Let's learn about Transformers via these 61 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology.In AI, transformers are a novel neural network architecture primarily used for processing sequential data, particularly prominent in natural language processing (NLP). They have revolutionized AI models like BERT and GPT, enabling unprecedented advancements in understanding and generating human language.1. Decoding Transformers' Superiority over RNNs in NLP TasksExplore the intriguing journey from Recurrent Neural Networks (RNNs) to Transformers in the world of Natural Language Processing in our latest piece: 'The Trans2. A Machine Learning Text Classification Case Study with a Product-driven TwistText classification case study with a product-driven twist. We're building various models (logreg, RNN, transformers) and compare their quality and performance3. Using BERT Transformer with SpaCy3 to Train a Relation Extraction ModelA step-by-step guide on how to train a relation extraction classifier using Transformer and spaCy3.4. Essential Guide to Transformer Models in Machine LearningTransformer models have become the defacto standard for NLP tasks. As an example, I’m sure you’ve already seen the awesome GPT3 Transformer demos and articles detailing how much time and money it took to train. 5. Text Classification With Zero Shot LearningZero-shot text classification using trnasformers and TARSclassifier.6. Train a NER Transformer Model with Just a Few Lines of Code via spaCy 3 Transformer models have become by far the state of the art in NLP technology, with applications ranging from NER, Text Classification, and Question Answering7. Semantic Search Queries Return More Informed ResultsIn this article, you will learn what a vector search engine is and how you can use Weaviate with your own data in 5 minutes.8. Scale Vision Transformers (ViT) Beyond Hugging FaceSpeed up state-of-the-art ViT models in Hugging Face 🤗 up to 2300% (25x times faster ) with Databricks, Nvidia, and Spark NLP 🚀9. Open Source Explained by 1980s CartoonsReader, I have a confession. I’m really into bad 1980's cartoons. You know, the ones that are little more than animated toy commercials? I’ve learned so many life lessons from those hours in front of a flickering analog TV.10. Positional Embedding: The Secret behind the Accuracy of Transformer Neural NetworksAn article explaining the intuition behind the “positional embedding” in transformer models from the renowned research paper - “Attention Is All You Need”.11. 'El transformador ilustrado' una traducción al español12. How to Get Started With EmbeddingsGetting started with embeddings using open-source tools.13. Deploying Transformers in Production: Simpler Than You ThinkA beginner-friendly guide showing developers how to easily deploy transformer models (like DistilBERT) using Docker, Flask, Gunicorn, and AWS SageMaker. Include14. Inside Transformers: The Hidden Tech Behind LLM's and Chatbots like ChatGPT Transformers explained: The secret technology behind ChatGPT and how it’s reshaping AI chatbots worldwide.15. MusicGen from Meta AI — Understanding Model Architecture, Vector Quantization and Model ConditioningWish to generate high quality, realistic, controllable music using AI? Meta's new MusicGen is the answer. 16. Mamba Architecture: What Is It and Can It Beat Transformers?Explore Mamba, an innovative architecture surpassing Transformers in efficiency for long sequences, promising advancements in AI with its flexible design.17. From Crappy Autocomplete to ChatGPT: The Evolution of Language ModelsAn easy explanation of how self-attention works and a brief look at the evolution of large language models.18. Sequence Length Limitation in Transformer Models: How Do We Overcome Memory Constraints?Transformers are limited by sequence length due to quadratic scaling. Explore solutions like sparse attention, low-rank approximations, and spectral methods.19. A Beginner Guide to Incorporating Tabular Data via HuggingFace TransformersTransformer-based models are a game-changer when it comes to using unstructured text data. As of September 2020, the top-performing models in the General Language Understanding Evaluation (GLUE) benchmark are all BERT transformer-based models. At Georgian, we often encounter scenarios where we have supporting tabular feature information and unstructured text data. We found that by using the tabular data in these models, we could further improve performance, so we set out to build a toolkit that makes it easier for others to do the same.20. A Digestible High-Level Overview of CPU & GPU CoresCPU & GPU - The Basics - A digestible high-level overview of what happens in The Die21. The Dawn of the Transformer Neural NetworksWhy are GPT-3 and all the other transformer models so exciting? Let's find out!22. Combining CNNs, GANs, and Transformers to Outperform Image-GPTResearchers combined the efficiency of GANs and convolutional approaches with the expressivity of transformers to outperform Open AI's Image-GPT23. Introduction to Clustering in Machine Learning: Types, Algorithms, and ApplicationsLearn the world of clustering in machine learning: explore types, algorithms, and applications for extracting insights from unlabeled data.24. The Simplest Way to Understand How LLMs Actually Work!The magic of transformers lies in their attention mechanism. But what does that actually mean?25. This Deep-learning Approach Can Help Double Your Gains in Crypto InvestmentsThis report presents a novel approach to cryptocurrency trading using a Transformer-based Deep Reinforcement Learning (DRL) agent. 26. Unleashing Transformers: Overcoming RNN ConventionsHow Transformers addressed the challenges posed by RNNs.27. The Translation Revolution: How LLMs Are Cutting 90% of Translation CostsLLM-based translation services are revolutionizing the industry, offering cost savings of up to 90% while delivering superior accuracy and nuanced results. 28. Cocktail Alchemy: Creating New Recipes With TransformersBuild a transformer model with natural language processing to create new cocktail recipes from a cocktail database.29. This Open-Source AI Reads the Earth Like ChatGPT Reads TextHow a rocket scientist turned entrepreneur created the “ChatGPT for Earth data” using transformers and satellite imagery30. The Impact of AI Transformers on the Customer ExperienceI have spent the last few weeks understanding the impact of a great revolution in the world of Artificial Intelligence and NLP on the customer experience. Not from a purely technical point of view, but trying to estimate the competitive advantage that this new approach can generate. We are facing yet another disruptive innovation, and it can bring significant advantages, let's try to find out which ones.31. The Art of Transformers: How AI Intuitively Summarizes Business Papers Using NLP“I don’t want a full paper, just give me a concise summary of it”. Who hasn't found themselves in this situation, at least once? Sound familiar?32. Using Sparse R-CNN As A Detection ModelToday, we are going to discuss a method proposed by researchers from four institutions one of which is ByteDance AI Lab (known for their TikTok App).33. Exploring T5 Model : Text to Text Transfer Transformer Model Recent years have seen a plethora of pre-trained models such as ULMFiT, BERT, GPT, etc being open-sourced to the NLP community. Given the size of such humungous models, it's nearly impossible to train such networks from scratch considering the amount of data and computation that is required. This is where a new learning paradigm "Transfer Learning" kicks in. Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. 34. See, Track, Describe: How OW‑VISCap Lets AI Tell the Story Behind Every FrameThis article introduces OW‑VISCap, a unified framework for open‑world video instance segmentation and object‑centric captioning.35. Teaching AI to See and Speak: Inside the OW‑VISCap ApproachThis article outlines the OW‑VISCap framework, which jointly detects, segments, and captions both seen and unseen objects within a video.36. The AI Industry's Obsession With Transformers Might Finally Be WaningNewer versions such as Mamba of State Space Models (SSMs) appear to be winning some favor.37. General Model Serving Systems and Memory Optimizations ExplainedModel serving has been an active area of research in recent years, with numerous systems proposed to tackle diverse aspects of deep learning model deployment.38. Hawk and Griffin: Efficient RNN Models Redefining AI PerformanceThis research introduces Hawk and Griffin models, efficient RNN alternatives to Transformers, with reduced latency and strong long-sequence performance.39. Bits of Thought: Yelp Content As EmbeddingsYou want an intro to embeddings, learn about the cool things done at Yelp, and later play with the off-the-shelf models available on Hugging Face? Let's dive in40. Real-World Evaluation of Anomaly Detection Using Amazon ReviewsExplore a comprehensive evaluation of anomaly detection methods using Amazon review data.41. Griffin Model: Advancing Copying and Retrieval in AI TasksThis research shows Griffin excels in copying and retrieval tasks, outperforming Hawk and Transformers in extrapolation for longer sequences.42. Technical Setup for RECKONING: Inner Loop Gradient Steps, Learning Rates, and Hardware SpecificationThis article outlines the implementation details for RECKONING, which uses a GPT-2-base model and runs on NVIDIA A100 GPUs.43. How An AI Understands Scenes: Panoptic Scene Graph Generation.Explore the groundbreaking AI technology of Panoptic Scene Graph Generation with Transformers for a deeper understanding of visual scenes.44. Optimizing Language Models: Decoding Griffin’s Local Attention and Memory EfficiencyExplores how Griffin’s local attention and recurrent layers outperform traditional Transformers, improving language modeling at scale and faster inference.45. Explainable AI in Action: Generating Insights from Review AnomaliesExplore a cutting-edge pipeline for detecting and explaining anomalous reviews in online platforms like Amazon. 46. RNNs vs. Transformers: Innovations in Scalability and EfficiencyThis research explores scalable RNN and SSM innovations, comparing their efficiency and performance to Transformers and linear attention techniques.47. Advancements in Anomaly DetectionExplore recent advancements in anomaly detection for texts, focusing on techniques like NLP and machine learning classifiers.[48. Bid Shading Fundamentals -Machine Learning Approaches and Advanced Optimization (Part 2)](https://hackernoon.com/bid-shading-fundamentals-machine-learning-approaches-and-advanced-optimization-part-2)Machine learning approaches to bid shading represent the evolutionary leap from rule-based algorithms to adaptive, data-driven optimization systems.49. How Griffin’s Local Attention Window Beats Global Transformers at Their Own GameExplores how Griffin’s local attention and recurrent layers outperform traditional Transformers, improving language modeling at scale and faster inference.50. Recurrent Models Scale as Efficiently as TransformersThis research compares MQA Transformers, Hawk, and Griffin models, highlighting Griffin's hybrid approach combining recurrent blocks with local attention.51. Effective Anomaly Detection Pipeline for Amazon Reviews: References & AppendixExplore findings from a study on an anomaly detection pipeline for Amazon reviews using MPNet embeddings.52. Hawk and Griffin Models: Superior Latency and Throughput in AI InferenceThis research shows Hawk and Griffin outperform MQA Transformers in latency and throughput, excelling in long-sequence and large-batch inference.53. Recurrent Models: Decoding Faster with Lower Latency and Higher ThroughputThis research shows recurrent models excel in decoding, offering lower latency and higher throughput than Transformers, especially for long sequences.54. You Should Stop Fine-Tuning Blindly: What to Do InsteadAn expert, workflow-first guide to model fine-tuning: the real taxonomy, proven pipelines, hardware math, and the traps that quietly ruin results.55. Effective Anomaly Detection Pipeline for Amazon Reviews: Insights and Future DirectionsExplore findings from a study on an anomaly detection pipeline for Amazon reviews using MPNet embeddings.56. Griffin Models: Outperforming Transformers with Scalable AI InnovationThis research shows Griffin models outperform Transformers in validation loss and scaling efficiency, following Chinchilla scaling laws up to 14B parameters.57. Explained Anomaly Detection in Text Reviews: Can Subjective Scenarios Be Correctly Evaluated?Discover a robust pipeline for detecting and explaining anomalous reviews in online platforms like Amazon.58. What You Need to Know About Tabular Data as a ChallengeDespite AI/ML research focusing on unstructured data, tabular data remains the primary area of time and financial investment in the Data Integration world.59. Recurrent Models: Enhancing Latency and Throughput EfficiencyThis research shows recurrent models reduce cache size, improving latency and throughput over Transformers for long sequences.60. Training speed on longer sequencesThe research paper compares training speeds across different model sizes and sequence lengths to conclude the computational advantages of Hawk and Griffin.61. Meet The AI Tag-Team Method That Reduces Latency in Your Model's ResponseSpeculative decoding is an advanced AI inference technique that is gaining traction in natural language processing (NLP) and other sequence generation tasks.Thank you for checking out the 61 most read blog posts about Transformers on HackerNoon.Visit the /Learn Repo to find the most read blog posts about any technology.