Machine Learning Frameworks — What To Choose?


Machine Learning (ML) is one of the fastest emerging technologies today. ML developers are looking for the right framework for their various kinds of projects for ML application development.

Machine learning (ML) is a scientific study of statistical models and algorithms to help a computing system to accomplish designated tasks efficiently and independently by relying solely on inferences and patterns extracted from the training or acquired data. It is a sub-set of Artificial Intelligence technology. Thereby, it automatically leans and improves the performance with the pace of time, interactions, and experiences, and most important acquisition of useful data pertaining to the tasks allocated.


Why Machine Learning Framework?

We know in the software industry that without an interface, libraries, and organized tools, software development proves a nightmare. When we combine these essentials, it becomes a framework or platform for easy, quick, and meaningful software development.

ML frameworks help ML developers to define ML models in precise, transparent, and concise ways. ML frameworks used to provide pre-built and optimize components to help in model building and other tasks.

Which To Choose?

Keras

Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.

Keras represents the library that can work with neural networks on a higher level. It simplifies many tasks, it is used in quick experiments and decreases the amount of the same code. Keras is used on convolutional and recurrent neural networks, and on CPU and GPU.


TensorFlow

Tensorflow is an excellent open-source library for data-based programming for a wide range of tasks. It supports regressions, classifications, and neural networks like complicated tasks and algorithms. You can run it on CPUs & GPUs both.

TensorFlow has complicated functions and paradigms, so it demands additional efforts while learning it. If you have grasped Numpy arrays of Python framework and its libraries, your job becomes easy while working with an n-dimensional array in TensorFlow framework.

It is a flexible framework and offers multiple models as well as versions, which are serving simultaneously. So, it helps in non-automatic migration to newer versions. The framework can run on the GPU, CPU,servers, desktops, and mobile devices. Therefore, it is portable. It is useful in research and development purposes. It provides auto differentiation and performance.


CAFFE (Convolutional Architecture for Fast Feature Embedding)

Caffe 2 is a deep learning tool and highly effective ML framework written in CPP. It supports the major schemes and connected with neural network design. It provides GPU & CPU-based acceleration. Therefore, Caffe 2 is popular in academic research projects and startup prototypes.

It is the fastest framework for deep neural networks and supports GPU training for sample data. It has a well-structured Mat Lab and Python interface to work. It allows switching between CPU & GPU with setting a single flag to train and deploy to commodity clusters.

CAFFE offers great performance and renders it perfect for industrial deployment as well as research experiments. It can process 60M images per day with a single GPU and provides the fastest convent implementation.


PyTorch or Torch

PyTorch is an ML library and scientific computing framework. It comes with scripting language interface from Lua programming UI. Torch used by Facebook, IBM, Yandex, and Idiap Research Institute.
Torch is flexible and offers high-end efficiencies and speed. It offers a lot of pre-trained modules. The most distinct thing in Torch is numbers, not an integer, short or double. Thus, it eases various operations & functions.


Scikit-Learn

Scikit-Learn is a powerful Python library for machine learning coding and explicitly used in the creation of Models. It has built on other libraries such as SciPy, Numpy, and matplotlib. It is a highly efficient tool for classification,regression, and clustering like statistical modeling.
It comes with supervised & unsupervised learning algorithms as well as cross-validation. It is written in Python, but it score is in Cython for the sake of performance. Cython wrapper around LIBSVM helps in the implementation of support vector machines.
It is highly efficient for data mining and supports various practical tasks. It is capable of dealing with complex tasks with ease and speed.


Conclusion

We have explored the top ten machine learning frameworks serving different arena of ML application development. Now, you have enough info to make the right choice for your ML app development needs.


2 Replies to “Machine Learning Frameworks — What To Choose?”

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