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What are the deep learning frameworks?
What are the deep learning frameworks?
1. TensorFlow
TensorFlow is an open source software library developed by Google that is designed for deep learning or artificial neural networks. TensorFlow allows you to create neural networks and computational models using flowcharts. It is one of the best maintained and most popular open source libraries available for deep learning. The TensorFlow framework can also use Python in C++. Other similar Python-based deep learning frameworks include Theano, Torch, Lasagne, Blocks, MXNet, PyTorch, and Caffe. You can use TensorBoard to stop simple visualizations and view the computational pipeline. Its savvy architecture allows you to easily deploy on different types of devices. The downside is that TensorFlow does not have symbolic loops and does not support scatter learning. In addition, it does not support Windows.
2. Theano
Theano is a Python library designed for deep learning. You can use this tool to define and evaluate mathematical expressions, including multidimensional arrays. The optimization is stopped for the GPU, which has integration with NumPy, dynamic C code generation and symbol recognition. However, in order to achieve a high degree of generality, the tool must be used in conjunction with other libraries such as Keras, Lasagne and Blocks. Theano supports platforms such as Linux, Mac OS X and Windows.
3.Torch
Torch is an easy-to-use open source computing framework for ML algorithms. The tool provides efficient GPU support, N-dimensional arrays, numerical optimization routines, linear algebra routines, and routines for indexing, slicing, and permutation. Based on Lua's scripting language, the tool comes with a lot of pre-worked models. This responsive and efficient ML seminar tool supports mainstream platforms such as Linux, Android, Mac OS X, iOS and Windows.
4.Caffe
Caffe is a far-reaching deep learning tool for building applications. The tool was created by Jia Yangqing during his Ph.D. at the University of California at Berkeley for a project with a good Matlab / C++/Python interface. This tool allows you to quickly apply neural networks to problems without using code to write text. Caffe does not fully support multi-GPU workouts. The tool supports operating systems such as Ubuntu, Mac OS X and Windows.
5.Microsoft CNTK
The Microsoft Cognitive Toolkit is one of the fastest deep learning frameworks supported by the C#/C++/Python interface. This open source framework comes with a powerful C++ API that is faster and more accurate than TensorFlow. The tool also supports distributed learning with built-in data readers. It supports algorithms such as feedforward, CNN, RNN, LSTM, and sequence-to-sequence. The tool supports both Windows and Linux.
6.Keras
Keras, written in Python, is an open source library designed to simplify the creation of new DL models. This advanced neural network API can run on top of the deep learning frameworks such as TensorFlow and Microsoft CNTK. The tool is known for its user-friendliness and modularity, making it ideal for rapid prototyping. The tool stopped optimizing for CPU and GPU.
7. scikit-learn
Scikit-learn is an open source Python library designed for machine learning. Scikit-learn based on libraries such as NumPy, SciPy and matplotlib can be used for data mining and data profiling. Scikit-learn is equipped with a variety of ML models, including linear and logical regression, SVM classifiers, and random forests. This tool can be used for multiple ML tasks such as classification, regression and clustering. Scikit-learn supports operating systems such as Windows and Linux. The drawback is that the GPU is not efficient.
8. Accord.NET
Accord.NET is an ML framework written in C# designed to build consumer-grade computer vision, computer audition, signal processing, and statistical applications. It is a well-documented ML framework that makes it easy to handle audio and image processing. Accord.NET can be used for numerical optimization, artificial neural networks and visualization. It supports Windows.
9. Spark MLlib
Apache Spark's MLIib is an ML library for Java, Scala, Python and R language. Designed to handle large-scale data, this powerful library has many algorithms and tools such as classification, regression, and clustering. The tool interoperates with NumPy in Python and R libraries. It can be easily plugged into a Hadoop workflow.
10. Azure ML Studio
Azure ML Studio is a modern cloud platform for data scientists. It can be used to develop ML models in the cloud. With universal modeling options and algorithms, Azure is a good fit for building larger ML models. This service provides 10GB of storage per account. It can be used with R and Python programs.
11.Amazon Machine Learning
Amazon Machine Learning (AML) is an ML service that provides tools and guides for creating ML models. With visual aids and easy-to-use profiling, AML is designed to make it easier for developers to use ML locally. AML can connect to data stored in Amazon S3, Redshift or RDS.
Machine learning frameworks have pre-built components that are easy to understand and code, so a good ML framework can reduce the complexity of defining ML models. Let's help build ML models quickly and easily with these open source ML frameworks.
TensorFlow is an open source software library developed by Google that is designed for deep learning or artificial neural networks. TensorFlow allows you to create neural networks and computational models using flowcharts. It is one of the best maintained and most popular open source libraries available for deep learning. The TensorFlow framework can also use Python in C++. Other similar Python-based deep learning frameworks include Theano, Torch, Lasagne, Blocks, MXNet, PyTorch, and Caffe. You can use TensorBoard to stop simple visualizations and view the computational pipeline. Its savvy architecture allows you to easily deploy on different types of devices. The downside is that TensorFlow does not have symbolic loops and does not support scatter learning. In addition, it does not support Windows.
2. Theano
Theano is a Python library designed for deep learning. You can use this tool to define and evaluate mathematical expressions, including multidimensional arrays. The optimization is stopped for the GPU, which has integration with NumPy, dynamic C code generation and symbol recognition. However, in order to achieve a high degree of generality, the tool must be used in conjunction with other libraries such as Keras, Lasagne and Blocks. Theano supports platforms such as Linux, Mac OS X and Windows.
3.Torch
Torch is an easy-to-use open source computing framework for ML algorithms. The tool provides efficient GPU support, N-dimensional arrays, numerical optimization routines, linear algebra routines, and routines for indexing, slicing, and permutation. Based on Lua's scripting language, the tool comes with a lot of pre-worked models. This responsive and efficient ML seminar tool supports mainstream platforms such as Linux, Android, Mac OS X, iOS and Windows.
4.Caffe
Caffe is a far-reaching deep learning tool for building applications. The tool was created by Jia Yangqing during his Ph.D. at the University of California at Berkeley for a project with a good Matlab / C++/Python interface. This tool allows you to quickly apply neural networks to problems without using code to write text. Caffe does not fully support multi-GPU workouts. The tool supports operating systems such as Ubuntu, Mac OS X and Windows.
5.Microsoft CNTK
The Microsoft Cognitive Toolkit is one of the fastest deep learning frameworks supported by the C#/C++/Python interface. This open source framework comes with a powerful C++ API that is faster and more accurate than TensorFlow. The tool also supports distributed learning with built-in data readers. It supports algorithms such as feedforward, CNN, RNN, LSTM, and sequence-to-sequence. The tool supports both Windows and Linux.
6.Keras
Keras, written in Python, is an open source library designed to simplify the creation of new DL models. This advanced neural network API can run on top of the deep learning frameworks such as TensorFlow and Microsoft CNTK. The tool is known for its user-friendliness and modularity, making it ideal for rapid prototyping. The tool stopped optimizing for CPU and GPU.
7. scikit-learn
Scikit-learn is an open source Python library designed for machine learning. Scikit-learn based on libraries such as NumPy, SciPy and matplotlib can be used for data mining and data profiling. Scikit-learn is equipped with a variety of ML models, including linear and logical regression, SVM classifiers, and random forests. This tool can be used for multiple ML tasks such as classification, regression and clustering. Scikit-learn supports operating systems such as Windows and Linux. The drawback is that the GPU is not efficient.
8. Accord.NET
Accord.NET is an ML framework written in C# designed to build consumer-grade computer vision, computer audition, signal processing, and statistical applications. It is a well-documented ML framework that makes it easy to handle audio and image processing. Accord.NET can be used for numerical optimization, artificial neural networks and visualization. It supports Windows.
9. Spark MLlib
Apache Spark's MLIib is an ML library for Java, Scala, Python and R language. Designed to handle large-scale data, this powerful library has many algorithms and tools such as classification, regression, and clustering. The tool interoperates with NumPy in Python and R libraries. It can be easily plugged into a Hadoop workflow.
10. Azure ML Studio
Azure ML Studio is a modern cloud platform for data scientists. It can be used to develop ML models in the cloud. With universal modeling options and algorithms, Azure is a good fit for building larger ML models. This service provides 10GB of storage per account. It can be used with R and Python programs.
11.Amazon Machine Learning
Amazon Machine Learning (AML) is an ML service that provides tools and guides for creating ML models. With visual aids and easy-to-use profiling, AML is designed to make it easier for developers to use ML locally. AML can connect to data stored in Amazon S3, Redshift or RDS.
Machine learning frameworks have pre-built components that are easy to understand and code, so a good ML framework can reduce the complexity of defining ML models. Let's help build ML models quickly and easily with these open source ML frameworks.
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