11 Deep Learning With Python Libraries and Frameworks

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

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

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

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.

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

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-

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

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.

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.

টেনসরফ্লো হ'ল সংখ্যার গণনার জন্য ওপেন-সোর্স লাইব্রেরি যেখানে এটি ডেটা ফ্লো গ্রাফ ব্যবহার করে। গুগল ব্রেইন টিম গবেষকরা গুগলের মেশিন ইন্টেলিজেন্স গবেষণা সংস্থা দিয়ে এটি তৈরি করেছেন developed টেনসরফ্লো ওপেন সোর্স এবং জনসাধারণের জন্য উপলব্ধ। এটি বিতরণ করা কম্পিউটিংয়ের জন্যও ভাল।

পাইপ ইনস্টল করুন

পিপ ইনস্টল এমএক্সনেট

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পাইপ ইনস্টল cntk

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