Learn Ai
PyTorch
Learn how to use Techpratham to build and deploy cutting-edge deep learning and AI models with PyTorch. Use PyTorch to learn how to do computer vision, natural language processing, and reinforcement learning in a real-world setting.
5/5(4,890 Reviews)
Level
Advanced
Duration
8 weeks
What is PyTorch?
Techpratham's PyTorch training lets you learn about deep learning by actually using PyTorch's flexible and dynamic computation graph. The course includes neural networks, computer vision, natural language processing (NLP), and putting models into use. Students will work with real data sets and use the most up-to-date AI models. By the end, you'll know how to use PyTorch to make, improve, and deploy AI solutions that can grow.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
PyTorch Course Curriculum
.
Introduction to PyTorch
Section 1
Learn the basics of PyTorch and its ecosystem.
PyTorch Overview
Installation & Setup
Tensors & Operations
Autograd Basics
Autograd Basics
.
PyTorch Installation & Setup
Section 2
Set up PyTorch in local and cloud environments with GPU/TPU support, IDE integration, and dependency management.
Installation Methods
CUDA & GPU Setup
Anaconda & Jupyter
Cloud Platforms
.
Tensors and Autograd
Section 3
Learn about tensors, operations, and PyTorch’s automatic differentiation (autograd) system for gradient computation.
Tensor Operations
Indexing & Slicing
Autograd Basics
Gradient Descent
.
Building Neural Networks in PyTorch
Section 4
Explore PyTorch modules for creating, training, and evaluating deep learning models with ease.
nn.Module Class
Activation Functions
Forward & Backward Pass
Loss Functions
.
Optimization and Training Loops
Section 5
Master training loops, optimization strategies, and hyperparameter tuning for building efficient models.
Optimizers (SGD, Adam)
Learning Rate Scheduling
Backpropagation
Training Best Practices
.
Computer Vision with PyTorch
Section 6
Implement convolutional neural networks (CNNs) for image classification, detection, and transfer learning.
Convolution Layers
Pooling Layers
Image Augmentation
Transfer Learning
.
Natural Language Processing (NLP) with PyTorch
Section 7
Build NLP applications using embeddings, RNNs, LSTMs, GRUs, and transformers with PyTorch.
Tokenization
Word Embeddings
RNN / LSTM / GRU
Transformers
.
PyTorch Lightning & Training Abstractions
Section 8
Use PyTorch Lightning to simplify model training, reduce boilerplate, and improve reproducibility.
Lightning Basics
Training Modules
Callbacks & Logging
Experiment Tracking
.
Model Deployment with PyTorch
Section 9
Deploy PyTorch models in production environments, including mobile, web, and cloud platforms.
TorchScript
ONNX Export
PyTorch Mobile
Cloud Deployment
.
Distributed Training & Performance Optimization
Section 10
Learn techniques for scaling PyTorch models across GPUs and clusters while optimizing performance.
Data Parallelism
Distributed Data Parallel (DDP)
Mixed Precision Training
Model Quantization
Key Projects
PyTorch
Handwritten Digit Recognition with CNNs
Use the MNIST dataset to teach a convolutional neural network to read handwritten numbers. Use TorchServe to train, validate, and tune hyperparameters, and then deploy the model.
Movie Review Sentiment Analysis with RNNs
Use embeddings and LSTMs to make an NLP model that can tell if IMDB movie reviews are good or bad. Train the model on sequence data and then make it available as an API for real-time predictions.
Real-Time Object Detection with YOLO/PyTorch
Use YOLO or Faster R-CNN in PyTorch to set up a system that can find objects in real time. Use an open dataset to train the model, then tweak it and use it for live video or camera input applications.
PyTorch – Associate Training Program
Category
Associate
Exam Name:
PyTorch – Associate
Exam Code:
N.A.
Exam Duration:
N.A.
Exam Format:
N.A.
Passing Score:
N.A.
Our Testimonials
Frequently Asked Questions (FAQs)
Why should I learn PyTorch if TensorFlow is already popular?
Researchers and prototypers often choose PyTorch because it is flexible and can run graphs in real time. PyTorch is used to make a lot of new AI models and research papers, so it's important for people who want to stay ahead in deep learning to learn it.
What is PyTorch?
PyTorch is an open-source deep learning framework developed by Facebook’s AI Research Lab. It is popular for its dynamic computation graph and ease of debugging. TechPratham offers comprehensive PyTorch training with practical projects to strengthen concepts.
Why should I learn PyTorch?
PyTorch is widely used in research, academia, and industry for building advanced AI models. Its simplicity makes it ideal for experimentation and innovation. TechPratham helps learners gain hands-on experience with real-world PyTorch applications.
Is PyTorch better than TensorFlow?
PyTorch is preferred for research because of its flexibility, while TensorFlow is strong in deployment and scalability. Both frameworks dominate the AI ecosystem. TechPratham covers both PyTorch and TensorFlow, ensuring learners understand their strengths and use cases.
What are the main applications of PyTorch?
PyTorch is used in computer vision, natural language processing, reinforcement learning, and recommendation systems. Its ecosystem includes TorchVision and TorchText for specific domains. TechPratham provides domain-based projects to master these applications.
Do I need strong programming skills to use PyTorch?
Yes, a solid understanding of Python is required since PyTorch APIs are Python-centric. However, its intuitive syntax makes learning smoother compared to other frameworks. TechPratham ensures learners build coding confidence while learning PyTorch.
How does PyTorch support GPU computing?
PyTorch has native CUDA support, allowing models to run efficiently on NVIDIA GPUs. This accelerates training for large-scale deep learning projects. TechPratham provides training with GPU-based labs so learners can practice in real-time environments.
Can PyTorch be used in production?
Yes, PyTorch now supports deployment through TorchServe, ONNX, and integration with cloud platforms. This makes it production-ready for AI applications. TechPratham covers deployment workflows to prepare learners for industry projects.
What industries use PyTorch the most?
Industries like healthcare, finance, automotive, retail, and robotics use PyTorch for AI-driven solutions. Its adoption is increasing in both startups and enterprises. TechPratham trains learners with case studies from these industries.
What career opportunities are available after learning PyTorch?
Roles include Deep Learning Engineer, NLP Engineer, Computer Vision Engineer, and AI Researcher. PyTorch skills are highly valued in AI-focused companies. TechPratham prepares learners with projects and placement guidance for these roles.
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