TensorFlow Powerful deep learning
TensorFlow is an open source framework from Google that was developed specifically for machine learning and deep learning. It provides a comprehensive and flexible platform for the development and deployment of machine learning models. TensorFlow supports both simple and complex neural networks and is known for its scalability and efficiency.
With TensorFlow, models can be developed and deployed on various platforms such as desktop, mobile, web and cloud. It provides robust tools for data preparation, modelling, training and deployment, including support for GPUs and TPUs to accelerate processing.
Keras An easy introduction to deep learning
Keras is a user-friendly, highly modular deep learning framework that is perfect for beginners. It runs on TensorFlow, Theano and CNTK and enables the quick and easy development of prototypes. Keras offers an intuitive API that simplifies the creation and training of neural networks.
It supports common layers such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) as well as utility layers such as dropout, batch normalisation and pooling. Keras is also known for its flexibility and scalability and can be run on GPUs and TPUs.
PyTorch Dynamic neural networks
PyTorch, developed by Facebook, is a flexible and dynamic framework that is particularly popular in research and development. It supports dynamic calculation graphs that allow easy customisation and modification of networks. PyTorch is known for its excellent performance and easy implementation of complex models. It offers extensive support for different types of neural networks and is particularly useful for applications that require fast prototyping and experimental flexibility.
Caffe Fast and efficient deep learning
Caffe is a deep learning framework that is characterised by its speed and efficiency. Originally developed for image processing, Caffe offers a comprehensive architecture for the development of Convolutional Neural Networks (CNNs). It is particularly suitable for applications that require high performance and low latency. Caffe supports a variety of layers and is known for its high speed of training and inference, making it ideal for production environments.