11 Deep Learning With Python Libraries and Frameworks
Let's take a look at eleven Deep Learning with Python libraries and frameworks, such as TensorFlow, Keras, and Apache mxnet.
Join the DZone community and get the full member experience.Join For Free
1. TensorFlow Python
TensorFlow is an open-source library for numerical computation in which it uses data flow graphs. The Google Brain Team researchers developed this with the Machine Intelligence research organization by Google. TensorFlow is open source and available to the public. It is also good for distributed computing.
You can install it using pip with conda Python:
pip install tensorflow
2. Keras Python
A minimalist, modular, Neural Network library, Keras uses Theano or TensorFlow as a backend. It makes it easy and faster to experiment and implement ideas into results.
Keras has algorithms for optimizers, normalization, and activation layers. It also deals with Convolutional Neural Networks and lets you build sequence-based and graph-based networks. One limitation is that it doesn’t support multi-GPU environments for training a network in parallel.
You can install it with Python pip:
pip install keras
3. Apache mxnet
Install it using Python pip:
pip install mxnet
Caffe is a deep learning framework that is fast and modular. This isn’t a library but provides bindings into Python. Caffe can process nearly 60 million images per day on a K40 GPU. However, it isn’t as easy to turn hyperparameters with it programmatically.
5. Theano Python
Without NumPy, we couldn’t have SciPy, scikit-learn, and scikit-image. Similarly, Theano serves as a base for many. It is a library that will let you define, optimize, and evaluate mathematical expressions that involve multidimensional arrays. It is tightly integrated with NumPy and transparently uses the GPU.
Theano can act as a building block for scientific computing. Install it with Python pip:
pip install theano
6. Microsoft Cognitive Toolkit
The Microsoft Cognitive Toolkit is a unified Deep Learning toolkit. It describes neural networks using a directed graph in computational steps.
You can install cntk using Python pip:
pip install cntk
PyTorch is a Tensor and Dynamic neural network in Python. It observes strong GPU acceleration, is open-source, and we can use it for applications like natural language processing. You can refer to this link to install PyTorch-
8. Eclipse DeepLearning4J
Lasagne is a lightweight Python library that helps us build and train neural networks in Theano. You can install it using Python pip:
pip install lasagne
nolearn wraps Lasagna into an API that is more user-friendly. All code it holds is compatible with scikit-learn. We can use it for applications like Deep Belief Networks (DBNs).
Install it using Python pip:
pip install nolearn
PyLearn2 is a machine learning library with most functionality built on top of Theano. It is possible to write PyLearn2 plugins making use of mathematical expressions. Theano optimizes and stabilizes these for us and compiles them to the backend we want.
So, this was all in Deep Learning with Python Libraries and Framework. Hope you like our explanation.
Hence, today in this Deep Learning with Python Libraries and Framework tutorial, we discussed 11 libraries and frameworks for you to get started with deep learning. Each Deep Learning Python Library and Framework has its own benefits and limitations. Moreover, in this, we discussed PyTorch, TensorFlow, Keras, Theano etc. That’s all for today. we will come back with the new tutorial of Deep Learning With Python. Tell us about your experience with us on Deep Learning with Python Libraries and Frameworks in the comments.
Published at DZone with permission of Rinu Gour. See the original article here.
Opinions expressed by DZone contributors are their own.