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SGD: Penalties
SGD: Penalties Contours of where the penalty is equal to 1 for the three penalties L1, L2 and elastic-net. All of the above are supported by SGDClassifier and SGDRegressor .
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Bayesian Priors and Regularization Penalties
Bayesian methods of performing machine learning offer several advantages over their counterparts, notably the ability to estimate uncertainty and the option to encode contextual knowledge as prior…
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Parameter Management
Once we have chosen an architecture and set our hyperparameters, we proceed to the training loop, where our goal is to find parameter values that minimize our loss function. After training, we will ne...
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A Winding Road to Parameter Efficiency
Deliberately Exploring Design Decisions for Parameter Efficient Finetuning (PEFT) with LoRA Good news: Using LoRA for Parameter Efficient Finetuning (PEFT) can be straightforward. With a simple strat...
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Parameter Constraints & Significance
Setting the values of one or more parameters for a GARCH model or applying constraints to the range of permissible values can be useful. Continue reading: Parameter Constraints & Significance
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Hyper-parameters in Action! Part II - Weight Initializers
This is the second post of my series on hyper-parameters. In this post, I will show you the importance of properly initializing the weights of your deep neural network. We will start with a naive…
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Regularization and Cross-Validation — How to choose the penalty value (lambda)
Regularization and Cross-Validation — How to choose the penalty value (lambda). Choosing the right hyperparameter values using Cross-Validation.
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Parameters
Section 4.3 Parameters I f a subroutine is a black box , then a parameter is something that provides a mechanism for passing information from the outside world into the box. Parameters are part of the...
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Common Mistakes in Hyper-Parameters Tuning
Starting from a given dataset, training a machine learning model implies the computation of a set of model parameters that minimizes/maximizes a given metric or optimization function. The optimum…
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Sensitivity Analysis: Optimization(Part2)
There are variety of approaches to playacting a sensitivity analysis. they’re additionally distinguished by the sort of sensitivity measure, be it supported variance decompositions, partial…
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L1 Penalty and Sparsity in Logistic Regression
L1 Penalty and Sparsity in Logistic Regression Comparison of the sparsity (percentage of zero coefficients) of solutions when L1, L2 and Elastic-Net penalty are used for different values of C. We can ...
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Tuning Parameters
If you have been reading my column here on Towards Data Science, you will know that I am on a mission. I wanted to count the number of cars passing my house using Computer Vision and Motion…
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Hyper-parameters in action!
This is the first of a series of posts aiming at presenting in a clear, concise and as much visual as possible fashion, some of the fundamental moving parts of training a neural network.
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Optimizing Model Parameters
Optimizing Model Parameters Now that we have a model and data it’s time to train, validate and test our model by optimizing its parameters on our data. Training a model is an iterative process; in eac...
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Learning Parameters, Part 0: Basic Stuff
This is an optional read for the 5 part series I wrote on learning parameters. In this post, you will find some basic stuff you’d need to understand my other blog posts on how deep neural networks…
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Optimization Overkill: How to Turn Good Code Bad
In the pursuit of a perfectly optimized program, developers can fall into a number of traps. These traps not only make code harder to read and debug but can also, paradoxically, make it perform worse....
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Risk Implications of Excessive Multiple Local Minima during Hyperparameter Tuning
Our Epistemological Limitation and Illusion of Knowledge 3D visualization with Matplotlib’s plot_trisurf: Produced by Michio Suginoo Excessive multiple local minima during hyperparameter tuning is a ...
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Over-parametrized equals to overfitted? Here is the answer you need.
Along the development of machine learning and statistical learning, obtaining a model with better generalization strength is always the bread and butter. Generalization solely define the…
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Optional function parameters
We'll start by passing arguments to functions that can, but may not, hold value. Have you ever stumbled upon a function signature similar to the following? void calculate(int param); // If param equal...
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Hyperparameters Optimization
The model parameters define how to use input data to get the desired output and are learned at training time. Instead, Hyperparameters determine how our model is structured in the first place…
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Parameter
A parameter is the name of a variable passed into a function. Parameters allow functions to accept inputs. An argument, on the other hand, is the actual value of the variable (also known as the parame...
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Learning Parameters, Part 1: Gradient Descent
Gradient Descent is one of the most popular techniques in optimization, very commonly used in training neural networks. It is intuitive and explainable, given the right background of essential…
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Penalized Regression with Classification
Previously, we looked at the Lasso and Elastic Net methods of regularization using JMP. Those models were built to predict continuous variables, so this time we’ll look at categorical variables. More…...
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