Top 10 Deep Learning Frameworks for Every Data Scientist
Deep learning framework with an interface or a library/tool helps Data Scientists and ML Developers to bring the deep learning models into life. Deep Learning a sub-branch of machine learning, that puts efficiency and accuracy on the table, when it is trained with a vast amounts of bigdata.
Analytics Insights brings the Top 10 Deep Learning Frameworks for every Data Scientist-
TensorFlow developed by the Google Brain team, is inarguably one of the most popular deep learning frameworks. It supports Python, C++, and R to create deep learning models along with wrapper libraries. It is available on both desktop and mobile. The most popular use case of TensorFlow is the Google Translate integrated with capabilities like NLP, text classification, summarization, speech/image/handwriting recognition and forecasting. Its visualization toolkit, TensorBoard, provides effective data visualization of network modelling and performance.
TensorFlow Serving, another TensorFlow tool, is deployed for the rapid deployment of new algorithms/experiments while retaining the same server architecture and APIs.
Deeplearning4j is a deep learning library for the Java Virtual Machine (JVM) developed in Java and supports other JVM languages like Scala, Clojure, and Kotlin.
Parallel training through iterative reduces, micro-service architecture adaption coupled with distributed CPUs and GPUs are some of the salient features when it comes to Eclipse Deeplearning4j deep learning framework. Deeplearning4j is widely adopted as a commercial, industry-focused, and distributed deep learning platform which comes with its own deep network support through RBM, DBN, Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), Recursive Neural Tensor Network (RNTN) and Long Short-Term Memory (LTSM). It can be administered on top of both Hadoop and Spark.
The Microsoft Cognitive Toolkit (earlier known as CNTK) is an open-source deep learning framework to train deep learning models. CNTK is used for Convolution Neural Networks and training for image, speech, and text-based data.
The Microsoft Cognitive Toolkit is known to provide higher performance and scalability while operating on multiple machines. the implementation of Reinforcement Learning models or Generative Adversarial Networks (GANs) can be done quickly using the CNTK.
The Microsoft Cognitive Toolkit is highly efficient and scalable for multiple machines, supported by interfaces such as Python, C++, and Command Line and fit for image, handwriting and speech recognition use cases.
Written in Python, Keras neural networks library supports both convolutional and recurrent networks that are capable of running on either TensorFlow or Theano. Keras deep learning framework is built to provide a simplistic interface for quick prototyping by constructing active neural networks which can work with TensorFlow. Keras is lightweight, easy-to-use, and with its minimalist approach, it is a part of TensorFlow’s core API.
Use cases of Keras range from classification, text generation, and summarization, tagging, translation along with speech recognition, and others. Keras is easy-to-understand and consistent APIs that seamlessly integrates with TensorFlow workflow and comes with a built-in support for multi-GPU parallelism and distributed training.
Shogun is an open-source machine learning framework compatible with the C++ programming language. Its free platform helps developers to design algorithms and data structures, primarily for ML problems in education and research domains.
