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PyTorch Corporate Training

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Our PyTorch Corporate Training Program covers foundational concepts, hands-on coding, and advanced concepts like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to help participants master PyTorch for seamless implementation of deep learning solutions.

The course modules are structured to give an empirical value and understanding to the candidates. However, all course modules are highly customizable and can be structured to suit the requirements of your organization.

Course Outcome

Participants will gain a solid understanding of PyTorch, including its fundamentals, how to set up the environment, and navigate its documentation.
The training covers essential concepts in deep learning, from basic operations with tensors to more advanced topics like autograd (automatic differentiation).
Participants will learn to create neural network architectures, define loss functions and optimizers, and train models using PyTorch.
The training delves into Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), providing insights into their architectures, applications, and even transfer learning with pre-trained models.
Participants will understand how PyTorch can be applied to NLP tasks, including tokenization, embeddings, and a hands-on project involving sentiment analysis.
The training includes advanced topics like working with custom datasets and DataLoaders, model interpretability, visualization techniques, and distributed training.

Why Mazenet?

  • Expert Faculty

    Our Faculty comprises of 300+ SMEs with many years of experience. All our trainers possess a minimum of 8+ years of experience.

  • Proven Track Record

    We have served over 200+ global corporate clients, consistently maintaining a 99% success rate in meeting training objectives for 300+ technologies with quick turnaround time.

  • Blended Learning

    We provide course content over any platform that our clients prefer. You can choose an exclusive platform or a combination of ILT, VILT, and DLP.

  • Learning Paths

    The learning paths are very defined with clear benchmarks. Quantitative assessments at regular intervals measure the success of the learning program.

  • Case Study

    We have amassed over 10,000 case studies to support training delivery. Candidates will be trained to work on any real-time business vertical immediately after the training.

  • 24*7 Global Availability

    We are equipped to conduct training on any day, date or time. We have delivered training pan India, Singapore, North America, Hong Kong, Egypt and Australia.

Key Features

  • Customized Training Modules

    Training programs are highly flexible with module customizations to suit the requirements of the business units.

  • Certification

    The training can be supplemented with appropriate certifications that are recognized across the industry.

  • Multi-language Support

    Course content can be delivered in English, Spanish, Japanese, Korean or any other language upon request.

  • Personalized Training Reports

    Candidates are assessed individually at regular intervals and are provided unique learning suggestions to suit their learning calibre.

  • Industry-Oriented Training

    Industry-oriented training, completing which, candidates can be immediately deployed for billable projects.

  • Diverse Training Platforms

    Choose from Instructor-Led Training, Virtual Instructor-Led Training, Digital Learning Platform and Blended Training platforms

Course Preview

  • Overview of PyTorch
  • Introduction to Deep Learning
  • Comparison with other Deep Learning Frameworks

  • Installing PyTorch and its Dependencies
  • Configuring GPU Support (if applicable)
  • Navigating the PyTorch Documentation

  • Tensors in PyTorch
  • Operations and Broadcasting
  • Autograd: Automatic Differentiation

  • Creating Neural Network Architectures
  • Defining Loss Functions and Optimizers
  • Training a Neural Network

  • Understanding Convolutional Layers
  • Building CNN Architectures in PyTorch
  • Transfer Learning with Pre-trained CNNs

  • Introduction to RNNs
  • Long Short-Term Memory (LSTM) Networks
  • Applications of RNNs in PyTorch

  • Tokenization and Embeddings
  • Building NLP Models with PyTorch
  • Hands-on Project: Sentiment Analysis

  • Custom Datasets and DataLoaders
  • Model Interpretability and Visualization
  • Distributed Training with PyTorch

  • Exporting PyTorch Models for Deployment
  • Using PyTorch Serve for Model Serving
  • Deploying PyTorch Models on Cloud Platforms

  • Participant Q&A Session with Experts
  • Final Hands-on Project
  • Certification Ceremony and Closing Remarks