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Related lectures (32)
PyTorch and Convolutional Networks
Covers PyTorch tensor data structure and training a CNN to classify images.
Principal Component Analysis: Eigenfaces
Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.
Deep Learning: Convolutional Neural Networks
Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.
Machine Learning Basics
Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.
Spectral Estimation Methods
Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.
Logistic Regression: Fundamentals and Applications
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Memory Management & Crash Programs
Covers memory management for engineers, focusing on crash programs related to memory access errors.
Logistic Regression: Vegetation Prediction
Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.
Machine Learning: Supervised and Unsupervised Learning Techniques
Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Deep Learning: Data, Models, and Challenges
Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.
Data Representations and Processing in Machine Learning
Covers data representations and processing techniques essential for effective machine learning algorithms.
Introduction to Machine Learning: Supervised Learning
Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.
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