Deep learning frameworks are used to create deep and machine learning models. The frameworks offer tried-and-true foundations for building and training deep neural networks by streamlining machine learning techniques. These deep learning frameworks give programmers access to tools, libraries, and interfaces that make building deep and machine learning models simpler than it would be to design them from scratch.
Additionally, they provide concise methods for defining models that make use of already developed and optimised functions. The top 10 deep learning frameworks provide practical and evidence-based methods for developing machine or deep learning algorithms, which speeds up the process and yields results that are significantly more accurate than if the model were built from scratch. Let’s examine the most significant deep learning frameworks for 2023.
Tensorflow
TensorFlow is an open-source, cost-free machine learning software library and one of the most popular deep learning frameworks. Almost all of the coding is done in Python. It was developed by Google and is particularly effective for inference and training of neural networks. Deep learning inference is the process of drawing conclusions about as-yet-untested data using trained deep neural network models.
Keras
Keras is yet another well-known open-source software library. The deep learning framework offers a Python interface for building artificial neural networks. The TensorFlow library interface is supplied by Keras. It has gained accolades for having a user-friendly, clear UI.
PyTorch: A Python library named PyTorch supports the creation of deep learning applications like computer vision and natural language processing. PyTorch offers two important features: tensor computing (like NumPy) with significant GPU acceleration, and deep neural networks built on top of a tape-based automatic differentiation system that numerically evaluates the derivative of a function given by a computer programme.
MxNet
Deep neural networks are deployed and trained using the Apache MxNet open-source deep learning framework. Scalability is a distinction for MxNet when compared to other frameworks. MxNet is distinguished from frameworks like Keras, which only support one language, by its versatility with numerous languages.
Deeplearning4j
Deeplearning4J is a collection of tools that enable the construction of JVM-based deep learning applications and aid model creation and model modification. For building MultiLayerNetworks and ComputationGraphs, it has a high-level API (DL4J), a general-purpose linear algebra library (ND4J), a deep learning and automatic differentiation framework (SameDiff), an ETL for machine learning data (DataVec), a C++ library (LibND4J), and integrated Python execution (Python4J).
CNTK
An open-source deep learning framework for designing, training, and analyzing neural networks is called Microsoft Cognitive Framework (CNTK). The common model types that can be employed with SGD learning, which makes use of automatic differentiation and parallelization over numerous GPUs and servers, are feed-forward DNNs, CNNs, and RNNs/LSTMs. It was released under an open-source licence in April 2015.
Torch: Torch is a scientific computing platform that provides a variety of deep-learning techniques. Based on the Lua programming language, it is open-source. The torch uses the scripting language LuaJIT and has a C implementation at its core. It was developed at the IDIAP research facility at the École Polytechnique Fédérale de Lausanne (EPFL).
Chainer
A deep learning framework named Chainer is built on the NumPy and CuPy libraries. Chainer is the first framework to ever use a “define-by-run” strategy as opposed to the more typical “define-and-run” method.
Caffe
Convolutional Architecture for Fast Feature Embedding, or Caffe, is a deep learning framework developed at the University of California, Berkeley. It is open-source C++ software with a Python user interface that is distributed under the BSD licence. Yangqing Jia developed the Caffe project while pursuing his doctorate at UC Berkeley; it is openly accessible on GitHub.
Theano: Theano is a powerful deep-learning tool that enables efficient manipulation and evaluation of mathematical expressions, especially those using matrix values. It is a Python-based open-source project with a syntax similar to NumPy that was developed by the Montreal Institute for Learning Algorithms (MILA) at the University of Montréal.
