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PyTorch
4.5/5

Level

Advanced

Duration

8 weeks

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What is PyTorch?

The PyTorch Certification Training program is a comprehensive, hands-on course designed to equip learners with practical deep learning and neural network development skills using PyTorch. Delivered through Jast Tech, this course covers everything from tensor operations and automatic differentiation to building, training, and optimizing deep neural networks for real-world applications. Learners will work with convolutional neural networks, recurrent networks, and transformers while gaining a strong understanding of model training pipelines, loss functions, optimizers, and GPU acceleration. The course emphasizes practical implementation through industry-aligned projects in computer vision and natural language processing. Advanced topics such as transfer learning, model evaluation, performance tuning, and deployment using TorchScript and ONNX are also included. By the end of this program, participants will be able to design scalable deep learning solutions, debug and optimize models efficiently, and confidently apply PyTorch in research and enterprise environments. This training is ideal for aspiring data scientists, machine learning engineers, and AI professionals seeking job-ready PyTorch expertise.

Job Roles You Can Achieve

After completing this course

  • Solutions Architect
  • Technical Consultant
  • Implementation Specialist
  • System Administrator
  • IT Professional

PyTorch Curriculum

1
Module 01

Introduction to PyTorch & Deep Learning

This module introduces PyTorch fundamentals and explains how it differs from other deep learning frameworks.

Overview of deep learning frameworks
PyTorch ecosystem and use cases
Installing PyTorch and environment setup
2
Module 02

Tensors and Mathematical Operations

Learners gain hands-on experience with PyTorch tensors and core numerical computations.

Tensor creation and manipulation
Broadcasting and indexing
Tensor operations on CPU and GPU
3
Module 03

Autograd and Computation Graphs

This module explains how PyTorch computes gradients dynamically using computation graphs.

Automatic differentiation
Gradients and backward propagation
Controlling gradient flow
4
Module 04

Building Neural Networks

Focuses on constructing neural networks using PyTorch’s modular API.

nn.Module and model structure
Layers, activation functions
Forward pass design
5
Module 05

Training and Optimization

Learners understand how to train models efficiently and monitor performance.

Loss functions
Optimizers (SGD, Adam, RMSprop)
Training loops and validation

Related Courses

Training Roadmap

Seven intentional milestones — from first session to dream job.

Onboarding

01
  • Meet your industry mentor
  • Define your goals
  • Skill gap assessment

Core Learning

02
  • Live interactive classes
  • AI-curated content
  • Recorded sessions

Hands-on Practice

03
  • Weekly assignments
  • MCQ evaluations
  • Module quizzes

Real Projects

04
  • 3 live industry projects
  • Portfolio building
  • Case studies

Mentorship

05
  • 1:1 doubt sessions
  • Peer collaboration
  • Expert feedback

Certification

06
  • Exam preparation
  • Practice dumps
  • Industry-recognised certificate

Career Launch

07
  • Resume crafting
  • Mock interviews
  • Job placement support

Key Projects

01

Image Classification System Using CNNs

This project involves developing an image classification system using PyTorch convolutional neural networks. The model processes image datasets, applies data augmentation, trains CNN architectures, and evaluates performa...
02

Sentiment Analysis Using Recurrent Neural Networks

This project focuses on building a sentiment analysis model using RNN and LSTM architectures in PyTorch. It processes textual data, converts text into embeddings, trains sequence models, and predicts sentiment polarity....
03

Transfer Learning-Based Object Recognition System

This project implements an object recognition system using pretrained deep learning models and transfer learning techniques in PyTorch. It fine-tunes existing architectures on custom datasets, optimizes performance, and...

Available Course Schedules

Select a schedule that works best for you

Weekend

Starts

04 Jul 2026

Time

09:30 AM – 12:30 PM

Duration

8 weeks

Weekdays

Starts

06 Jul 2026

Time

07:00 AM – 09:00 AM

Duration

8 weeks

Weekend

Starts

11 Jul 2026

Time

02:00 PM – 05:00 PM

Duration

8 weeks

Weekdays

Starts

13 Jul 2026

Time

08:00 PM – 10:00 PM

Duration

8 weeks

Need a custom schedule?

Our team will craft the perfect batch for you.

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Real-world experience

Review from Tejas Kumar

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Comprehensive Curriculum

JastTech Advantage

Well-structured, up-to-date curriculum designed by industry experts to build strong fundamentals and advanced knowledge.

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Outdated or incomplete curriculum that may not cover current industry needs.

Practical Hands-on Learning

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Extensive practical sessions, live demos, and hands-on exercises to ensure real learning.

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Limited practical exposure with theory-heavy teaching approach.

Expert Instructors

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Learn from certified professionals with years of industry experience and teaching expertise.

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Instructors with limited industry experience or practical knowledge.

Real-world Projects

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Work on real-world projects that enhance problem-solving skills and build a strong portfolio.

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Lack of real-world projects or unrealistic practice examples.

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Certification

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Industry-recognized certificate that validates your skills and enhances your career opportunities.

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Lifetime Access

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Lifetime access to course content, recordings, and resources even after completing the course.

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Limited access duration with extra charges for resources.

Affordable Fees

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High-quality training at affordable prices with no hidden costs and flexible payment options.

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High course fees with hidden charges and no flexibility.

Certification Details

PyTorch – Associate

  • Exam Name

    PyTorch – Associate

  • Duration

    130 minutes

  • Format

    Multiple Choice & Multi-Response

  • Passing Score

    720 (Scale: 100–1000)

  • Level

    Associate

Certificate of Completion

Prepare

Top Interview Questions

Curated questions with expert answers to help you ace your next interview.

Q1. What is PyTorch and why is it popular?

PyTorch is an open-source deep learning framework known for its dynamic computation graph, flexibility, and strong research and production support.

Q2. What is Autograd in PyTorch?

Autograd is PyTorch’s automatic differentiation engine that computes gradients for tensor operations during backpropagation.

Q3. Difference between PyTorch and TensorFlow?

PyTorch uses dynamic graphs, making debugging easier, while TensorFlow traditionally used static graphs (though this has evolved).

Q4. What is nn.Module?

nn.Module is the base class for all neural network models in PyTorch, enabling modular and reusable model design.

Q5. How do you deploy a PyTorch model?

PyTorch models can be deployed using TorchScript, ONNX, or by integrating them into APIs and production pipelines.

Support

Frequently Asked FAQs

Can't find what you're looking for? Reach out to our support team anytime.

Q1. Who should take this PyTorch course?

This course is ideal for students, data scientists, ML engineers, and AI professionals with basic Python knowledge.

Q2. Are prerequisites required?

Basic Python programming and fundamental machine learning concepts are recommended.

Q3. Does this course include real-world projects?

Yes, the course includes industry-oriented projects to ensure practical exposure.

Q4. Will I learn GPU acceleration with PyTorch?

Yes, the course covers CUDA usage and GPU-based model training.

Q5. Is this course suitable for job preparation?

Absolutely. The curriculum is aligned with industry requirements and interview expectations.

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