Foundations of Deep Learning with PyTorch: From Tensors to Real-World Models

Software engineers or developers with basic Python programming skills

Aspiring or early-career Machine Learning engineers

Data scientists looking to strengthen their deep learning foundation

AI enthusiasts who understand ML concepts (like supervised learning, overfitting, optimization).

Professionals aiming to transition into AI/ML roles

Students or researchers who want practical hands-on experience with PyTorch.

Teams or individuals tasked with building, training, or deploying ML models

Anyone who has basic knowledge of vectors, matrices, and calculus (helpful but not mandatory)

Cloud familiarity (e.g., using Colab or cloud notebooks) is beneficial.

• Understand the core components of the PyTorch framework, including tensors, autograd, and neural network modules.
• Construct and train neural networks using PyTorch’s modular API for various deep learning tasks.
• Load, preprocess, and augment datasets using PyTorch utilities for image and text data.
• Apply optimization techniques such as backpropagation, regularization, and learning rate tuning to improve model performance
• Build and evaluate models for real-world applications in computer vision (e.g., CNNs) and natural language processing (e.g., RNNs/LSTMs).
• Fine-tune pretrained NLP models (e.g., BERT) and evaluate on domain-specific datasets.
• Utilize transfer learning and pretrained models to accelerate development on custom datasets.
• "Deploy PyTorch models for inference using TorchScript or ONNX in production-ready formats.
• Deploy trained models as REST APIs using FastAPI, TorchScript, and containerization techniques.

• Welcome and Setup 
• Tensors and Operations
• Autograd and Computational Graphs
• PyTorch Modules & Custom Models
• Training Loop Mechanics

• Data Loading and Preprocessing
• Training Best Practices
• Model Evaluation & Metrics
• Introduction to Computer Vision with CNNs
• Saving, Loading, and Deploying Models
• End if Day Challenge - Mini Project

• Intro to NLP with PyTorch
• Transformers with HuggingFace
• Transfer Learning & Fine-Tuning
• Advanced Optimization Techniques
• Model Deployment and Production Readiness
• Responsible NLP

• Basic proficiency in Python (familiarity with functions, loops, classes, list comprehensions, etc.)
• Ability to work with libraries like NumPy or Pandas is helpful, but not mandatory.
• Basic knowledge of ML concepts such as: Features and labels, Training vs. testing, Loss functions and model evaluation,
• No prior experience with deep learning required, but familiarity is a plus.