Deep learning is a very exciting field. There are numerous application areas, ranging from reinforcement learning applications to image categorization and sound production. While working on these interesting initiatives, we frequently desire to “outsource” the onerous process of designing model algorithms to deep learning frameworks.<\/p>\n
Deep Learning is currently one of the most in-demand industrial skills. Deep learning is now being applied in numerous fields to address previously unsolvable issues, such as self-driving cars, face recognition\/augmentation, and so on.
\nThere are various deep learning frameworks that have been built and supported by big tech, institutions, and researchers. Maintaining a deep learning framework, on the other hand, is a difficult task. Despite the fact that they are supported by large tech companies such as Microsoft, several outdated deep learning frameworks exist. As a result, finding a deep learning framework that is still in active development is critical for your project’s future.<\/p>\n
TensorFlow is a high-performance numerical computation library with approximately 35,000 comments and a vibrant community of approximately 1,500 contributors. It is used in a variety of scientific fields. TensorFlow is essentially a framework for defining and running computations involving tensors, which are partially defined computational objects that produce a value.<\/p>\n
TensorFlow\u00a0 Features:<\/strong><\/p>\n Applications:<\/strong><\/p>\n Installation and importing<\/strong><\/p>\n PyTorch is a Python-based scientific computing tool that makes use of the power of graphics processing units, PyTorch is a popular deep learning research platform that is designed to provide maximum flexibility and speed.<\/p>\n PyTorch, introduced by Facebook, is the second most popular framework. Torch Python is, as the name implies, the Python version of the Torch (C++ library). PyTorch works in tandem with Python and Numpy. PyTorch works with multidimensional arrays known as Tensors, which provide an API that is quite similar to Numpy.<\/p>\n In PyTorch, you must build your own training loops and manually update the weights. This gives you more control over your model. This is the primary reason why researchers prefer PyTorch. However, this strategy frequently leads to boilerplate code, which is poor for software deployment.<\/p>\n Installation and importing<\/strong><\/p>\n The Apache Foundation introduced MXNet, an open-source deep learning framework. It is a deep learning framework that is adaptable, scalable, and rapid. It is available in a variety of programming languages, including C++, Python, Java, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, and Wolfram Language. Installation and importing<\/strong><\/p>\n fastai is a deep learning library that provides practitioners with high-level components that can rapidly and easily produce state-of-the-art results in standard deep learning domains, as well as low-level components that may be combined and matched to develop new techniques. It seeks to achieve these goals while making no significant sacrifices in terms of usability, flexibility, or performance. This is made possible by a meticulously layered design that defines the common underlying patterns of numerous deep learning and data processing approaches in terms of disconnected abstractions. By using the dynamic of the underlying Python language and the flexibility of the PyTorch library, these abstractions may be stated eloquently and unambiguously.<\/p>\n fastai consists of:<\/strong><\/p>\n fastai<\/strong> is organized around two key design goals: being approachable and productive while simultaneously being profoundly hackable and flexible. It is built on a foundation of lower-level APIs that provide reusable building components. As a result, a user who wants to rewrite a portion of the high-level API or add specific behavior to meet their needs does not need to learn how to utilize the lowest level.<\/p>\n When compared to the other frameworks on this list, PyTorch lighting is a relatively recent framework. Lighting is nothing more than a wrapper for the original PyTorch library. It adds a thin layer of abstraction, decreasing the amount of boilerplate code while retaining PyTorch’s strength and beauty.<\/p>\n Lightning makes it easy to perform profiling, metric logging and visualization, and distributed training. Furthermore, the switch from GPUs to TPUs does not necessitate any additional lines of code. As a result, PyTorch is getting closer to being deployed.<\/p>\n Every framework has some features that are superior to others. So, if you’re looking for a framework to start with, you can choose any of these. As you go, you will realize which framework is best suited for you or your task and will modify it accordingly.<\/p>\n","protected":false},"excerpt":{"rendered":" Deep learning is a very exciting field. There are numerous application areas, ranging from reinforcement learning applications to image categorization and sound production. While working on these interesting initiatives, we frequently desire to “outsource” the onerous process of designing model algorithms to deep learning frameworks. Deep Learning is currently one of the most in-demand industrial …<\/p>\n\n
\n
pip install tensorflow\r\nimport tensorflow<\/pre>\n
PyTorch<\/h4>\n
\n
pip install torch torchvision\r\nimport torch<\/pre>\n
MxNet<\/h4>\n
\nAmazon, Intel, Baidu, Microsoft, Wolfram Research, Carnegie Mellon, MIT, and the University of Washington all use and support MXNet. Despite the fact that MXNet is supported by a number of reputable institutions and technology firms, community support for the project is minimal. As a result, it is less popular than TensorFlow, Keras, and PyTorch.<\/p>\npip install mxnet\r\nimport mxnet<\/pre>\n
FastAI<\/h4>\n
\n
PyTorch Lightning<\/strong><\/h4>\n