YoVDO

Training Neural Networks - Part II - Lecture 11

Offered By: University of Central Florida via YouTube

Tags

Neural Networks Courses Deep Learning Courses Computer Vision Courses Momentum Courses Overfitting Courses Regularization Courses Loss Functions Courses

Course Description

Overview

Delve into the intricacies of training neural networks in this comprehensive lecture from the University of Central Florida's Computer Vision course. Explore essential concepts such as loss functions, learning rates, momentum, and mini-batch training. Gain valuable insights into overfitting and learn effective strategies to mitigate it, including regularization techniques and dropout. This in-depth session, part of a broader curriculum covering mathematical preliminaries, image processing, deep learning, and various computer vision tasks, equips students with advanced knowledge in neural network optimization for improved performance in AI and machine learning applications.

Syllabus

Intro
Loss Function
Learning Rate
Loss
Momentum
Learning
MiniMatch Training
Insights
Overfitting
Avoiding Overfitting
Regularization Regulation
Dropout


Taught by

UCF CRCV

Tags

Related Courses

Data Analysis and Visualization
Georgia Institute of Technology via Udacity Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
DeepLearning.AI via Coursera 機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations
National Taiwan University via Coursera Data Science: Machine Learning
Harvard University via edX Art and Science of Machine Learning auf Deutsch
Google Cloud via Coursera