11 Deep Learning With Python Libraries and Frameworks

1. Deep Learning With Python Libraries & Frameworks

Today, in this Deep Learning with Python Libraries and Framework Tutorial, we will discuss 11 libraries and frameworks that are a go-to for Deep Learning with Python. In this Deep Learning with Python Libraries, we will see TensorFlow, Keras, Apache mxnet, Caffe, Theano Python and many more.
A library is a collection of modules that implement the related functionality. A framework defines inversion of control- it manages the flow of control and the flow of data.
So, let’s begin Deep Learning with Python Libraries and Framework.

Deep Learning With Python Library and Framework

11 Deep Learning With Python Libraries and Frameworks

2. TensorFlow Python

TensorFlow is an open-source library for numerical computation, for 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.

Deep Learning With Libraries and Framework

TensorFlow Python in Python Deep Learning Libraries

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You can install it using pip with conda Python-

pip install tensorflow

3. Keras Python

Deep Learning With Libraries and Framework

Keras in Deep Learning With Python Libraries and Frame work

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.
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Keras has algorithms for optimizers, normalization, and activation layers. It also deals with Convolutional Neural Networks. It 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

4. Apache mxnet

Deep Learning With Libraries and Framework

Apache mxnet in Deep Learning with Python Libraries

mxnet delivers an amazing number of language bindings for languages like C++, Python, R, JavaScript, and more. It does great with distributed computing and lets us train a network across CPU/GPU machines. The only downside is that we need a little more code to run an experiment in it.
Install it using Python pip-

pip install mxnet

Let’s discuss more in Python Libraries

5. Caffe

Deep Learning With Python Libraries and Framework

Caffe in Python Deep Learning Libraries and Framework

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.

6. Theano Python

Deep Learning With Python Libraries and Framework

Deep Learning With Python Libraries and Framework – Theano

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

Let’s discuss Python machine Learning Algorithms

7. Microsoft Cognitive Toolkit

Deep Learning With Python Libraries and Framework

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

8. PyTorch

Deep Learning With Python Libraries and Framework

Deep Learning With Python Libraries and Framework – PyTorch

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-
https://pytorch.org/#pip-install-pytorch

9. Eclipse DeepLearning4J

Deep Learning With Python Libraries and Framework

Deep Learning With Python Libraries and Framework – Eclipse DeepLearning4J

DeepLearning4J is a deep learning programming library by Eclipse. It is written for Java and the JVM; It is also a computing framework for good support with deep learning algorithms.

10. Lasagne

Deep Learning With Python Libraries and Framework

Deep Learning With Python Libraries and Framework – Lasagne

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

11. nolearn

Deep Learning With Python Libraries and Framework

Deep Learning With Python Libraries and Framework – nolearn

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).
Do you know about Python Machine Learning Techniques
Install it using Python pip-

pip install nolearn
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12. PyLearn2

Deep Learning With Python Libraries and Framework

Pylearn2

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.

13. Conclusion: Deep Learning With Python Libraries & Frameworks

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 Framework through comments.
See also –
Computational Graphs in Deep Learning With Python
For reference

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