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PyTorch
Learn Deep Learning with PyTorch – Build Neural Networks and AI Systems with Python and EasePreview PyTorch course
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A digit recognizer using convolutional neural networks (CNNs)
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A sentiment analysis model using LSTM for text data
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A custom image classifier deployed with TorchScript
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Work with tensors, datasets, and DataLoaders
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Use GPU acceleration and batch processing
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Build custom neural networks using
nn.Module -
Train and validate models with proper loss functions and optimizers
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Track metrics and prevent overfitting with early stopping and regularization
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Save, load, and deploy PyTorch models in production environments
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Beginners in deep learning seeking a practical and friendly start
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Python developers expanding into AI/ML
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Data scientists working with neural networks or NLP
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Computer vision and NLP enthusiasts
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Researchers moving from TensorFlow to PyTorch
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Start with the Basics – Tensors, gradients, and simple models
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Code Along – Implement networks step-by-step in notebooks or scripts
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Practice with Real Data – Use MNIST, IMDB, CIFAR, and custom datasets
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Visualize Training – Use matplotlib, TensorBoard, or Weights & Biases
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Experiment Often – Change architectures, learning rates, and optimizers
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Use GPU Early – Learn to manage CUDA devices and memory
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Deploy Your Work – Save models using TorchScript or ONNX for production
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
By the end of this course, you will be able to:
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Understand and manipulate tensors and perform GPU-accelerated computations
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Build deep learning models from scratch using PyTorch
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Apply convolutional, recurrent, and transformer-based architectures
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Train, tune, and evaluate models using loss functions and optimizers
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Work with datasets and loaders efficiently
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Save, reload, and export models for inference and deployment
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Use transfer learning and pretrained models for rapid prototyping
Course Syllabus
Module 1: Introduction to PyTorch and Deep Learning
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What is PyTorch?
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PyTorch vs TensorFlow
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Installing and Setting Up the Environment
Module 2: Tensor Operations and Autograd
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Tensor Creation, Indexing, and Math
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Gradients and Autograd
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Working with GPUs (CUDA)
Module 3: Datasets and DataLoaders
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Loading MNIST, CIFAR, IMDB, and Custom Datasets
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Using
torch.utils.data.DatasetandDataLoader -
Data Augmentation and Normalization
Module 4: Neural Network Basics
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Building Models with
nn.Module -
Layers: Linear, ReLU, Softmax
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Forward and Backward Passes
Module 5: Training a Model
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Loss Functions and Optimizers
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Accuracy, Precision, and F1 Score
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Epochs, Batching, and Logging
Module 6: CNNs and Image Classification
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Convolutional Layers and MaxPooling
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Training CNNs on MNIST/CIFAR
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Visualizing Filters and Activations
Module 7: RNNs and NLP Applications
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Recurrent Networks and LSTMs
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Embedding Layers
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Sentiment Classification Project
Module 8: Transfer Learning and Pretrained Models
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Using torchvision.models
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Fine-Tuning vs Feature Extraction
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Applying ResNet, VGG, BERT
Module 9: Saving and Deploying Models
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Saving State Dictionaries
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TorchScript and ONNX Export
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Real-Time Inference in API
Module 10: Advanced Topics and Projects
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Transformers and Self-Attention
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Time Series Forecasting with LSTM
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Image Captioning and GANs
Module 11: PyTorch Interview Questions & Answers
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Concepts and APIs
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Training and Debugging
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Deployment and Optimization
Upon successful completion of the course, learners will receive a Certificate of Completion from Uplatz, validating their expertise in PyTorch-based deep learning. This credential enhances your profile for roles in AI development, machine learning engineering, and research-driven analytics.
PyTorch is in high demand across academic, startup, and enterprise sectors. With this course, you'll be ready for roles like:
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Deep Learning Engineer
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AI Researcher
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Machine Learning Developer
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Computer Vision Engineer
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NLP Engineer
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What is PyTorch and why is it popular?
Answer: PyTorch is a deep learning framework known for its dynamic computation graph and Pythonic interface. It’s popular due to ease of use, flexibility, and seamless integration with Python tools and libraries. -
What is the difference between
torch.Tensorandtorch.nn.Parameter?
Answer:Tensoris the core data structure for computations, whileParameteris a Tensor that is automatically registered as a trainable weight when inside annn.Module. -
What is autograd in PyTorch?
Answer: Autograd is PyTorch’s automatic differentiation engine. It records operations on tensors and computes gradients automatically for backpropagation. -
How does PyTorch handle GPU computations?
Answer: Tensors and models can be moved to CUDA-enabled GPUs using.to(device)or.cuda(). PyTorch abstracts GPU memory management and kernel operations. -
What is the purpose of
DataLoader?
Answer:DataLoaderprovides an efficient way to load datasets in batches with features like shuffling, parallelism, and prefetching. It improves training speed and memory efficiency. -
How do you prevent overfitting in PyTorch models?
Answer: Use techniques like dropout layers, early stopping, weight decay (L2 regularization), and data augmentation. -
What are the steps to save and load models in PyTorch?
Answer: Save usingtorch.save(model.state_dict())and load withmodel.load_state_dict(torch.load(PATH)). This helps reuse trained models for inference. -
What is the difference between
model.eval()andmodel.train()?
Answer:model.train()enables dropout and batch normalization;model.eval()disables them for consistent evaluation. -
Can PyTorch be used for deployment?
Answer: Yes. Models can be exported with TorchScript or ONNX and deployed in production environments using C++, REST APIs, or edge devices. -
What are common use cases for PyTorch?
Answer:-
Image classification and object detection
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Natural Language Processing (NLP)
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Time-series forecasting
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GANs and generative modeling
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Reinforcement learning
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