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PyTorch

Dynamic Deep Learning Research and Production Deployment with PyTorch 45+ hours of hands-on training.

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PyTorch

45+

Hours

8

Modules

14

Topics

Beginner-Friendly

Level

New

Batches weekly

About PyTorch

Dynamic Deep Learning Research and Production Deployment with PyTorch

In this course, you will: Work with PyTorch tensors, broadcasting, and GPU-accelerated computations; Understand autograd and implement custom backward passes for novel architectures; Build neural networks using nn.Module with clean, modular design patterns.

What This Training Covers

The PyTorch programme at Tutorsbot spans 45+ hours across 8 structured modules. Every module is built around hands-on projects and real-world scenarios — not slide-heavy theory. Your instructor walks you through each concept with live demonstrations, code reviews, and practical exercises so you can apply what you learn from day one. The curriculum is aligned with current Technology Training industry expectations and hiring patterns.

Enrollment & Training Quality

PyTorch is available in 2 flexible learning modes — choose online live classes, classroom, hybrid, self-paced, or one-on-one depending on your schedule. Every batch is limited in size to ensure each learner receives personal attention, code-level feedback, and doubt resolution. Career support and certification are included with every enrolment. Tutorsbot instructors are working professionals who teach from delivery experience, and the training standard stays consistent across all modes and batches.

Course Curriculum

8 modules · 14 topics · 45 hrs

01

PyTorch Tensors and Autograd

7 topics

  • Tensor creation — torch.tensor, torch.zeros, torch.randn, and torch.arange
  • Tensor operations — Arithmetic, matrix multiplication, and reduction functions
  • GPU tensors — Moving tensors to CUDA with .to(device) and .cuda()
  • Broadcasting rules — Implicit dimension expansion for compatible tensor shapes
  • Autograd fundamentals — requires_grad, grad_fn, and computation graph
  • Backward pass — loss.backward() and gradient accumulation in custom loops
  • Disabling gradients — torch.no_grad and detach for inference and data manipulation
02

Neural Network Module

7 topics

  • nn.Module base class — __init__, forward, and submodule registration
  • Common layers — Linear, Conv2d, BatchNorm2d, Dropout, and Embedding
  • Activation functions — ReLU, GELU, Sigmoid, Tanh, and when to use each
  • Loss functions — CrossEntropyLoss, MSELoss, BCEWithLogitsLoss, and custom losses
  • Parameter and buffer registration — nn.Parameter vs register_buffer
  • Module inspection — named_parameters, children, and modules iteration
  • Sequential and ModuleList for building blocks and dynamic architecture assembly
03

Training Loops and Data Loading

0 topics

5 more modules available

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Same curriculum & certification across all formats. Updated Apr 2026.

✓ 7-day refund guarantee✓ Same certificate for all formats✓ Lifetime access to recordings

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What You Get After Completion

Every graduate receives a verified certificate, a portfolio of real projects, and dedicated career support.

Verified Certificate

Digitally signed with a permanent shareable link — not just for attendance.

LinkedIn-importable·Permanent URL·PDF download

Project Portfolio

Real, deployable projects reviewed by your instructor — ready for interviews.

Instructor-reviewed·GitHub-hosted·Interview-ready

Career Support

Résumé review, mock interviews, LinkedIn guidance, and employer introductions.

1-on-1 coaching·Mock interviews·Employer connect

Meet Your Instructor

Every PyTorch batch is led by a practitioner who teaches from production experience, not textbooks.

Industry Expert

Tutorsbot Assigned

Senior Technology Professional

10+ yrs experience

Senior professionals with substantial hands-on delivery experience at top companies, bringing real-world projects, industry insights, and best practices.

How We Teach

  • Concepts start with a real problem so theory lands in context
  • Projects reviewed the way a senior colleague reviews pull requests
  • Every topic includes the kind of questions you'll face in interviews
Hire Trained Talent

Hire PyTorch Talent from Tutorsbot

Companies hiring PyTorch talent from Tutorsbot receive pre-assessed profiles backed by project work, instructor review, and interview-ready candidates who can explain what they built and why.

Why hire from us

Project repositories with documented technical decisions

Assessment outcomes backed by instructor context

Candidate readiness shaped by interview-style practice

Project-based portfolios available

Frequently Asked Questions

Everything you need to know about PyTorch, answered by our training experts

1Who should take PyTorch?
This programme is ideal for Python developers and data scientists starting with deep learning and neural networks, ML researchers who need flexible model architectures and custom training loops, Engineers migrating from TensorFlow to PyTorch's Pythonic, dynamic graph API. Whether you are a fresh graduate looking for your first role, a working professional aiming for a promotion, or a career switcher exploring a new domain, this course meets you where you are with personalised learning paths.
2Does PyTorch include a certificate?
Absolutely. Upon successful completion of PyTorch, you will receive an industry-recognised certificate from Tutorsbot that you can share on LinkedIn, attach to your résumé, and present at job interviews. Our certificates are built to support hiring conversations across India and international teams.
3Is placement support included with PyTorch?
Yes. Every PyTorch enrolment includes our comprehensive placement support programme. This covers one-on-one résumé building, LinkedIn profile optimisation, mock interview sessions with industry experts, soft-skills workshops, and direct job referrals through our hiring network.
4How long does PyTorch take to complete?
The full course spans 45+ hours of instructor-led training, typically delivered over 6–10 weeks depending on the batch schedule you choose. Self-paced learners can progress faster, while weekend batches allow working professionals to learn without disrupting their current role.
5What is the mode of delivery for PyTorch?
PyTorch is available in multiple modes: Online Live (real-time classes with an instructor), Classroom (in-person at our training centres), Hybrid (mix of online and classroom), and Self-Paced (pre-recorded with mentor support). Every mode includes the same curriculum, certificate, and placement support. Choose what fits your schedule best.
6Can I get a free demo class for PyTorch?
Yes, Tutorsbot offers a complimentary demo session for every course. During the demo, you will meet your instructor, experience the teaching methodology, and get a detailed walkthrough of the course curriculum. This is a zero-obligation session. Join, evaluate, and then decide if the programme is right for you.
7What kind of projects will I work on in PyTorch?
Throughout PyTorch, you will work on 8+ real-world projects and case studies drawn from actual business scenarios. These projects are designed to build your portfolio and give you hands-on experience that you can showcase during job interviews. Our instructors review every project submission and provide personalised feedback.
8What if I miss a class?
We understand that life happens. If you miss a session, you can access the recorded video of the class within 24 hours. You can also join the next available batch for that specific module at no extra cost. Our support team will help you stay on track regardless of any interruptions.
9Is PyTorch worth it for experienced professionals?
Absolutely. Many of our learners are already working professionals who enrol to upskill, transition into specialised roles, or earn a recognised credential. The PyTorch curriculum includes advanced modules and real-world case studies that challenge experienced learners while filling practical gaps.
10What is the refund policy for PyTorch?
Tutorsbot offers a 7-day money-back guarantee on all courses. If you are not satisfied with the quality of training within the first 7 days, you can request a full refund, no questions asked. We are confident you will love the experience, but we want you to enrol risk-free.
11Do you offer corporate or group training?
Yes. Tutorsbot works with enterprises on custom corporate training programmes. For PyTorch, we offer dedicated batches, customised curriculum, flexible scheduling, team progress dashboards, and volume-based commercial options. Contact our corporate team for a tailored proposal.
12How are the instructors selected at Tutorsbot?
Every Tutorsbot instructor goes through a rigorous selection process. We require substantial hands-on industry experience, proven teaching ability, and strong communication skills. Our instructors bring real-world engineering and delivery insights directly into the classroom.
13Will I get lifetime access to PyTorch materials?
Yes. Once you enrol in PyTorch, you get lifetime access to all course materials, including recorded sessions, slides, code notebooks, assignments, and project files. As the curriculum gets updated, you automatically receive access to the latest content at no additional cost.
14Can I switch between batch timings?
Yes, Tutorsbot offers flexible batch transfers. If your current batch timing no longer works for you, simply reach out to our support team and we will move you to another batch: weekday mornings, evenings, or weekend batches, at no extra charge.
15What support do I get after completing the course?
Post-completion, you retain access to our alumni community, placement support for up to 12 months, and all updated course materials. Our career coaches continue to assist you with interview preparation, salary negotiation, and career guidance long after you finish the programme.

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