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Assignment_1.txt
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Assignment_1.txt
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1. TensorFlow:
Distinct Features:
TensorFlow is an open-source deep learning framework developed by Google.
It offers a comprehensive ecosystem for machine learning and deep learning.
TensorFlow provides both high-level APIs (like Keras) and low-level APIs for maximum flexibility.
It supports distributed computing and can run on various platforms, including CPU and GPU.
Functionality:
TensorFlow is known for its computational graph-based approach, making it highly efficient for deep learning.
It has extensive libraries for neural networks, including pre-built layers and optimizers.
TensorFlow also supports deployment on mobile and embedded devices, making it versatile.
2. Keras:
Distinct Features:
Keras is an open-source high-level neural networks API that can run on top of TensorFlow, Theano, or other deep learning frameworks.
It is designed for easy and rapid prototyping of neural networks.
Keras is user-friendly, with a simple and consistent API.
Functionality:
Keras is excellent for beginners and for quickly building and testing neural network models.
It provides a high-level interface for defining and training neural networks with minimal code.
Keras can seamlessly switch between different backend engines, such as TensorFlow and Theano.
3. Theano:
Distinct Features:
Theano is an open-source numerical computation library that specializes in optimizing mathematical expressions.
It is one of the early deep learning frameworks and has been influential in the field.
Theano focuses on CPU and GPU computation optimization.
Functionality:
Theano is used as a backend for other deep learning frameworks like Keras.
It is highly efficient for symbolic mathematics and can automatically optimize expressions for better performance.
While Theano has been influential, it is less actively developed now in favor of TensorFlow and PyTorch.
4. PyTorch:
Distinct Features:
PyTorch is an open-source deep learning framework developed by Facebook's AI Research lab.
It's known for its dynamic computation graph, which makes it more flexible for certain applications.
PyTorch has gained popularity for research and rapid experimentation.
Functionality:
PyTorch's dynamic computation graph allows for easy debugging and dynamic changes during runtime.
It is highly popular in the research community due to its flexibility and support for dynamic networks.
PyTorch also offers a wide range of libraries for natural language processing, computer vision, and reinforcement learning.