Course overview
Deep learning is a discipline in artificial intelligence that has recently garnered a lot of interest. It’s used to solve complex problems in various fields such as computer vision, natural language processing, robotics, and others that might be difficult to solve using traditional machine learning methods.
In this course, you’ll start with the fundamentals of deep learning and PyTorch tensors, then advance to professional-grade techniques including proper data methodology, advanced regularization, and comprehensive evaluation practices. You’ll learn to build robust models that generalize well to new data using batch normalization, dropout, and early stopping.
Best of all, you’ll learn by doing – you’ll practice and get feedback directly in the browser. At the end of the course, you’ll apply your advanced skills to build a regularized deep neural network that predicts IPO listing gains with sophisticated evaluation techniques.
Key skills
- Explaining the major concepts and terminology used in deep learning and PyTorch tensor operations
- Implementing proper three-way data splitting with stratification and scaling methodology
- Building and evaluating deep learning regression and classification models using PyTorch's Sequential API with advanced regularization
- Applying batch normalization, dropout, and early stopping to prevent overfitting and improve generalization
- Building a robust deep neural network to predict IPO listing gains with comprehensive business-focused evaluation
Course outline
Introduction to Deep Learning in PyTorch [6 lessons]
Projects in this course
Predicting Listing Gains in the Indian IPO Market Using PyTorch
For this project, you’ll work as a data scientist for an investment firm analyzing the Indian IPO market. You’ll build a deep learning model using PyTorch to predict listing gains, applying skills in data exploration, preprocessing, advanced regularization, and comprehensive evaluation.
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