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Introduction

Facebook AI developed Pytorch in 2016. It is an open-source deep learning framework that helps develop machine learning models. With the implementation of the Pytorch framework, deploying machine learning models is accessible in the production phases of an organization. 

Besides, Pytorch provides various functionalities that work well with other Python libraries. Moreover, Pytorch offers faster GPU acceleration to allow complex computational tasks to be performed more effectively with speed and proper optimization necessary for sophisticated models. 

In addition, Pytorch offers additional functionalities like lag-free techniques to modify network behavior and flexibility in object-oriented support programming and C++ runtime environments.

There is also support for an end-to-end workflow to implement machine learning for mobile application necessities. Therefore, Pytorch is immensely popular among data science and artificial intelligence professionals, with multiple benefits. It is implemented for neural network architectures, and any aspirant aiming for a career in these disciplines should upskill themselves with these skills to achieve some of the top positions in the industry.

1. Deep Neural Networks with PyTorch by IBM – CourseraDeep Neural Networks with PyTorch by IBM – Coursera

The course is available on Coursera. In this program, learning Pytorch from top industry professionals of IBM offers tremendous value and exposure to critical concepts of the industry. The program begins with the basics of Pytorch and gradually proceeds to concepts like Pytorch tensors and the automatic differentiation package. 

Next, the learners will cover linear regression, logistic regression, and softmax fundamentals. The learners will also explore the essentials of feedforward deep neural networks and the role of activation functions, normalization, and dropout layers in deep learning models.

Finally, the learners will learn about convolutional neural networks and transfer learning and cover various deep learning methods for model building. In terms of practical skills, the program allows learners to learn how to use Python libraries such as Pytorch library for deep learning applications. Furthermore, the learners will learn to build deep neural networks using Pytorch for solving critical problems efficiently and accurately. 

The course curriculum includes:

  • Tensor and datasets
  • Linear regression
  • Linear regression: Pytorch way
  • Multiple input-output Linear regression
  • Logistic regression for classification
  • Softmax regression
  • Shallow neural networks
  • Deep networks
  • Convolutional neural network
  • Peer review for capstone

Instructor: Joseph Santarcangelo

Level: Intermediate

Duration: 31 hours

User Review: 4.4/5

No. of Reviews: 1032

Price: Free Enrollment (Additional charges for certification may apply for certification)

2. PyTorch Basics for Machine Learning – edX

PyTorch Basics for Machine Learning – edX

This is the first part of a two-part course covering the fundamentals of Pytorch. IBM offers it on the edX platform. In this program, the learners will learn to implement machine learning algorithms and understand how Pytorch creates well-optimized models for computation. Besides, the learners will cover various aspects of Pytorch with a solid foundation for mastering the deep learning model-building phases in future advanced programs. 

The learners who complete this course are eligible for an IBM skill badge. This badge is a unique verifiable and digital credential that is well-recognized and accepted in the industry, proving the learners’ knowledge and skills to the highest standards. 

The takeaways from the course include:

  • Learning to build machine learning pipeline in Pytorch
  • Training machine learning models in Pytorch
  • Loading and working with large datasets
  • Training machine learning applications using Pytorch
  • Gain a solid understanding of applying deep learning methods and incorporating Pytorch and Python libraries like Numpy and Pandas.

The course contents are:

  • Module 1
  • Tensors 1D
  • Two-Dimensional Tensors
  • Derivatives in PyTorch
  • Dataset
  • Module 2
  • Prediction Linear Regression
  • Training Linear Regression
  • Loss
  • Gradient Descent
  • Cost
  • Training PyTorch
  • Module 3
  • Gradient Descent
  • Mini-Batch Gradient Descent
  • Optimization in PyTorch
  • Training and Validation
  • Early stopping
  • Module 4
  • Multiple Linear Regression Prediction
  • Multiple Linear Regression Training
  • Linear regression multiple outputs
  • Multiple Output Linear Regression Training
  • Module 5
  • Final project

Instructor: Joseph Santarcangelo

Level: Beginner/Intermediate

Duration: 5 weeks

User Review: NA

No. of Reviews: NA

Price: Free Enrollment (Additional charges for certification may apply for certification)

3. Introduction to Deep Learning with Pytorch – Udacity

Introduction to Deep Learning with Pytorch – Udacity

This is a free course offered by Facebook AI on the Udacity platform. This certification course focuses on providing the basics of deep learning and allowing learners to learn to build deep neural networks using Pytorch. 

The emphasis is on providing learners with practical experience with Pytorch through various coding exercises and projects. In addition, the learners will explore how to implement Pytorch to build state-of-the-art AI-based text generation applications. However, this program has a few prerequisites that require learning to have some essential experience with Python and data processing libraries such as Numpy and Matplotlib. In addition, the basic knowledge of linear algebra and calculus is recommended but not mandatory. 

The course curriculum includes:

  • Introduction to deep learning
  • Introduction to Pytorch
  • Deep learning with Pytorch
  • Convolutional neural network
  • Style transfer
  • Recurrent neural network
  • Natural language classification
  • Deploying with Pytorch

Instructor: Luis Serrano, Alexis Cook, Soumith Chintala, Cezanne Camacho, Mat Leonard

Level: Beginner/Intermediate

Duration: 2 months

User Review: NA

No. of Reviews: NA

Price: Free

4. Pytorch: Deep Learning and Artificial Intelligence – Udemy

Pytorch- Deep Learning and Artificial Intelligence – Udemy

The course is offered on Udemy. Among various Pytorch courses, this program is among the highest-rated courses on the platform. This certification program is ideal for both beginners and expert-level learners. The fundamentals of Pytorch to the deep learning architectures such as deep neural networks, convolutional neural networks, and recurrent neural networks are covered in-depth. 

Further, the learners will learn to convert previous codes to integrate with Pytorch and work on various time series forecasting and stock predictions projects.

Additionally, concepts on loss function are covered in detail, including the Pytorch library and its functionalities. The program is more about practical skill-building than a dense theory course. 

Finally, there are additional projects that the learners will work upon, such as NLP, recommender system, transfer learning for a computer vision problem, generative adversarial network, and deep reinforcement learning for stock trading bots. 

The course curriculum includes:

  • Introduction
  • Google Colab
  • Machine learning and neurons
  • Feedforward artificial neural networks
  • Convolutional neural networks
  • Recurrent neural network, time-series, and sequence data
  • NLP
  • Recommender system
  • Transfer learning and GANs
  • Uncertainty estimation
  • Facial recognition
  • In-depth loss functions and gradient descent
  • Extras

Instructor: Lazy Programmer Team

Level: Beginner/Intermediate/Advanced

Duration: 23 hours and 47 minutes

User Review: 4.7/5

No. of Reviews: 961

Price: $169.2

5. Intro to Machine Learning with Pytorch Nanodegree Program – Udacity

Intro to Machine Learning with Pytorch Nanodegree Program – Udacity

The course is offered on Udacity in collaboration with Kaggle and Amazon AWS. This program offers foundational machine learning concepts from data cleaning to supervised models.

Further, the learners will understand various concepts on machine learning algorithms, understand supervised and unsupervised learning and gain practical experience at each step of the way using Python and PyTorch. There are specific prerequisites for the program that include familiarity with probability, mean and variance concepts, and intermediate python programming language.

 The course curriculum includes:

  • Supervised learning, and common class methods for model construction
  • Deep learning foundations of neural network design and training using Pytorch
  • Implementing unsupervised learning methods for different domains and problem-solving

Instructor: Cezzane Camacho, Mat Leonard, Luis Serrano, Dan Romuald Mbanga, Jennifer Staab, Sean Carrell, Josh Bernhard, Jay Alammar, and Andrew Paster

Level: Intermediate/Advanced

Duration: 3 months

User Review: 4.7/5

No. of Reviews: 419

Price: Monthly: $302/3-Month Access: $770

6. PyTorch for Deep Learning with Python Bootcamp – UdemyPyTorch for Deep Learning with Python Bootcamp – Udemy

The course is available on Udemy. This certification course is a blend of theory and practical hands-on experience. The learners will understand how to apply the concepts to various datasets. The learners will generally cover all the aspects and concepts covered in a deep learning specialization course. Numpy, Pandas, machine learning theory, testing, training, validation, and model evaluation will be covered in-depth. 

The concepts on tensors with Pytorch and neural network theory are covered with complete hands-on experience. By the end of the course, the learners will gain sufficient exposure to deep learning models to solve complex problems. 

The course curriculum includes:

  • Introduction
  • Installation and setup
  • Crash course for Numpy and Pandas
  • Pytorch basics
  • Machine learning concepts overview
  • Artificial neural networks
  • Convolutional neural networks
  • Recurrent neural networks
  • Using a GPU with Pytorch and CUDA
  • NLP with Pytorch
  • Bonus

Instructor: Jose Portilla

Level: Beginner

Duration: 17 hours

User Review: 4.6/5

No. of Reviews: 2958

Price: $46.2

7. Deep Learning Nanodegree Program – Udacity

Deep Learning Nanodegree Program – Udacity

The course is available on Udacity in collaboration with AWS and Facebook AI. It is a specialization program that offers learners to learn about neural networks and understand the implementation using Pytorch. Further, the learners will explore convolutional neural networks for image recognition, recurrent neural networks for sequence generation problems, and GANs for image generation. 

The learners will also delve into the concepts of model deployment in-depth. The course prerequisites require learners to be familiar with the basics of Python programming. 

The course curriculum includes:

  • Introduction
  • Neural networks basics and creating a first network using Python and Numpy.
  • Building multi-layer neural networks for analyzing real data.
  • Building convolutional neural networks to classify images based on patterns and objects.
  • Recurrent neural networks and short-term memory networks using Pytorch and performing sentiment analysis.
  • Generative adversarial networks for generating realistic images.
  • Deploying sentiment analysis model: building, deploying, and creating a gateway for accessing a website.

Instructor: Cezzane Camacho, Mat Leonard, Luis Serrano, Alexis Cook, Jennifer Staab, Sean Carrell, Ortal Arel, Jay Alammar, and Daniel Jiang

Level: Intermediate

Duration: 4 months

User Review: 4.7/5

No. of Reviews: 3297

Price: $46.2

8. Building Deep Learning Models Using PyTorch – PluralsightBuilding Deep Learning Models Using PyTorch – Pluralsight

The course is offered on the Pluralsight platform. This certification focuses on providing learners with the skills to build deep learning models using Pytorch and other libraries. Firstly, the learners will begin with the principles and understanding of Torch tensors, dynamic computation graphs, and the autograd library for gradient computation. 

In addition, the learners will learn to train neural networks and forward and backward passes and build a simple neural network for predicting automobile prices. Next, the learners will cover the concepts on image classification using a convolutional neural network and become familiar with their architecture.

Furthermore, the learners will learn to build CNN models to classify images and implement transfer learning with the help of a pre-trained model for performing image classification. Finally, the learners will explore the concepts of recurrent neural networks for data sequencing and deep dive into executing dynamic computation graphs in Pytorch for RNN. 

At the end of the course, the learners will be comfortable with Pytorch libraries and APIs and learn to use pre-trained models that Pytorch offers for solving specific use cases. 

The course contents are:

  • Introduction to Pytorch
    • Neurons and neural networks
    • Introducing and installing Pytorch
    • Tensors
    • Creating and working with Pytorch tensors
    • Operating with tensors
    • The computation graph
    • Gradient Descent
    • Forward and Backward passes
  • Building simple neural networks
    • Introducing Autograd
    • Reverse mode automatic differentiation to calculate gradients
    • Linear model using autograd
    • Exploring the automobile price prediction dataset
    • Price prediction using a fully connected neural network
    • Optimizers
    • Neural networks for classification
    • Exploring the titanic dataset for classification
    • Plotting accuracy and loss metrics
  • Building an image classification model
    • Perceiving an image
    • Convolutional layers
    • Pooling layers
    • CNN architectures
    • Batch normalization
    • Exploring the CIFAR10 Dataset
    • Building and training the CNN
    • Predictions on test data
    • Transfer learning
    • ResNet: Data exploration, training and prediction, and frozen layers
  • Building a text classification model
  • Recurrent neurons
  • Unrolling RNN memory cells through time
  • Long memory cells
  • Gender prediction of names RNN structure
  • Preparing the names dataset
  • Building and training RNN
  • Confusion matrix
  • Plotting name predictions in a confusion matrix

Instructor: Janani Ravi

Level: Beginner

Duration: 3 hours 18 minutes

User Review: 4.6/5

No. of Reviews: 35

Price: 10-day free trial (Charges applicable after trial period)

9. Pytorch Essential Training: Deep Learning – LinkedIn Learning

Pytorch Essential Training- Deep Learning – LinkedIn Learning

This is a course offered by LinkedIn Learning. The training program explores the fundamentals of the deep learning frameworks and how Pytorch enables deep learning tasks effectively with its integration with Python. The learners will also explore the concepts on computational graphs and build an image recognition model with different components. Besides, the learners will understand how tensors work, concepts on loss function, autograd, and troubleshooting a Pytorch network. 

The course curriculum includes:

  • Introduction
  • Fashion MNIST and neural networks
  • Working with classes and tensors
  • Working with loss, autograd, and optimizers
  • Troubleshooting and CPU/GPU usage
  • Conclusion

Instructor: Jonathan Fernandes

Level: Intermediate

Duration: Self-paced

User Review: NA

No. of Reviews: NA

Price: 1-month free trial (Charges applicable after trial period)

10. Deploying PyTorch Models in Production: PyTorch Playbook – Pluralsight

Deploying PyTorch Models in Production- PyTorch Playbook – Pluralsight

The course is offered on the Pluralsight platform. It is an advanced course that focuses on deploying Pytorch models in production. First, the learners will become familiar with the Pytorch playbook and learn to leverage the advanced functionalities for serializing and deserializing Pytorch models. 

Next, the concepts of training these advanced models will be covered with hands-on exercises, followed by learning to deploy them for prediction tasks. In addition, the learners will delve into the concepts of load-state_dict and the torch. save() and torch.load() methods and understand how they complement and differ from one another. The learners will also delve into the pros and cons of these methods and learn to leverage state_dict(), a helpful dictionary comprising the information about parameters and hyperparameters.

Furthermore, the learners will learn about multiprocessing, data-parallel, and distributed data-parallel approaches for performing distributed training using Pytorch. Besides, practical exercises allow learners to understand how to train a Pytorch model on a distributed cluster with high-level estimator APIs. Finally, the learners will learn to deploy Pytorch models using the Flask application, a clipper cluster, and a serverless environment.

By the end of the course, the learners will master the distributed training and deployment of Pytorch models and implement advanced mechanisms for model serialization and deserialization. 

The course contents are:

  • Persisting and loading Pytorch models
    • Saving and loading Pytorch models
    • Building and training a classifier model
    • Saving and loading models using torch.save()
    • Saving model using the state.dict()
    • Saving and loading checkpoints
    • Introducing ONNX
    • Exploring a model to ONNX and loading in Caffe2
  • Implementing training using single and multiple processors
    • Distributed training options in Pytorch
    • Training using multiple processes
    • Setting up a deep learning VM with multiple GPUs
  • Implementing distributed training on multiple machines
    • Distributed training on the cloud
    • Setting up SageMaker Notebook Instance
    • Setting up training and test data loaders
    • Define the training function
    • Functions to test and save the trained model
    • Running distributed training using Pytorch estimator
  • Deploying Pytorch models to production
  • Exploring options to deploy Pytorch models
  • Installing libraries and uploading model parameters to a GCP bucket
  • Creating a flask app to serve the Pytorch model
  • Using the model for prediction
  • Installing Docker
  • Creating and using a clipper cluster for prediction
  • Deploying a model for prediction to a serverless environment

Instructor: Janani Ravi

Level: Advanced

Duration: 2 hours 12 minutes

User Review: NA

No. of Reviews: NA

Price: 10-day free trial (Charges applicable after trial period)

11. PyTorch for Deep Learning and Computer Vision – Udemy

PyTorch for Deep Learning and Computer Vision – Udemy

The course is available on Udemy. This training program covers the basics of building sophisticated deep learning and computer vision applications using Pytorch. There are various step-by-step demos and hands-on exercises to provide a comprehensive learning experience. 

By the end of the course, the learners will master the skills of working with tensor data structure and implementing machine learning and deep learning applications with Pytorch. In addition, the learners will understand neural networks and become familiar with their development. Besides, the learners will build complex models for advanced imaging requirements in computer vision-related problems. 

The learners will also learn to use pre-trained models and understand transfer learning concepts to build sophisticated AI applications with minimal computational resources at their disposal. 

The course contents are:

  • Getting started
  • Introduction to tensors using Pytorch
  • Linear regression
  • Perceptron
  • Deep neural networks
  • Image recognition
  • Convolutional neural networks
  • CIFAR10 classification
  • Transfer learning

Instructor: Rayan Slim, Jad Slim, Amer Sharaf and Sarmad Tanveer

Level: Beginner/Intermediate

Duration: 14 hours 14 minutes

User Review: 4.5/5

No. of Reviews: 1654

Price: $46.2

Conclusion

Pytorch has emerged as one of the popular choices amongst engineers for building deep learning models. This is because it offers greater flexibility and user friendly with multiple built-in support for well-optimized hardware such as GPUs. Using Pytorch, developing sophisticated deep learning models for solving complex deep learning has become more accessible in a production environment.

Currently, Pytorch skills are most in-demand in the industry, and it is evident from the average base pay of a machine learning engineer starting at $114,864 and $190,000 for the most advanced professionals. Therefore, the aspirants or experienced professionals aiming for a career in the artificial intelligence industry should upskill themselves with Pytorch skills. Thus, this article presented some of the top trending online courses for Pytorch that can add value to the profiles to achieve lucrative career opportunities in the industry.

 

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Kaushik Das

Kaushik is an artificial intelligence researcher and a data scientist with expertise in medical image processing, intelligent automation, computer vision, and big data engineering—a technical and a scientific writer by passion. He is also passionate about sports, photography, traveling, and exploring new technologies.

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