Big Data Archive - Rock the Prototype - Softwareentwicklung & Prototyping

Artificial intelligence: How does Chat GPT work?

Sascha Block2024-04-12T09:18:22+02:00Categories: Artificial intelligence (AI)|Tags: AI, AI, AI models, Artificial intelligence, Artificial intelligence, Big Data, BigData, Chat GPT, ChatGPT, Data Science, DeepLearning, Distortions, Embeddings, FineTuning, GPT, Hallucinations, machine learning, Machine learning, neural networks, neural networks, NLP, PositionalEncoding, PreTraining, Prototyping, SelfAttention, Software development, Transformer|

Artificial intelligence: How does Chat GPT work? I unravel the threads of semantic probabilities and reveal how AI technology can come so incredibly close to our human understanding. Join me on an exciting journey through the neural landscape of GPT and expand your boundaries between human creativity and machine learning... ✅ What exactly is GPT? ✅ How does a Generative Pretrained Transformer (GPT) work? ✅ Chat GPT - facts about this fascinating AI technology ✅ How does GPT work and why is this AI model so groundbreakingly revolutionary? ✅ How is a GPT model actually trained? ✅ The revolutionary difference between GPT AI technology and traditional AI models ✅ The power of combining self-attention and positional encoding ✅ Pre-training - the pre-training process of AI ✅ How does GPT process all this information? ✅ The embeddings strategy of the AI model

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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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IT standards – Why interoperability and security are essential

Sascha Block2024-02-24T18:19:09+01:00Categories: Software development|Tags: Agile methods, AI, API, Big Data, Blockchain, BSI, Cloud computing, Coding Standards, Compliance, Continuous Deployment, Continuous Integration, Cybersecurity, Data format, Data transmission, Databases, DevOps, Digital transformation, Digitization, DIN, eIDAS, Encryption, ETSI, IaaS, IEEE, IETF, Interoperability, IoT, ISO Standards, IT Governance, IT infrastructure, IT Management, IT Operations, IT Service Management, IT Standard, IT standards, ITIL, machine learning, Mobile Development, Network Security, Open Source, PaaS, Privacy, Programming languages, Protocols, Quality assurance, RFC, SaaS, Security, Software Architecture, Software development, Software Testing, Standards, System integration, Technology, User Experience, Virtualization, W3C, Web development|

IT standards are the invisible network that ensures security, enables interoperability and holds our digital landscape together. Without IT standards, we would fall back into an era of digital turbulence in which nothing communicates with each other, security gaps are omnipresent and no progress is possible. Today we dive into the world of IT standards - we explore why they are essential, what makes a good standard and how they shape our digital future.

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Machine Learning

Sascha Block2023-12-10T19:11:37+01:00Tags: Alan Turing, Algorithms, Artificial intelligence, Artificial intelligence programs, artificial neural networks, Automation, Backpropagation, Big Data, Chess program, continuous learning, Convolutional Neural Networks (CNNs), Data, Decision making, Deep learning, Efficiency, Image processing, Logic Theorist, machine learning, Models, natural language processing, Pattern recognition, Perceptron, personalized recommendations, Predictions, Recurrent Neural Networks (RNNs), Turing machine|

Machine learning is a branch of artificial intelligence (AI) that deals with the development of algorithms and models that enable computers to learn from experience and data, recognize patterns and make predictions without being explicitly programmed. The aim of machine learning is to enable computers to learn independently from data and extract information in order to master complex tasks. We explain which concepts are used to do this and how predictions can be made by adapting models to existing data.

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Big Data

Artificial intelligence: How does Chat GPT work?

Deep learning frameworks

IT standards – Why interoperability and security are essential

Machine Learning

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