John Snow Labs’ Post

Portuguese clinical NER models provide valuable tools for processing and analyzing Portuguese clinical texts. They assist in automating the extraction of important clinical information, facilitating research, medical documentation, and other applications within the Portuguese healthcare domain. please check the model card: https://lnkd.in/dvpQg237 Healthcare NLP: https://lnkd.in/dPpr_r9B #deeplearning #dl #nlproc #nlp #nlu #AI #apachespark #bigdata #opensournce #opendata #datascience #datascientist #analytics #predictiveanalytics #machinelearning #ml #datamining #textmining #visualization #digitaltransformation #transferlearning #data #Python #NoSQL #IoT #DataEngineer #DataArchitext #lifescience #entityrecognition #OCR #healthcaredatamining

I'm thrilled to share my recent project where I explored Visual Question Answering (VQA), a fascinating field at the intersection of computer vision and natural language processing. This project has deepened my understanding of how Al can bridge the gap between visual and textual information. Visual Question Answering holds immense potential for creating more intuitive and interactive Al systems. #VisualQuestion Answering #VQA #DeepLearning #Al #ComputerVision #NaturalLanguage Processing #Machine Learning #TechInnovation #DataScience #ImageRecognition #NLP #INDIANSERVERS #AIMERS.

Excited to share that I've completed a course on Encoder-Decoder Architecture! This architecture is a game-changer in natural language processing and machine learning, enabling applications like language translation, text summarization, and chatbots. I'm now equipped with the skills to build and deploy my own encoder-decoder models, and I'm looking forward to applying this knowledge in real-world projects. Thanks to Simplilearn for an insightful and engaging learning experience! #EncoderDecoder #NLP #MachineLearning #AI #SkillsAcquired #LearningJourney

Congratulations on obtaining our prestigious Healthcare NLP for Data Scientist certification! Your data science knowledge and hands-on expertise in building state-of-the-art natural language processing solutions are exceptional. Congratulations, Madhav Sigdel, Kaiser Permanente, Prachi Gopalani, and Gaurav Mantri, UnitedHealth Group Learn more about upcoming training at https://lnkd.in/gYjR6ZC #training #nlptraining #webinar #healthcareai #healthcaredataexperts #machinelearning #artificialintelligence #digitaltransformation #NLP #nlu #pythonprogramming

A Brief #History of #GPT-1 #GPT-3 was created by, and is a significant milestone for, #OpenAI, a San Franciscobased pioneer of AI research. #OpenAI’s stated mission is “to ensure that artificial general intelligence benefits all of humanity.” Artificial general intelligence is a type of AI that is not confined to specialized tasks but instead performs well at a variety of tasks, just like humans do. GPT-1 OpenAI presented #GPT-1 in June 2018. The developers’ key finding was that combining the #transformer architecture with #unsupervised pretraining yielded promising results. GPT-1, they write, was fine-tuned for specific tasks to achieve “strong natural language understanding.” GPT-1 served as an important stepping stone toward a language model with general language-based capabilities. It proved that language models can be effectively pre-trained, which could help them generalize well. The architecture could perform various #NLP tasks with very little fine-tuning. GPT-1 used the Book #Corpus dataset, which contains some seven thousand unpublished books, and the decoder part of the transformer with self-attention to train the model. The architecture remained largely the same as in the original transformer. The model had 117 million parameters. #GPT-1 opened avenues for future models, which could unleash this potential better with larger datasets and more parameters. One of its achievements was decent zero-shot performance ability on various #NLP tasks like question answering (Q&A) and sentiment analysis, due to pre-training. #Zero_shot learning is the ability of a model to perform a task without having seen any example of that kind in the past; the model is supposed to understand the task without looking at any examples. #Zero_shot task transfer is a setting in which the model is presented with few to no examples and asked to understand the task based on the examples and an instruction. 😊 #openai #llm #nlp #problem_solving #humanity

The latest lineup of 7 cutting-edge resolver models are designed to enhance clinical entity mapping and coding accuracy. These models leverage advanced natural language processing to seamlessly map medical entities and concepts to standardized codes, facilitating streamlined data analysis and healthcare decision-making. Each model specializes in precise code assignment within specific medical domains, from drug ingredients to veterinary-related entities. Dive into our resolver models and empower your applications with state-of-the-art clinical entity resolution. Try Healthcare NLP: https://lnkd.in/dPpr_r9B Full Release Notes: https://lnkd.in/dSzMkM9H #ClinicalEntityResolution #NLP #HealthcareAI #MedicalCoding #DataAnalysis #HealthcareDecisionMaking #EntityMapping #MedicalNLP #AIinHealthcare #HealthcareTechnology

With Healthcare NLP 5.1.2, we're introducing 3 New Clinical NER Models for Extracting Clinical Entities in the Arabic, Finnish, and Bulgarian Languages 3 new Clinical NER models provide valuable tools for processing and analyzing multi-language clinical texts. They assist in automating the extraction of important clinical information, facilitating research, medical documentation, and other applications within the multi-language healthcare domain. Full release notes: https://lnkd.in/dSzMkM9H Healthcare NLP: https://lnkd.in/dPpr_r9B #deeplearning #dl #nlproc #nlp #nlu #AI #apachespark #bigdata #opensournce #opendata #datascience #datascientist #analytics #predictiveanalytics #machinelearning #ml #datamining #textmining #visualization #digitaltransformation #transferlearning #data #Python #NoSQL #IoT #DataEngineer #DataArchitext #lifescience #entityrecognition #OCR #healthcaredatamining

Training a Binary Classifier with the IMDB Reviews Dataset Pleased to share a visual snippet from my recent lab assignment where I dived into the fascinating realm of word embeddings in Large Language Models (LLM)! Word embeddings transform words into dense vectors, capturing semantic relationships and contextual information. This allows LLM's to understand language nuances and relationships, crucial for tasks like sentiment analysis, which I tackled in this assignment. Let's continue exploring the frontiers of AI together! #NLP #WordEmbedding #LLM #ArtificialIntelligence #MachineLearning #LanguageModels #DeepLearning.AI #Coursera

🚀I'm thrilled to share my recent project where I explored Visual Question Answering (VQA), a fascinating field at the intersection of computer vision and natural language processing. This project involved developing a model capable of understanding and answering questions about images, integrating image analysis with natural language understanding to provide accurate and contextually relevant answers. 🖼️❓ 🔍 How It Works...🤔🤔: 📌 Multimodal Learning:The VQA model combines visual data from images with textual data from questions. Convolutional neural networks (CNNs) process the comprehension. 📌 End-to-End Pipeline: An end-to-end pipeline was designed, from data preprocessing to model deployment, ensuring seamless integration and usability. The VQA model effectively utilizes a hybrid approach, leveraging CNNs and RNNs to address both image and text inputs, trained on the diverse VQA dataset. This project has deepened my understanding of how AI can bridge the gap between visual and textual information. Visual Question Answering holds immense potential for creating more intuitive and interactive AI systems. I'm eager to explore more in this dynamic field and apply these insights to future projects. #VisualQuestionAnswering #VQA #DeepLearning #AI #ComputerVision #NaturalLanguageProcessing #MachineLearning #TechInnovation #DataScience #ImageRecognition #NLP #INDIANSERVERS #AIMERS.

Fine-tuning techniques are essential for optimizing pre-trained language models for specific tasks. In this post, we'll explore powerful methods: Feature Extraction, LoRA ,QLoRA & Prompt Tuning . LoRA Implementation =[https://lnkd.in/gNCwD4-h] QLoRA implementation= [https://lnkd.in/gDMD7RXn] I'd love to hear about your experiences with these techniques, so please share your feedback in the comments! #MachineLearning #NLP #LanguageModels #FineTuning #FeatureExtraction #LoRA #QLoRA #PromptTuning #AI #DeepLearning #AIResearch #DataScience #ModelOptimization #TechInnovation #ArtificialIntelligence #ML #AIcommunity #Research #TechUpdates #ModelTraining