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Pytorch for Deep Learning

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PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab. It is free and open-source software released under the Modified BSD license.

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NOTE : You can run these ipython notebooks directly from jovian.ml in Binder or Kaggle or Google Colab or you can download these and run it in you local environment.Make sure you have downloaded all the python packages before using them.

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Required packages :

1. Numpy
2. Pandas
3. Torch

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Notebooks for guide :

1. Numpy - https://jovian.ml/sayantan-world98/numpy

NumPy - NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

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2. Pandas - https://jovian.ml/sayantan-world98/pandas

Pandas - Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.

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3.1 Pytorch Basics - https://jovian.ml/sayantan-world98/pytorch-basics-1

PyTorch - PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab. It is free and open-source software released under the Modified BSD license.

3.2 Pytorch Operations - https://jovian.ml/sayantan-world98/pytorch-operations

This section covers:

• Indexing and slicing
• Reshaping tensors (tensor views)
• Tensor arithmetic and basic operations
• Dot products
• Matrix multiplication
• Additional, more advanced operations

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4. ANN - Artificial Neural Networks

ANN - Artificial neural networks or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules.

1. This section covers the PyTorch autograd implementation of gradient descent. Tools include:

Notebook Link : https://jovian.ml/sayantan-world98/00-pytorch-gradients

• torch.autograd.backward()
• torch.autograd.grad()

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