BERT Archive - Rock the Prototype - Softwareentwicklung & Prototyping

AI language models

Sascha Block2025-02-05T13:55:57+01:00Tags: AGI, AI ethics, AI for companies, AI Governance, AI in everyday life, AI in research, AI language models, AI Research, AI safety, AI-supported creativity, Alexa, Algorithmic Bias, Artificial intelligence, Attention mechanism, Autoencoding, Autoregressive models, BERT, Bias in AI, bidirectional models, Chatbots, CLIP, Cloud computing, Computational linguistics, Computing power, Conversational AI, DALL-E, Data ethics, Deep learning, DeepMind, digital assistants, Embeddings, Energy-efficient AI, Explainability, Fairness in AI, Few-Shot Learning, Few-Shot Prompting, Fine tuning, GDPR, generative AI, generative language models, GitHub Copilot, GloVe, Google AI, Google Assistant, GPT, GPT-2, GPT-3, GPT-4, GPUs, Hallucinations in AI, Hugging Face, Information retrieval, Interpretability, Knowledge Graphs, Language processing, Large Language Models, LLM, LSTM, machine learning, machine translation, Masked Language Modeling, Meta AI, Model compression, multimodal AI, multimodal models, Named Entity Recognition, natural language processing, neural networks, NLP, Open source AI, OpenAI, OpenAI Codex, Perplexity, Privacy, Prompt Engineering, Prompt Tuning, Regulation, Reinforcement Learning, Responsible AI, Retrieval-Augmented Generation, RNN, Scalability, Self-Attention, Sentiment analysis, Siri, Supervised Learning, synthetic data, T5, Text classification, Text generation, Tokenizer, TPUs, Training data, Transfer Learning, Transformer, Unsupervised Learning, Voice assistants, Word2Vec, Zero-shot learning|

AI language models - what's behind them? Artificial intelligence is changing the way we interact with machines. Language models enable machines not only to understand language, but also to generate meaningful, human-like responses. Find out more now...

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Deep learning frameworks

Sascha Block2025-02-01T17:01:23+01:00Tags: Activation Functions, AI benchmarking, AI deployment in the cloud, AI ethics, AI for autonomous systems, AI for companies, AI for image recognition, AI for language models, AI for recommendation systems, AI for robotics, AI for word processing, AI Frameworks, AI in medicine, AI in production, AI in the automotive industry, AI in the financial sector, AI infrastructure, AI optimization, AI Performance, AI Pipeline, AI Research, AI toolkits, AI training, Algorithmic fairness, Algorithmic optimization, Artificial intelligence, artificial neural networks, Autoencoder, AutoML, Backpropagation, Batch Normalization, BERT, Bias in AI, Big Data, Caffe, Cloud-based AI, Clustering, CNTK, Computer Vision, Convolutional Neural Networks, Cross Validation, Data Augmentation, Data Parallelism, Data Science, Deep learning, Deep reinforcement learning, DeepMind, Deployment of AI models, Deployment strategies, Dropout, Edge AI, Edge Computing, Explainable AI, Feature engineering, Federated Learning, Few-Shot Learning, FPGAs, Generative Adversarial Networks, GPT, GPU acceleration, Gradient Descent, Graph Neural Networks, Hardware acceleration for AI, High-Performance Computing, Hyperparameter optimization, Hyperparameter tuning, Image classification, Industrial applications for AI, Inference, Interpretability of models, JAX, Keras, Language processing, Loss function, LSTM, machine learning, Machine Learning Operations, MobileNet, Model compatibility, Model compression, Model management, Model Parallelism, Model Quantization, Model Serving, Model validation, MXNet, natural language processing, neural networks, neuronal architecture, NVIDIA CUDA, Object recognition, ONNX, Open Source, OpenAI, OpenAI Gym, Optimization, PyTorch, Recurrent Neural Networks, Regularization, Reinforcement Learning, ResNet, Scalability, TensorFlow, Theano, TPUs, Training of models, Training Pipelines, Transfer Learning, Transformer models, VGG, Zero-shot learning|

What are deep learning frameworks? Deep learning frameworks are

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Natural Language Processing (NLP)

Sascha Block2025-01-24T15:57:08+01:00Tags: AllenNLP, API integration, Artificial intelligence, Attention mechanism, Azure Text Analytics, BERT, Bias reduction, bidirectional models, Chatbot development, Computational linguistics, computational semantics, Constituent parsing, Context-aware models, Contextual embeddings, Conversational AI, Corpus analysis, Data preprocessing, Deep learning, Dependency parsing, Dialog systems, ethical AI, Fairness in AI, Gated Recurrent Unit (GRU), GloVe, Google Cloud NLP, GPT, Handling sensitive data, Hugging Face, IBM Watson, Information retrieval, Language comprehension, Language models, Lemmatization, Long Short-Term Memory (LSTM), machine learning, morphological analysis, Multilingual NLP, Named Entity Recognition (NER), natural language processing, neural networks, NLP, NLTK, NLU (Natural Language Understanding), OpenNLP, Parsing algorithms, Privacy, PyTorch, Question answer, recurrent neural networks (RNN), Self-Attention, semantic analysis, Sentiment analysis, Sentiment classification, Sequence modeling, spaCy, Speech recognition, Speech-to-text, Stemming, syntactic analysis, TensorFlow, Text analysis, Text classification, Text generation, Text mining, Text parsing, Text summary, Text-to-speech, Tokenization, Transformer models, virtual assistants, Word embeddings, Word type tagging, Word2Vec|

Natural Language Processing (NLP) - a highly specialized subfield of artificial intelligence (AI) How do machines understand human language? Natural Language Processing is the key to bridging the gap between humans and machines

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BERT

AI language models

Deep learning frameworks

Natural Language Processing (NLP)

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