All About Pytorch: Features and Advantages

PyTorch is an open-source machine learning library that is a part of computer software based on the Torch library. This library is used for applications such as computer vision and natural language processing. It is developed by the Facebook's AI Research lab. It is free and is an open-source library that has a C++ interface.

What is PyTorch?

It is a GPU-accelerated tensor computational framework with its functionality extended with Python libraries such as SciPy, NumPy, and Cython. Automatic differentiation is done through a tape-based system at both functional and neural network layer levels. This functionality provides high flexibility and speed like a deep learning framework. PyTorch has two high-level features including:

1. Deep neural networks, that are built on a type-based automatic differentiation system. 
2. Tensor computing (NumPy) with strong acceleration through graphics processing units (GPU)

PyTorch is used in applications such as natural language processing and computer vision developed by Meta AI. It defines a class called Tensor for storing and operating on homogeneous multidimensional rectangular arrays of numbers. PyTorch also provides NumPy-like accelerated functionality.

Features of Pytorch

PyTorch has the following features:

1. Dynamic Computing Graphs: PyTorch offers a platform to provide computational graphs that are dynamic and can be modified during execution.
2. Cloud platform support: PyTorch is supported on most cloud platforms, which allows scaling with large-scale preparation on the GPUs. It also offers the capability to track models in a construction scale setting.
3. Network of libraries: Researchers have been developing a network of libraries to support improvement in areas such as reinforcement learning and computer vision.
4. Imperative programming: It performs calculations as it goes over each line of the code that has been written. This helps in the quick debugging and programming of logic.
5. Flawless transitioning: Hybrid front-end with PyTorch allows ease of use while transitioning flawlessly to diagram mode for optimization as well as operability in C++ execution environments.  

Components of PyTorch

The following are components of PyTorch:

1. Tensors: PyTorch uses tensors as the basic data structure for storing and manipulating data. A tensor is a multi-dimensional array of numbers representing scalars, vectors, matrices, and higher-dimensional arrays.
2. Autograd: PyTorch uses a system called autograd to compute gradients for backpropagation during neural network training automatically. Autograd allows developers to define complex neural network architectures and easily compute gradients with respect to the model's parameters.
3. Neural network building and training: PyTorch provides a range of tools and libraries for building and training neural networks, including the nn module for defining custom neural network architectures and the optim module for defining and using optimization algorithms.
4. Data loading and preprocessing: PyTorch includes tools for loading and preprocessing data, such as the torchvision library for working with image and video data and the torchtext library for working with text data.
5. GPU acceleration: PyTorch supports GPU acceleration, allowing developers to take advantage of the computational power of graphics processing units (GPUs) to speed up training and inference.
6. Interoperability with other frameworks: PyTorch can be used in conjunction with other deep learning frameworks, such as TensorFlow, and it can import and export models from and to other frameworks.
7. Ease of use: PyTorch is designed to be easy to use, with a simple and intuitive API, and it provides many helpful tools and libraries for building and training deep learning models.

Advantages of PyTorch

The following are the advantages of PyTorch:

1. 1. You can use the standard Python control flow, and the model comes out differently for every sample.
   2. With PyTorch, you can create tree-shaped RNNs effortlessly which can otherwise become very difficult.
   3. Using Python debuggers with PyTorch, you can stop the program at any moment and inspect gradients, variables, and anything.
   4. PyTorch helps researchers easily build CNNs for computer vision.
   5. The PyTorch models are quite flexible objects in a way that they do not enforce and do not even expect fixed input shape for data.
   6. It is imperative in nature which allows computations to run immediately. As a result, users do not need to write the complete code to verify if it works. It allows users to run a part of code and inspect it in real-time

Limitations of PyTorch

The following are the limitations of PyTorch:

1. PyTorch is less mature and has less advanced features such as monitoring and visualization tools.
2. The development community of PyTorch is smaller in comparison to what other frameworks offer.
3. In PyTorch, code requires frequent checks for CUDA availability. In case the device has explicitly enabled CUDA, PyTorch requires the user to move everything onto the device.

Modules in PyTorch

PyTorch uses modules for representing neural networks. Modules are the building blocks of a stateful computation. PyTorch offers a library of modules and allows defining new custom modules, thus enabling easy construction of elaborate and multi-layer neural networks. Modules are tightly integrated with the autograd system of PyTorch. You can easily transform modules. These are straightforward to save, restore and transfer between GPU/CPU/TPU devices, quantize, prune, and others.  Let us discuss the type of Modules available in PyTorch.

1. Autograd module

PyTorch uses the automatic differentiation method. A recorder records the operations that have been performed and replay it backwards for computing the gradients. This method is effective in building neural networks since it saves time on one epoch as it calculates the differentiation of parameters at the forward pass.

2. nn module

Computational graphs be easily defined through PyTorch autograd. ‘torch.nn’ module is used since raw autograd is low level for defining complex neural networks. 

3. Optim module

torch.optim implements optimization algorithms that are used to build neural networks. Most commonly used methods are already supported and there is no requirement to build them from scratch.

The fundamental characteristics of modules are:

1. It inherits from the base Module class. 
2. Modules should subclass Module for composability with other modules.
3. It defines “state” that is used in computation. The state contains randomly-initialized weight and bias tensors which define affine transformation. Since it is defined as a Parameter, they are registered for module and will be automatically tracked and returned from calls to the parameters(). Parameters are considered as the “learnable” aspects of module’s computation. 
4. There is no compulsion for modules to have a state and these can remain stateless.
5. It defines the forward() function which performs computation. The input is matrix-multiplied with weight parameter (using @ short-hand notation) and is added to the bias parameter for producing the output. Usually, forward() implementation for a module can perform arbitrary computation involving unlimited inputs and outputs.

Why Learn PyTorch?

PyTorch is used for Deep Learning applications via GPUs and CPUs. Just llike TensorFlow and Keras, PyTorch is a widely used open-source ML library for Python. It is used for building deep learning applications on top of dynamic graphs that can be played with on runtime. Other deep learning frameworks work on static graphs in which computational graphs must be built beforehand.

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PyTorch is mainly utilized in deep learning, computer vision and natural learning process. Through use of PyTorch as core data structure, developers can construct inticrate neural networks such as Tensor and multi-dimensional arrays such as Numpy arrays. 

Due to the dynamic approach of PyTorch, any level of computation can be accessed. It offers an in-depth understanding of what is going on in each algorithm. PyTorch minimizes the number of errors since everything can be automated.  

PyTorch has potential related to deep learning framework in the future. Its flexible nature and computation power put it ahead of other similar software. Anyone who wants to learn about Artificial Intelligence and Machine Learning should choose PyTorch for the following reasons:

1. It is an easy to learn software for both programmers and non-programmers.  
2. It has a dedicated developers community who work towards improving it.
3. The documentation of PyTorch is very helpful for beginners.

Comparison of PyTorch with Other Deep Learning Frameworks

PyTorch vs TensorFlow

Parameters	PyTorch	TensorFlow
Composition	Torch library.	Theano (Python library)
Based on	Dynamic graph concept	Static graph concept
Features	Fewer	More features with higher functionality
Advantage	Pytorch uses a simple API which saves the entire weight of the model.	It has a major benefit that whole graph could be saved as protocol buffer.
Deployment Support	Less support in deployment	More support for embedded and mobile deployments
Scale of community	Smaller community.	Larger community.
Difficulty level	Easy to learn and understand	Comparatively harder to learn

PyTorch vs Keras

Parameter	PyTorch	Keras
API	Low-level API	High-level API.
Speed	Higher in speed than Keras	Comparatively slower in speed
Architecture	Complex architecture	Simple architecture
Readability	Low readability	More readable and easy to use
Community	Larger community support	Smaller community support
Use	For large datasets	For small datasets
Debugging	Easier and faster	Difficult
Static Computation Graphs	Available	Available

Who Should Learn Pytorch?

Anyone with a basic knowledge of Python and Machine Learning can start learning PyTorch. Professionals such as software developers, Python developers, data scientists, data analysts, data architect, AR/VR professionals, robotics professionals, and gaming professionals can take up PyTorch certification.

Why is PyTorch suitable for beginners?

The following reasons make PyTorch suitable for beginners as well:

1. The library comes with a built-in module that offers ready-to-use data sets for numerous deep-learning applications. This includes speech recognition, natural learning processing and speech recognition. Without collecting and processing the data, beginners can build their own neural network. 
2. Along with that, it gets easily integrated with the Python ecosystem. PyTorch is easy to use, learn, extend and debug.
3. It comes with very comprehensive and easy-to-understand documentation along with several learning resources. This makes learning PyTorch very easy.
4. A focused and dedicated community of PyTorch developers makes it easy for beginners to learn and ask questions about PyTorch.

Job Opportunities After PyTorch

Due to a wide number of applications in Deep Learning, there are a number of job opportunities related to PyTorch including the following:

1. AI engineer
2. AI solution architect
3. Full Stack Lead
4. Algorithm Engineer
5. Machine Learning Developer

Resources to Learn PyTorch

There are many official resources for learning PyTorch, including the following:

1. PyTorchDiscuss: This is the official forum for those who want to learn about PyTorch. Here, discussions on deep learning with PyTorch take place. There is an FAQ category available where the most frequently asked questions with answers are available.
2. Tutorials: This is another official page where PyTorch tutorials are available. From learning the basic concepts to loading data, everything is explained over here. Learners can also take a look at the ready-to-deploy PyTorch code with examples.
3. YouTube: An official video series for beginners is available on the platform where learners can take a look at the demonstration of concepts.
4. Dive into Deep Learning: This is an interactive deep learning book consisting of codes, mathematics and discussions. It is officially recommended on PyTorch's website.
5. Online courses: There are many reliable PyTorch online courses through which you can learn about this library from experts. You will also learn about the implementation through demos, projects, and assessments.

Explore PyTorch courses

There are a number of vendors that offer PyTorch related online courses that you can start at your own comfort. Following are the courses that can deepen your understanding of the software:

1. Deep Learning with PyTorch : Convolutional Neural Network on Coursera
2. Deep Learning with PyTorch : Neural Style Transfer 
3. The PyTorch basics you need to start your ML projects 
4. Predictive Analytics with PyTorch 
5. Natural Language Processing with PyTorch 
6. PyTorch Basics for Machine Learning 
7. Deep Neural Networks with PyTorch