Based on our record, PyTorch seems to be a lot more popular than Deeplearning4j. While we know about 106 links to PyTorch, we've tracked only 4 mentions of Deeplearning4j. 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.
While KotlinDL seems to be a good solution by Jetbrains, I would personally stick to Java frameworks like DL4J for a better community support and likely more features. Source: over 2 years ago
Would recommend taking a look at dl4j: https://deeplearning4j.org. Source: almost 3 years ago
We use DeepLearning4j in this chapter because it is written in Java and easy to use with Clojure. In a later chapter we will use the Clojure library libpython-clj to access other deep learning-based tools like the Hugging Face Transformer models for question answering systems as well as the spaCy Python library for NLP. Source: almost 3 years ago
FastAPI. Or even simpler: DL4J, to be used in Java when we need to communicate with the rest of the applications in real time. Source: about 3 years ago
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more... - Source: dev.to / about 19 hours ago
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch. - Source: dev.to / about 1 month ago
Import torch # we use PyTorch: https://pytorch.org Data = torch.tensor(encode(text), dtype=torch.long) Print(data.shape, data.dtype) Print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this. - Source: dev.to / about 2 months ago
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries. - Source: dev.to / about 1 month ago
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework. - Source: dev.to / about 1 month 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.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Darknet - Darknet is an open source neural network framework written in C and CUDA.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
OpenCV - OpenCV is the world's biggest computer vision library
PyCaret - open source, low-code machine learning library in Python