Top 10 Deep Learning Frameworks

Top 10 Deep Learning Frameworks

This blog post will provide you with a list of the top 10 deep learning frameworks that you can use to develop your own AI applications.

Check out this video for more information:


TensorFlow is an open-source software library for data analysis and machine learning. Developed by Google Brain, it can run on multiple CPUs and GPUs, making it a popular choice for deep learning. In addition to being able to process large amounts of data quickly, TensorFlow is also easy to use, with a simple syntax that makes it easy to build complex models.

TensorFlow is one of the most popular deep learning frameworks available, and it is used by a wide variety of businesses and organizations, including Apple, Facebook, IBM, and NASA.


Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.
Caffe has its origins in academic research, but its adopters are businesses and organizations in many sectors who are looking to take advantage of the state-of-the-art in deep learning for their purposes.


Keras is a deep learning framework that allows developers to easily create complex, multi-layer neural networks. It runs on top of other popular DL frameworks such as TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK).

Keras is designed to be user-friendly, modular, and extensible. It has a simple API that makes it easy to get started with DL. Additionally, Keras can be run on both CPUs and GPUs.

-Can be run on both CPUs and GPUs

-May be less efficient than some other DL frameworks


Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:

– Tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.
– Transparent use of a GPU – Perform data-intensive computations up to 140x faster than with CPU.(float32)
– Efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.
– Speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.
– Dynamic C code generation – Evaluate expressions faster.
– Extensive unit-testing and self-verification – Detect and diagnose many types of errors.


Lasagne is a lightweight library to build and train neural networks in Theano. It’s designed to be as flexible as possible and to support rapid prototyping. The core data structure of Lasagne is a network layer, which is a thin wrapper around an actual neural network implementations.

Lasagne comes with a number of built-in layer types, and you can create your own layers as needed. The most important built-in layers are:

-DenseLayer: A fully connected layer with no restrictions on the connectivity pattern.
-Conv2DLayer: A convolutional layer performing only 2D convolutions.
-Pool2DLayer: A pooling layer performing only 2D pooling operations.
-LSTMLayer: An Long Short-Term Memory recurrent layer, with optional peephole connections.
Lasagne also supports recurrent neural networks (RNNs), including Gated Recurrent Units (GRUs) and LSTMs.


Deeplearning4j is a free, open-source software library for Java and the JVM. It can be used to create neural networks and other deep learning models. Deeplearning4j is designed to be used in business environments, not research ones. That means it can be integrated with Hadoop and Spark. Deeplearning4j also runs on GPUs, making it fast.


Torch is one of the most popular deep learning frameworks and is used by researchers all over the world. It’s known for its ease of use and flexibility, and has been used in a wide variety of applications.

Torch is based on the Lua programming language and provides a wide range of features for deep learning. It includes a powerful numerical computation library, which makes it easy to train and deploy neural networks.

Torch also provides a rich set of tools for data visualization and debugging, which are essential for developing deep learning models.


MXNet is a free and open-source deep learning platform used for both research and production. It is supported by a vibrant community of developers and scientists and can be used on a wide range of devices.

MXNet is highly scalable, allowing for easy deployment of large-scale deep learning models. It can be used to train deep neural networks (DNNs) on a variety of data types, including images, text, and time series data.

MXNet is also one of the most popular frameworks for implementing reinforcement learning (RL) algorithms. RL is a type of machine learning that focuses on training agents to perform tasks by trial and error.

MXNet is developed by Apache Software Foundation and was originally created by Carnegie Mellon University, the University of Texas at Austin, and the New York University Moore-Sloan Data Science Center.


Pylearn2 is a machine learning library based on Theano. It is developed by the Yamaha Corporation and Université de Sherbrooke.

Pylearn2 is focused on deep learning, specifically neural networks. It offers a wide range of features, including energy-based models, autoencoders, and generative models.

Pylearn2 is released under the 3-clause BSD license.


Chainer is a powerful, flexible and intuitive deep learning framework. It has been open-source since 2015, developed by Japanese tech company Preferred Networks.

Chainer provides a simple, define-by-run interface that makes it easy to design complex neural networks. It also supports various robust optimization methods such as SGD, Adam, RMSprop and gives users the freedom to design their own custom optimization algorithms.

With Chainer, you can train your models on CPUs or GPUs with equal ease. Chainer is also supported on a wide range of devices such as Raspberry Pi, NVIDIA Jetson TX1/2 and Amazon EC2.

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