My notes

The notes I've been taking thorought my studies on different subjects.



re:Invent 2023

08 Dec 2023

I couldn't really imagine a better way of saying goodbye to 2023 than diving headfirst into the tech wonderland that was AWS re:Invent. From November 28 to December 2 2023, Las Vegas transformed into a hub of innovation, and I found myself right in the middle of it all.

  • other

    • re:Invent 2023

    08 Dec 2023

    I couldn't really imagine a better way of saying goodbye to 2023 than diving headfirst into the tech wonderland that was AWS re:Invent. From November 28 to December 2 2023, Las Vegas transformed into a hub of innovation, and I found myself right in the middle of it all.

  • other

    • Atlassian Codegeist 2023

    24 Oct 2023

    Documentation. Every developer's Achilles heel. I can't count the number of times I've found myself switching between screens, trying to simultaneously remember what each part of my code does while documenting it.

  • other

    • Atlassian Codegeist 2023

    24 Oct 2023

    Documentation. Every developer's Achilles heel. I can't count the number of times I've found myself switching between screens, trying to simultaneously remember what each part of my code does while documenting it.

  • other

    • Google Summer of Code 2023

    24 Aug 2023

    cclib is an open source library, written in Python, for parsing and interpreting the results of computational chemistry packages. The goals of cclib include extracting data from different files, creating an interface for computational chemistry results, etc.

  • other

    • Google Summer of Code 2023

    24 Aug 2023

    cclib is an open source library, written in Python, for parsing and interpreting the results of computational chemistry packages. The goals of cclib include extracting data from different files, creating an interface for computational chemistry results, etc.

  • other

    • AWS Machine Learning Specialty Bite Size Recap 3/3

    08 Jul 2023

    A bunch of bite-size information on AI services on AWS to help with AWS Machine Learning Specialty Revision. Part 3/3

  • other

    • AWS Machine Learning Specialty Bite Size Recap 3/3

    08 Jul 2023

    A bunch of bite-size information on AI services on AWS to help with AWS Machine Learning Specialty Revision. Part 3/3

  • other

    • AWS Machine Learning Specialty Bite Size Recap 2/3

    08 Jul 2023

    A bunch of bite-size information on AI services on AWS to help with AWS Machine Learning Specialty Revision. Part 2/3

  • other

    • AWS Machine Learning Specialty Bite Size Recap 2/3

    08 Jul 2023

    A bunch of bite-size information on AI services on AWS to help with AWS Machine Learning Specialty Revision. Part 2/3

  • other

    • AWS Machine Learning Specialty: Bite Size Recap 1/3

    07 Jul 2023

    A bunch of bite-size information on AI services on AWS to help with AWS Machine Learning Specialty Revision.

  • other

    • AWS Machine Learning Specialty: Bite Size Recap 1/3

    07 Jul 2023

    A bunch of bite-size information on AI services on AWS to help with AWS Machine Learning Specialty Revision.

  • other

    • Solving the Problem of Overfitting

    10 Nov 2021

    Overfitting arises when a hypothesis function is too complex, while underfitting occurs when it's too simple. To address overfitting, we can manually select features or use regularization to reduce the weight of some terms in the function. Regularization works well when we have a lot of slightly useful features.

  • machine learning

    • Solving the Problem of Overfitting

    10 Nov 2021

    Overfitting arises when a hypothesis function is too complex, while underfitting occurs when it's too simple. To address overfitting, we can manually select features or use regularization to reduce the weight of some terms in the function. Regularization works well when we have a lot of slightly useful features.

  • machine learning

    • Multiclass Classification

    16 Oct 2021

    Multiclass classification is approached by expanding the definition of y to include more than two categories. This problem is divided into n+1 binary classification problems, and binary logistic regression is applied to each case to predict which class the data belongs to. The hypothesis that returns the highest value is used as the prediction.

  • machine learning

    • Multiclass Classification

    16 Oct 2021

    Multiclass classification is approached by expanding the definition of y to include more than two categories. This problem is divided into n+1 binary classification problems, and binary logistic regression is applied to each case to predict which class the data belongs to. The hypothesis that returns the highest value is used as the prediction.

  • machine learning

    • Logic Regression Model

    12 Oct 2021

    This post explains how to create a cost function for logistic regression that is convex and can be optimized using gradient descent. The cost function is defined in terms of the log-likelihood of the model and is derived using maximum likelihood estimation. The article also provides the formula for gradient descent and introduces more advanced optimization algorithms, such as Conjugate Gradient, BFGS, and L-BFGS.

  • machine learning

    • Logic Regression Model

    12 Oct 2021

    This post explains how to create a cost function for logistic regression that is convex and can be optimized using gradient descent. The cost function is defined in terms of the log-likelihood of the model and is derived using maximum likelihood estimation. The article also provides the formula for gradient descent and introduces more advanced optimization algorithms, such as Conjugate Gradient, BFGS, and L-BFGS.

  • machine learning

    • Classification and Representation

    01 Oct 2021

    This post discusses binary classification problems where y can only take on two values, 0 and 1. It explains how to use the logistic function to change the form of the hypothesis function and ensure that it returns values between 0 and 1. Iu also introduces the decision boundary and how it helps determine the class that a particular data point belongs to.

  • machine learning

    • Classification and Representation

    01 Oct 2021

    This post discusses binary classification problems where y can only take on two values, 0 and 1. It explains how to use the logistic function to change the form of the hypothesis function and ensure that it returns values between 0 and 1. Iu also introduces the decision boundary and how it helps determine the class that a particular data point belongs to.

  • machine learning

    • Multivariate Linear Regression

    29 Sep 2021

    Gradient descent gives one way of minimizing J. Let's discuss a second way of doing so, this time performing the minimization explicitly and without resorting to an iterative algorithm. In the Normal Equation method, we will minimize J by explicitly taking its derivatives with respect to the thetas of position j and setting them to 0.

  • machine learning

    • Multivariate Linear Regression

    29 Sep 2021

    Gradient descent gives one way of minimizing J. Let's discuss a second way of doing so, this time performing the minimization explicitly and without resorting to an iterative algorithm. In the Normal Equation method, we will minimize J by explicitly taking its derivatives with respect to the thetas of position j and setting them to 0.

  • machine learning

    • Multivariate Linear Regression

    29 Sep 2021

    Linear regression with multiple variables is also known as "multivariate linear regression". We now introduce notation for equations where we can have any number of input variables.

  • machine learning

    • Multivariate Linear Regression

    29 Sep 2021

    Linear regression with multiple variables is also known as "multivariate linear regression". We now introduce notation for equations where we can have any number of input variables.

  • machine learning

    • Parameter Learning

    28 Sep 2021

    So we have our hypothesis function and we have a way of measuring how well it fits into the data. Now we need to estimate the parameters in the hypothesis function. That's where gradient descent comes in.

  • machine learning

    • Parameter Learning

    28 Sep 2021

    So we have our hypothesis function and we have a way of measuring how well it fits into the data. Now we need to estimate the parameters in the hypothesis function. That's where gradient descent comes in.

  • machine learning

    • Model and Cost Function

    26 Sep 2021

    To establish notation for future, we'll use x to denote the input variables (living area in this example), also called input features, and y to denote the output or target variable that we are trying to predict (price).

  • machine learning

    • Model and Cost Function

    26 Sep 2021

    To establish notation for future, we'll use x to denote the input variables (living area in this example), also called input features, and y to denote the output or target variable that we are trying to predict (price).

  • machine learning

    • Machine Learning - Introduction

    25 Sep 2021

    Two definitions of Machine Learning are offered. Arthur Samuel described it as: "The field of study. that gives computer the ability to learn without being explicitly programmed". This is an older, informal definition.

  • machine learning

    • Machine Learning - Introduction

    25 Sep 2021

    Two definitions of Machine Learning are offered. Arthur Samuel described it as: "The field of study. that gives computer the ability to learn without being explicitly programmed". This is an older, informal definition.

  • machine learning

    • Predictive Models - Regression

    16 May 2021

    Least squares minimizes the sum of squared errors. This is the same as maximizing the likelihood of observed data if you start thinking of the problem in terms of probabilities and probability distribution functions.

  • machine learning

    • Predictive Models - Regression

    16 May 2021

    Least squares minimizes the sum of squared errors. This is the same as maximizing the likelihood of observed data if you start thinking of the problem in terms of probabilities and probability distribution functions.

  • machine learning

    • Statistic and Probability

    16 May 2021

    Discrete Data: integer based, often counts of some event. How many purchases did a customer make in a year? How many times did I flif heads? Continous Data: Has an infinite number of possible values. How much time did it take for a user to check out? How much rain fell on a givern day?.

  • machine learning

    • Statistic and Probability

    16 May 2021

    Discrete Data: integer based, often counts of some event. How many purchases did a customer make in a year? How many times did I flif heads? Continous Data: Has an infinite number of possible values. How much time did it take for a user to check out? How much rain fell on a givern day?.

  • machine learning

    • Data Mining

    23 Apr 2021

    Data Mining in Python with focus on machine learning algorithms and techniques. K-Nearest Neighbors (KNN), Principal Component Analysis (PCA).

  • machine learning

    • Data Mining

    23 Apr 2021

    Data Mining in Python with focus on machine learning algorithms and techniques. K-Nearest Neighbors (KNN), Principal Component Analysis (PCA).

  • machine learning

    • Real World Data

    22 Apr 2021

    Coding in Python with focus on machine learning algorithms and techniques. Confusion Matrix, Measuring Classifiers, Precision, Recall, ROC Curve, Bias and Variance, K-Fold Validation, Outliers, Unbalanced Data, SMOTE, Binning, Transforming, Encoding, Normalization.

  • machine learning

    • Real World Data

    22 Apr 2021

    Coding in Python with focus on machine learning algorithms and techniques. Confusion Matrix, Measuring Classifiers, Precision, Recall, ROC Curve, Bias and Variance, K-Fold Validation, Outliers, Unbalanced Data, SMOTE, Binning, Transforming, Encoding, Normalization.

  • machine learning

    • Machine Learning with Python, Part 2

    22 Apr 2021

    Coding in Python with focus on machine learning algorithms and techniques. Decision Trees, Random Forest, Ensemble Learning, CGBoost, Support Vector Machines.

  • machine learning

    • Machine Learning with Python, Part 2

    22 Apr 2021

    Coding in Python with focus on machine learning algorithms and techniques. Decision Trees, Random Forest, Ensemble Learning, CGBoost, Support Vector Machines.

  • machine learning

    • Machine Learning with Python, Part 1

    21 Apr 2021

    Coding in Python with focus on machine learning algorithms and techniques. Train / test split, Bayesian Methods, Naive Bayes, K-Means Clustering, Entropy.

  • machine learning

    • Machine Learning with Python, Part 1

    21 Apr 2021

    Coding in Python with focus on machine learning algorithms and techniques. Train / test split, Bayesian Methods, Naive Bayes, K-Means Clustering, Entropy.

  • machine learning

    • Machine Learning General Questions

    21 Mar 2021

    Machine Learning Questions - general questions usually asked on interviews.

  • machine learning

    • Machine Learning General Questions

    21 Mar 2021

    Machine Learning Questions - general questions usually asked on interviews.

  • machine learning

    • Experimental Design

    21 Feb 2021

    A/B Testing. A controlled experiment, usually in the context of a website. You test the performance of some change to your website (the variant) and measure conversion relative to your unchanged site (the control).

  • machine learning

    • Experimental Design

    21 Feb 2021

    A/B Testing. A controlled experiment, usually in the context of a website. You test the performance of some change to your website (the variant) and measure conversion relative to your unchanged site (the control).

  • machine learning