in which a model trained on a specific task forgets the knowledge it has gained while learning a new task.
- Discriminator overfitting results in the generator loss vanishing, causing multiple flat regions to emerge, further leading to a decrease in the diversity of generated samples.
- In order to prevent catastrophic forgetting, the model must be trained with multiple tasks simultaneously, while the local maxima should have a wide shape to prevent mode collapse.
- The visualization of the surface of the discriminator shows that the generator produces similar outputs, indicating mode collapse, and the discriminator scores for images change between training steps.
- GANs demonstrate the same catastrophic forgetting tendencies when trained on symmetric 2D datasets, as displayed in the visualizations of the surface of the discriminator.
- To avoid discriminator overfitting and promote diversity in generated samples, local maxima in various regions of the data space should have a wide shape, and the generator should be trained on less specific targets.
- By understanding the causes of mode collapse in GANs, we can allow them to better develop GANs that are capable of generating diverse and high-quality outputs.
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Medium
4w
0
Image Credit: Medium
Build our Diffusion Model From Scratch and Understand the math
- The article describes how to build a diffusion model from scratch and understand the math behind it.
- The diffusion model is helpful in image generation and is based on a posterior distribution that represents the diffusion process.
- The article explains how to derive q(x_t-1|x_t) and q(x_t-1|x_t,x_0) distributions and makes use of the Evidence Lower Bound (ELBO) to maximize the likelihood function.
- The article also explains how to use the reparameterization trick to compute the derivative and perform backpropagation.
- Code snippets are provided to illustrate the steps involved in building the diffusion model, creating the UNet model, and calculating the loss function.
- The author acknowledges the contributions of the original paper authors Jonathan Ho, Ajay Jain, and Pieter Abbeel, and other researchers whose work this paper builds upon.
- The author also acknowledges Lil Log for her helpful blog on the mathematics involved in the diffusion model.
- The article cautions that there may be inaccuracies and encourages feedback and corrections from readers.
- Overall, the article provides a detailed explanation of the mathematics involved in building a diffusion model from scratch.
- The article also provides code snippets to illustrate the various steps involved in building the model and the UNet model.
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Medium
4w
40
Image Credit: Medium
5 Mistakes Every Data Aspirant Makes in the Beginning & How to Avoid Them
- Establishing a career in data science is an exciting yet overwhelming journey, and many people struggle to stay on track, often skipping important steps along the way.
- Beginners in data science often make the mistake of jumping into technicalities without fully understanding foundational knowledge, causing confusion and frustration.
- Neglecting the importance of math and statistics is a common mistake made by beginner data scientists, as these concepts are fundamental to data science.
- To avoid mistakes, it is essential for data aspirants to develop a strong foundation in math and statistics and continuously improve their skills in these areas.
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Medium
4w
151
Season 4 XBANKING Ambassador Program : Unlocking a World of Rewards and Opportunities
- The XBANKING Ambassador Program is back with Season 4, offering increased rewards and opportunities.
- Participants have a chance to win $100,000 in SOL tokens and additional rewards for top performers.
- The program invites individuals to apply, complete challenges, create content, and engage with the XBANKING community.
- Tips for success include being authentic, creating high-quality content, and actively engaging with the community.
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Marktechpost
4w
142
RhoFold+: A Deep Learning Framework for Accurate RNA 3D Structure Prediction from Sequences
- RhoFold+ is an advanced deep learning framework for accurate RNA 3D structure prediction from sequences.
- It leverages an RNA language model pretrained on over 23.7 million sequences and incorporates multiple sequence alignments (MSAs) to address data limitations.
- RhoFold+ integrates a structure prediction module that employs a geometry-aware attention mechanism (IPA) for 3D structure refinement.
- The model outperforms existing methods in accuracy and demonstrates faster prediction times, with high accuracy across multiple validation scenarios.
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Medium
4w
72
Image Credit: Medium
Apple Intelligence: How Apple Uses AI to Revolutionize User Experience
- Apple utilizes AI extensively in its products and services to enhance user experience.
- Siri, Apple's voice assistant, uses AI for intuitive voice commands and personalized suggestions.
- Core ML framework enables developers to integrate machine learning models into apps, providing real-time, personalized experiences.
- Apple incorporates AI in camera technology to optimize photos in real-time and promotes health with AI-driven features in the Health app.
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Medium
4w
418
The Rise of AI Art: A Creative Revolution or Cause for Concern?
- AI art refers to the creation of images, music, or other forms of artistic expression using artificial intelligence algorithms.
- Benefits of AI art include accessibility, speed, new possibilities, and collaboration.
- Controversies surrounding AI art include originality, copyright issues, impact on artists, and ethical dilemmas.
- The future of AI art lies in finding a balance between innovation and ethics, embracing AI as a complementary tool.
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Medium
4w
432
Image Credit: Medium
An Explanation of the Vision Transformer (ViT) Paper
- The Vision Transformer (ViT) paper adapts the transformer architecture used in NLP to process images, treating them as a sequence of smaller, fixed-size patches that are processed through a pure transformer.
- The ViT takes images as input, divides them into small, fixed-size patches, flatens and converts each patch into a numerical representation called a patch embeddings.
- The ViT adds positional embeddings to patch embeddings, to help the model retain spatial structure of the image.
- The ViT appends a special classification token ([CLS]) to the sequence to aggregate information from all patches during processing for image summarization.
- The ViT showed excellent performance on larger datasets than CNNs for scalability, transfer learning, and performance in low-data scenarios. While CNNs, on the other hand, performed better on smaller datasets.
- The authors proposed an optional hybrid architecture that starts with CNN to extract feature maps, which are then treated as input patches for the Vision Transformer.
- ViT outperformed BiT and other state-of-the-art methods in Natural and Structured categories in the VTAB benchmark suite, demonstrating its ability to generalize well across varied datasets.
- The ViT processes images differently from CNNs by learning spatial relationships from scratch and without CNN's inherent assumptions for localized patterns like textures, edges or shapes.
- The authors also explored self-supervised learning applied to ViT, where parts of the input image were hidden, and the model was tasked with reconstructing the missing patches.
- The ViT showed promising scaling efficiency but has not reached its full potential yet and could perform even better with larger datasets.
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