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PyTorch Deep Learning Framework

PyTorch is a very popular and among the most widely used deep learning frameworks. Learn more about this incredible technology in our detailed whitepaper.

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PyTorch Deep Learning Framework

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  1. PYTORCH DEEP LEARNING FRAMEWORK ONE THING YOU NEED TO MASTER IN DATA SCIENCE www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  2. Ever since its inception in 2017 by the Facebook AI Research (FAIR) team, PyTorch is considered as the most popular and efficient deep learning framework. This open-source machine learning framework is based on the Python Programming language and Torch Python library and enjoys the title of the most favored library for AI researchers and practitioners worldwide. You may find it interesting to know that it is one of the most preferred platforms for deep learning research and offers greater speed for prototype research and deployment. This whitepaper will give you a complete understanding of the PyTorch Deep Learning framework highlighting its working, advantages, key features, and comparison with other frameworks in detail. WHAT IS PYTORCH AND HOW DOES IT WORK? This is the most optimized deep-learning tensor library mostly used for applications that use GPUs and CPUs. It is often preferred over its alternatives like TensorFlow and Keras because it is completely Pythonic and uses dynamic computation graphs. Data scientists, researchers, and developers can use it to run and test portions of their codes in real time and they do not have to wait for the entire code to be implemented to find out if a particular portion of the code is working or not. Here a simple performance and speed comparison between PyTorch and its alternatives. Deep Learning Framework perfromance (Tesla P100/CUDA 8/CuDNN 6) Problem CNN (VGG-style, 32bit) on CIFAR-10- Image Recognition RNN (GRU) on IMDB- Sentiment Analysis ResNet- 5- Feature Extraction 16 14 Relative spped (Tensorflow=1) 12 10 0.8 0.6 0.4 0.2 0.0 TensorFlow CNTK MXNet R-Keras (TensorFlow) Keras (TensorFlow) PyTorch Keras (CNTK) CC by piotr Migdal (Deepsense.ai) based on github.com/ilkarman/DeepLearningFrameworks www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  3. Some of the important features of PyTorch are: Tensor Computation with strong GPU Just like NumPy array, tensors are also generic n-dimensional arrays that can be used for numeric computation which can be accelerated by GPUs. Dynamic computation graphs PyTorch builds computational graphs easily that makes it more flexible and easier for developers to debug as compared to static graph frameworks. Autograd PyTorch’s automatic differential engine or autograd make the process of calculating gradients, an essential step in training neural networks, simple. Modules This deep learning framework also provides a modular way to build and organize neural networks which further makes creating complex architectures easier. PYTORCH MODULES Torch PyTorch’s main module that contains other modules Optimizers A lot of optimization algorithms are included in the PyTorch library that help with training neural networks efficiently. nn Provide layers and functions for the neutral networks Large community PyTorch also has a huge and thriving community that offers developers enough resources, support, tutorials, and pre-trained models for their tasks. Autograd Provides automatic differentiation for all operations on tensors Cloud support Most importantly, PyTorch is supported by several cloud platforms which makes it easier to deploy models and scale them as required Optim Provides neutral network optimization algorithms Utils Provides utilities and helper functions www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  4. A PYTORCH WORKFLOW Improve through Experimentation Fit the model to the data and make a prediction Get data ready (turn into tensors) Evaluate the model Save and reload your trained model Build or pick a pretrained model (to suit your problem) Build a training loop Pick a loss function & optimizer ADVANTAGES OF PYTORCH Using PyTorch for your Data Science projects can offer you several benefits such as: It is easy to learn and has a simple-to-code structure based on Python The learning models can be exported to Open Neural Network Exchange (ONNX) Offers easy debugging with easily available Python tools Has a very user-friendly interface Provides a lot of powerful APIs and extends PyTorch library Offers a C++ front-end interface option All these features make PyTorch a popular choice among beginners as well as experienced Data Science professionals. www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  5. COMPARING WITH TOP CONTENDERS PYTORCH VS. TENSORFLOW Dynamic vs static PyTorch and TensorFlow both can be worked on tensors. However, the basic difference lies in the dynamic computation graphs used in PyTorch and the static computation graph for TensorFlow. Data Parallelism To implement data parallelism, asynchronous execution of Python is used in PyTorch. But in the case of TensorFlow, you will have to manually configure the operation for data parallelism. Visualization Support TensorFlow offers TensorBoard which is a very good visualization library and it is used to track the model training process properly. Whereas PyTorch used to have a visualization library called Visdom which now provides full support to TensorBoard. Model Deployment TensorFlow serving framework is used to deploy models and it uses REST Client API to use model for prediction. PyTorch, on the other hand, does not have a specialized framework to deploy models. TensorFlow Training Time Accuracy Values 100 12.0 Training Accuracy Validation Accuracy 90 11.8 TensorFlow Accuracy and training time 11.6 80 Accuracy Time 11.4 70 11.2 60 11.0 50 5 10 Epochs 20 20 15 10 Epochs 15 5 PyTorch Training Time Accuracy Values 100 7.90 Training Accuracy Validation Accuracy 90 7.85 PyTorch Accuracy and training time 80 7.80 Accuracy Time 7.75 70 7.70 60 7.65 50 5 10 Epochs 20 20 15 10 Epochs 15 5 Source- Upgrad.com www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  6. PYTORCH VS. SCIKIT-LEARN Deep Learning vs Machine Learning Scikit-learn or SKlearn is another popular Python library for machine learning. It offers great support for different machine-learning operations like classification, dimensionality, clustering, reduction, etc. While Scikit-learn is mostly used for machine learning, developers prefer PyTorch for deep learning projects. Scikit-learn isn’t used for training deep neural networks. Ease of use Scikit Learn is very easy to use as compared to PyTorch as in PyTorch, you will have to write more lines of code just to execute the same code as compared to Scikit-learn. Ease of customization Scikit-learn makes it difficult to customize the code whereas PyTorch offers more flexibility to customize the code for specific machine learning problems. COMMON USE CASES OF PYTORCH PyTorch is a highly flexible and powerful library and that is why it is used for a wide range of applications such as: Computer Vision PyTorch is a great tool for tasks like image classification, object detection, and image segmentation. Its torchvision library offers pre-trained models and tools for developer for image processing and makes it easier for them to build and train effective computer vision models. Natural Language Processing (NLP) PyTorch is widely used in NLP for tasks like sentiment analysis, machine translation, and text generation. Its torchtext library offers tools for text preprocessing and working with different NLP datasets. Reinforcement Learning PyTorch's dynamic computation graphs make it well-suited for reinforcement learning tasks, where the model needs to interact with an environment and learn from its experiences. It's used in developing AI agents for games, robotics, and control systems. Generative AI PyTorch can also be used to power models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) which can generate new images, text, and other types of data. Scientific Computing PyTorch has tensor operations and GPU acceleration that makes it useful in various scientific computing sectors including physics simulation, bioinformatics, drug discovery, and more. It must be also noted that these are just a few ways PyTorch is used for different tasks. It is very versatile and its ease of use has made it popular for researchers and developers across all industries. www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  7. CONCLUSION PyTorch is a great data science tool. Every aspiring as well as expert data science professionals must have a strong understanding of PyTorch deep learning framework which will make them perform their tasks easily and advance in their career rapidly. ARE YOU ALSO LOOKING TO MASTER PYTORCH AND GET AHEAD IN THIS REWARDING CAREER PATH? Then, register with top Data Science Certifications from the United States Data Science Institute (USDSI®) and take your career to new heights. www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

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  9. GET STARTED ON YOUR PROFESSIONAL DATA SCIENCE JOURNEY © Copyright 2024. United States Data Science Institute. All Rights Reserved

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