neo4j
ArangoDB
Redis
OrientDB
MongoDB
Azure Cosmos DB
Apache Cassandra
CouchBase
PyTorch
TensorFlow
Keras
Scikit-learn
NumPy
OpenCV
Pandas
CUDA Toolkit
neo4j PyTorchBased on our record, PyTorch should be more popular than neo4j. It has been mentiond 133 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The key difference lies in the retrieval mechanism. Vector databases focus on semantic similarity by comparing numerical embeddings, while graph databases emphasize relations between entities. Two solutions for graph databases are Neptune from Amazon and Neo4j. In a case where you need a solution that can accommodate both vector and graph, Weaviate fits the bill. - Source: dev.to / 23 days ago
Neo4j is a leading graph database that is easy to use and powerful for knowledge graphs. - Source: dev.to / 25 days ago
Neo4j is one of the most popular graph databases. It offers powerful querying capabilities through its Cypher query language. - Source: dev.to / 3 months ago
Great heads up. I wonder about graph databases. He mentioned and both include the graph use case and I wonder how they compare to . - Source: Hacker News / 5 months ago
The first blog in this series is to install neo4j - desktop version and few plugins which would help us to build an application. I am using Ubuntu 22.04.4 LTS. - Source: dev.to / 9 months ago
To aspiring innovators: Dive into open-source frameworks like OpenCV or PyTorch, experiment with custom object detection models, or contribute to projects tackling bias mitigation in training datasets. Computer vision isn’t just a tool, it’s a bridge between the physical and digital worlds, inviting collaborative solutions to global challenges. The next frontier? Systems that don’t just interpret visuals, but... - Source: dev.to / 6 days ago
With the quick emergence of new frameworks, libraries, and tools, the area of artificial intelligence is always changing. Programming language selection. We're not only discussing current trends; we're also anticipating what AI will require in 2025 and beyond. - Source: dev.to / 19 days ago
Next, we define a training loop that uses our prepared data and optimizes the weights of the model. Here's an example using PyTorch:. - Source: dev.to / about 1 month ago
8. TensorFlow and PyTorch: These frameworks support AI and machine learning integrations, allowing developers to build and deploy intelligent models and workflows. TensorFlow is widely used for deep learning applications, offering pre-trained models and extensive documentation. PyTorch provides flexibility and ease of use, making it ideal for research and experimentation. Both frameworks support neural network... - Source: dev.to / 3 months ago
Frameworks like TensorFlow and PyTorch can help you build and train models for various tasks, such as risk scoring, anomaly detection, and pattern recognition. - Source: dev.to / 3 months ago
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
OrientDB - OrientDB - The World's First Distributed Multi-Model NoSQL Database with a Graph Database Engine.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
