Edge Computing

A D

Artificial intelligence (AI)

Cloud Computing & Cloud Technology

IT infrastructure

Learn programming

Programming Languages & Frameworks

Software Architecture

Software development

Unkategorisiert

AI Frameworks

Sascha Block2023-12-16T10:14:26+01:00Tags: Algorithms, Apache MXNet, Automation, Caffe, Cloud services, Data analysis, Deep learning, E-learning, Edge Computing, Ethics, Fast.ai, Fraud detection, Game development, IoT, IT Security, Keras, machine learning, medical diagnostics, Microsoft Cognitive Toolkit (CNTK), Model management, natural language processing, NLP, OpenAI Gym, Privacy, PyTorch, Robotics, Scikit-learn, TensorFlow, Theano|

AI frameworks are collections of software libraries, tools and guidelines that are specifically designed for the development of artificial intelligence (AI) are designed. Such frameworks provide developers with a structured starting point with a powerful AI environmentin which they can develop and implement algorithms for machine learning (ML), deep learning, natural language processing (NLP) and other AI-related technologies. Top 10 AI frameworks, Types of AI frameworks, Technological aspects of AI frameworks Application areas and use cases of AI frameworks

Jetzt weiterlesen

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

Jetzt weiterlesen

Diese Website verwendet Cookies und Dienste von Drittanbietern. Hier findest Du Deine individuellen Datenschutz-Einstellungen Ok Individuelle Einstellungen

Cookies von Drittanbietern

YouTube
Facebook
Flickr
X