AI News, Advanced Topics in Neural Networks
- On 26. august 2019
- By Read More
Advanced Topics in Neural Networks
As you have likely come to realize from your own adventures with neural networks, and possibly from other articles and research literature, the learning rate is a very important part of neural network training.
A smaller learning rate results in slower learning, and whilst convergence is possible, it may only occur after an inordinate number of epochs, which is computationally inefficient.
Clearly, there is a sweet spot in between that is optimal for a given neural architecture — this is unique and is a function of the neural loss surface.
In addition to this temporal dependence, the sweet spot is also spatially dependent — since certain locations on the loss surface may have extremely sharp or shallow gradients — which further complicates matters.
The only way to achieve better results is to use a dynamic learning rate that tries to leverage the spatial and temporal variations in the optimal learning rate.
The main use of cyclical learning rates is to escape local extreme points, especially sharp local minima (overfitting).
After 30 iterations, the learning rate scheduler resets the learning rate to the same value as epoch 1, and then the learning rate scheduler repeats the same exponentially decay.
This idea is similar to the cyclical learning rate except for the learning rate graph typically looks more like a sawtooth wave rather than something symmetric and cyclic.
- On 20. september 2021
Regression forecasting and predicting - Practical Machine Learning Tutorial with Python p.5
In this video, make sure you define the X's like so. I flipped the last two lines by mistake: X = np.array(df.drop(['label'],1)) X = preprocessing.scale(X) X_lately ...
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
Python Data Science Training : ** This Edureka Video on Time Series Analysis n Python will give you all the information you ..
32. ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Gilbert Strang View the complete course: ...
Kaggle Camera Model Identification (1-2 places) — Artur Fattakhov, Ilya Kibardin, Dmitriy Abulkhanov
Artur Fattakhov, Ilya Kibardin and Dmitriy Abulkhanov share their winner's solutions of Kaggle Camera Model Identification. In this competition, Kagglers ...
Intro to Azure ML: Cleaning & Summarizing Data
Let's understand the aggregate behavior of our features further by looking at summary statistics. Azure Machine Learning gives us easy access to mean, median, ...
Ruby Conf 2013 - Test Driven Neural Networks with Ruby by Matthew Kirk
Neural networks are an excellent way of mapping past observations to a functional model. Many researchers have been able to build tools to recognize ...
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 6 - CNNs and Deep Q Learning
Professor Emma Brunskill, Stanford University Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for ..
Caffeine and Adenosine Receptors
We are all familiar with caffeine's stimulatory effects, but how does it actually work? Check out this episode of Medicurio to learn more about the world's most ...
Sir Roger Penrose: Fashion, Faith, and Fantasy in the New Physics of the Universe
What can fashionable ideas, blind faith, or pure fantasy possibly have to do with the scientific quest to understand the universe? Surely, theoretical physicists are ...
Introduction to TensorFlow (Cloud Next '18)
In this session, you'll learn how you can easily get started with coding for Machine Learning and AI with TensorFlow. We'll cover the basics of Machine Learning, ...