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]
Deep Learning Fundamentals 2h
Lesson Objectives- Identify the differences between a shallow and multi-layer (dense) neural network
- Implement forward propagation in Python
- Identify different types of activation functions in Python
- Implement an activation in Python
Tensors and Autograd in PyTorch 2h
Lesson Objectives- Create tensors with PyTorch
- Learn how to control gradient tracking in tensors
- Convert between tensors and NumPy arrays
- Perform indexing, slicing, reshaping, concatenation/stacking, broadcasting core tensor operations
Building Neural Networks with nn.Sequential 2h
Lesson Objectives- Build neural networks using nn.Sequential and nn.Linear layers
- Prepare tabular data with normalization and categorical encoding
- Explore model parameters and experiment with activation functions
- Split data into training and testing sets properly
- Compute loss functions for regression tasks
Training Neural Networks 2h
Lesson Objectives- Build deep neural networks with multiple hidden layers to capture complex patterns in data
- Apply dropout layers to prevent overfitting and improve model generalization
- Implement batch normalization to stabilize training and enable faster convergence
- Diagnose overfitting and underfitting by analyzing training and validation curves
- Use validation sets to monitor model performance on unseen data during training
- Implement early stopping to automatically halt training when validation performance plateaus
Deep Networks and Regularization 2h
Lesson Objectives- Build deep neural networks with multiple hidden layers to capture complex patterns in data
- Use validation sets to monitor model performance on unseen data during training
- Diagnose overfitting and underfitting by analyzing training and validation curves
- Implement batch normalization to stabilize training and enable faster convergence
- Apply dropout layers to prevent overfitting and improve model generalization
- Implement early stopping to automatically halt training when validation performance plateaus
Guided Project: Predicting Listing Gains in the Indian IPO Market Using PyTorch 2h
Lesson Objectives- Implement proper three-way data splitting with stratification and scaling methodology
- Build and train a classification model using PyTorch's Sequential API with advanced regularization techniques
- Apply batch normalization, dropout, and early stopping to prevent overfitting
- Evaluate model performance using confusion matrices, precision, recall, and business-focused metrics
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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