AI News, Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters
- On Saturday, October 13, 2018
- By Read More
Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters
It is hence a good method for meta-optimizing a neural network which is itself an optimisation problem: tuning a neural network uses gradient descent methods, and tuning the hyperparameters needs to be done differently since gradient descent can’t apply.
Therefore, Hyperopt can be useful not only for tuning hyperparameters such as the learning rate, but also to tune more fancy parameters in a flexible way, such as changing the number of layers of certain types, or the number of neurons in a layer, or even the type of layer to use at a certain place in the network given an array of choices, each with nested tunable hyperparameters.
parameter is defined with a certain uniformrange or else a probability distribution, such as: There is also a few quantized versions of those functions, which rounds the generated values at each step of “q”: It is also possible to use a “choice” which can lead to hyperparameter nesting: Visualisations of the parameters for probability distributions can be found below.
That’s because we want to observe changes in the learning rate according to changing it with multiplications rather than additions, e.g.: when adjusting the learning rate, we’ll want to try to divide it or multiply it by 2 rather than adding and substracting a finite value.
In : Here are the space and results of the 3 first trials (out of a total of 1000): What interests us most is the ‘result’ key of each trial(here, we show 7): Note that the optimization could be parallelized by using MongoDB and storing the trials’ state here.
- On Thursday, March 21, 2019
Hyperparameter Tuning and Cross Validation to Decision Tree classifier (Machine learning by Python)
In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. The traditional ...
Hyperparameter Optimization - The Math of Intelligence #7
Hyperparameters are the magic numbers of machine learning. We're going to learn how to find them in a more intelligent way than just trial-and-error. We'll go ...
3. Bayesian Optimization of Hyper Parameters
Video from Coursera - University of Toronto - Course: Neural Networks for Machine Learning:
Lecture 6 | Training Neural Networks I
In Lecture 6 we discuss many practical issues for training modern neural networks. We discuss different activation functions, the importance of data ...
Lecture 16: Dynamic Neural Networks for Question Answering
Lecture 16 addresses the question ""Can all NLP tasks be seen as question answering problems?"". Key phrases: Coreference Resolution, Dynamic Memory ...
Predicting the Winning Team with Machine Learning
Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn ...
Optimization II (Genetic Algorithms)
Artificial Intelligence by Prof. Deepak Khemani,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit
Lecture 18: Tackling the Limits of Deep Learning for NLP
Lecture 18 looks at tackling the limits of deep learning for NLP followed by a few presentations.
TensorFlow in 5 Minutes (tutorial)
This video is all about building a handwritten digit image classifier in Python in under 40 lines of code (not including spaces and comments). We'll use the ...
Lecture 2 | Image Classification
Lecture 2 formalizes the problem of image classification. We discuss the inherent difficulties of image classification, and introduce data-driven approaches.