Python Deep Learning Libraries and Frameworks

Welcome back to TechVidvan Deep learning Tutorials. In this article, we will review the essential stack of deep learning libraries and frameworks that you must know. Libraries are a pre-written code that a user can directly use in their project without underlying algorithms.

Python Deep Learning Libraries and Frameworks

Deep Learning Libraries and Frameworks

Deep Learning has led to great breakthroughs in various subjects such as computer vision, audio processing, self –driving cars, etc. Python is an open-source programming language and supports various libraries. This has several advantages for Deep Learning. A framework is an interface that permits us to build deep learning models easily and quickly.

Below are a list of various frameworks and  libraries of Deep Learning with python:

1. TensorFlow

tensorflow

TensorFlow has become the foremost popular Deep Learning framework. It supports multiple languages for creating deep learning models. Researchers of the Google brain team have developed this with the machine intelligence organization of google. It is an open-source library and available to the public. It performs numerical computation using data flow graphs. The flexible architecture of tensor flow allows you to deploy models on one or more CPUs. Distributed Computing is the prime benefit of tensor flow. It is a rare case where popularity and effectiveness are equal.

2. Keras

Keras

Keras is the best library for beginners. It is perfect for learning and prototyping simple concepts. It is a minimalist, modular neural network library that uses either theano or tensor flow as a backend. Kears is a high -level API, developed with a focus to enable fast experiments. For quick results, it will automatically complete the core tasks and generate the output. It supports both convolutional neural networks and recurrent neural networks. Sequence-based networks and graph-based networks can easily build in Keras.

3. PyTorch

Pytorch

PyTorch is the prime software tool after tensorflow. It operates with a dynamically updated graph. It supports data parallelism and distributed learning models. PyTorch is better for small projects and prototyping. Researchers also use PyTorch for research purposes. It also provides a framework to construct computational graphs.

4. MXNet

MXNet is another high-level library like Keras. It supports distributed computing. MXNet allows users to train networks over multiple GPU/CPU machines. It can choose between imperative and symbolic styles. This makes it suitable for both beginners and experienced users. It also has fast solving ability. The only limitation is that the user needs little more code to run an experiment.

5. Caffe

It is nearly impossible to mention Deep Learning libraries without using Caffe. Berkely Vision and Learning Center (BVLC) has developed this framework. It is modular and extreme fast. Academics and industry in start-of-the-art applications are prominent users of Caffe. It gives bindings into python programming. Caffe can process 60 million images per day on a K40 GPU.

6. Theano

theano

Theano is a library, used to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays. It tightly integrates with NumPy and transparently uses GPU. Theano makes other libraries more accessible and user-oriented. It acts as a building block for neural networks.

7. DL4j

It implements in java and is more efficient than python. Hadoop and Spark architecture supports this framework. It classifies loading data and training algorithms as a separate process. Deep Learning4j works on different data types like images, CSV, plain text, etc.

8. Lasagne

Lasagne is a lightweight wrapper for Theano. It permits the user to build and train neural networks in Theano. It works as a medium between the low- level programming of Theano and the higher-level abstractions of Keras. Lasagne gives modular building blocks on top of Theano.

9. Gluon

Gluon framework can create simple as well as sophisticated models. MXNet is the base of Gluon. For distributed computing, gluon can alter Keras. Like PyTorch, it supports work with dynamic graphs. Gluon brings training algorithms and neural network models together to provide flexibility in the development process.

10. Microsoft Cognitive toolkit

Microsoft’s cognitive toolkit is an open-source toolkit for deep learning. It explains the neural network as a series of computational steps via directed graphs.

11. Chainer

Chainer

Chainer is the first framework to use dynamic architecture. In comparison to tensor flow, it has a better GPU and the GPU data center performance. It was the prime neural network framework for dynamic computation of graphs.

12. ONNX

Microsoft and Facebook have collaboratively developed the ONNX(Open Neural Network Exchange). It allows models to be trained in one framework and transferred to another for inference. It currently supports Caffe2, Microsoft cognitive toolkit, MXNet, and PyTorch.

13. Nolearn

Nolearn wraps lasagna into a more adaptable application program interface. All the code it carries is unanimous with scikit-learn. Users can use it for applications such as Deep Belief Networks.

Conclusion

Python’s easy accessibility and conciseness of code have sought the attention of developers. It’s a great selection of deep learning specific libraries and frameworks simplify the development process and reduces the development time. Hence, in this article, you have learned Deep learning with python libraries and frameworks. Each library and framework has its use in industry. Hope this article will help you to understand libraries clearly.

Do let us know in the comment section any other Library or Framework you know.

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