Keras
TensorFlow
PyTorch
Scikit-learn
TFlearn
OpenCV
Clarifai
DeepPy
neo4j
ArangoDB
Redis
OrientDB
MongoDB
Azure Cosmos DB
Apache Cassandra
CouchBase
neo4jKeras might be a bit more popular than neo4j. We know about 35 links to it since March 2021 and only 34 links to neo4j. 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 unchallenged leader in AI development is still Python. And Keras, and robust community support. - Source: dev.to / about 1 month ago
If you need simplicity, Keras is a great high-level API built on top of TensorFlow. It lets you quickly prototype neural networks without worrying about low-level implementations. Keras is perfect for getting those first models up and running—an essential part of the startup hustle. - Source: dev.to / 7 months ago
At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck. - Source: dev.to / 8 months ago
The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models. - Source: dev.to / 12 months ago
As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / about 1 year ago
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 / about 1 month ago
Neo4j is a leading graph database that is easy to use and powerful for knowledge graphs. - Source: dev.to / about 1 month 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 / 10 months ago
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.
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
OrientDB - OrientDB - The World's First Distributed Multi-Model NoSQL Database with a Graph Database Engine.