Posts
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Welcome 
Chang In Moon #meta CommentsA quick hello and what you'll find on this site.
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Chromosome 8 Gain and High-Grade Transformation in MPNST Chang In Moon #cancer-genomics#sarcoma#research CommentsHow genomic and transcriptomic profiling implicated chromosome 8 amplification in the progression of a rare and aggressive sarcoma.
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Differential Gene Correlation Analysis, Explained Chang In Moon #bioinformatics#rna-seq#statistics CommentsBeyond differential expression: what changes in gene-gene co-expression can reveal about rewired biological networks.
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Connecting Molecular Data to Drug Response in Clinical Trials Chang In Moon #genomics#oncology#databases#clinical-trials CommentsThe motivation and design behind ClinicalOmicsDB, a resource for exploring how molecular features associate with oncology drug responses.
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Self- and Semi-Supervised Learning for Tabular Data Chang In Moon #machine-learning#deep-learning#tabular-data CommentsWhy deep learning struggles with tables, and how VIME uses value imputation and mask estimation to learn from unlabeled records.
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Introduction to PyTorch: A Beginner’s Guide with Detailed Explanations Chang In Moon #python#deep-learning#machine-learning CommentsWelcome to an enhanced beginner’s guide to PyTorch, where we not only introduce you to this powerful machine learning library but also delve into the…
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Quick Takes on Recurrent Neural Networks (RNNs) Chang In Moon #deep-learning CommentsIn the fascinating realm of artificial neural networks, the Recurrent Neural Network (RNN) emerges as a unique powerhouse, adept at deciphering the intricacies of sequential data.
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The Intricacies of Deep Learning for Tabular Data Chang In Moon #deep-learning#machine-learning CommentsDeep learning has revolutionized various fields such as computer vision, natural language processing, and speech recognition.
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What is Self-Supervised Learning? An Introduction Chang In Moon #machine-learning CommentsSelf-supervised learning (SSL) has been creating waves in the world of artificial intelligence (AI) and machine learning.
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Understanding RMSprop: A Visual Guide Chang In Moon #python#deep-learning CommentsRMSprop adjusts the learning rate of each parameter, making it smaller for parameters with consistently large gradients and larger for parameters with small gradients.
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Lasso Pathway Feature Selection: An In-depth Tutorial Chang In Moon #python#machine-learning CommentsFeature selection is a fundamental step in many machine learning workflows.
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Semi-Supervised Learning with Scikit-learn’s SelfTrainingClassifier: A Visual Guide Chang In Moon #python#machine-learning CommentsIn many real-world scenarios, we often find ourselves with a lot of unlabeled data and a small portion of labeled data.
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The Extraordinary Journey of Dave Kunst: The First Person to Walk Around the World Chang In Moon #data-science CommentsIn 1970, Dave Kunst set out on an adventure that would take him across 4 continents, 20 countries, and 14,450 miles.
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Do You Really Need a PhD to Work in Machine Learning? Chang In Moon #python#machine-learning#statistics CommentsAt some point in your career as a machine learning practitioner or aspirant, you may have wondered if you need a PhD to succeed in the field.
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What Companies Look for in ML Candidates: Technical and Non-Technical Skills Chang In Moon #python#deep-learning#machine-learning CommentsWhen it comes to job interviews, companies have two main objectives.
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Applications Companies vs. Tooling Companies: Which Is the Right Choice for Your Career Path? Chang In Moon #data-science CommentsWhen it comes to software development, there are two main types of companies: applications companies and tooling companies.
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ML Production: Other Technical Roles You Need to Know Chang In Moon #machine-learning CommentsAs Machine Learning (ML) continues to revolutionize the tech industry, more companies are exploring ways to integrate ML into their products and services.
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Startup vs. Big Company: Which One is Better for Your Machine Learning Career? Chang In Moon #machine-learning CommentsOne of the biggest questions that people ask when considering their options is whether to work for a startup or a big company.
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Machine Learning Engineer vs. Software Engineer: Understanding the Differences and Overlap Chang In Moon #machine-learning#statistics CommentsIn the world of software development, machine learning engineering (MLE) is often seen as a subfield of software engineering (SWE).
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Artificial General Intelligence and Its Potential Impact on Society: A Future Vision Chang In Moon #deep-learning#machine-learning CommentsThere are many possible benefits and risks associated with this technology.
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Differences between ML Research and Production: Which One is Right for You? Chang In Moon #machine-learning#statistics CommentsThe field of machine learning is rapidly evolving, and professionals have the option to work in research or production.
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Exploring the Stages of the Production Cycle in Machine Learning Chang In Moon #machine-learning CommentsThe production cycle in machine learning refers to the process of taking a model from development to deployment.
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Uncovering the Role of Research in Machine Learning Industry Chang In Moon #machine-learning CommentsFortunately, there are only a handful of machine learning research labs in the world, and most of them are funded by corporations such as Alphabet, Microsoft, Facebook, and Tencent.
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5 Pieces of Life Advice You Should Avoid Chang In Moon #data-science Comments“Just be yourself” is a common piece of advice that you’ve probably heard before.
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Relationship Red Flags You Should Never Ignore Chang In Moon #data-science CommentsAre you tired of being in relationships that don’t seem to work out in the end?
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Mastering the Jargon of Deep Learning: 25 Essential Terms Chang In Moon #deep-learning#machine-learning CommentsIf you’re new to the world of deep learning, it can be overwhelming to navigate through all the technical terms and concepts.
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Navigating the World of Deep Learning: Applications and Challenges Chang In Moon #deep-learning#machine-learning CommentsWith the rapid advancements in technology and the increasing demand for automation, deep learning has become a critical tool in many industries.
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The 7 Traps of Learning Data Science and How to Avoid Them Chang In Moon #data-science CommentsIf you’re an average person interested in data science, you’re likely to run into some difficulties on your learning journey.
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Home Buying Mistakes to Watch Out for in 2023: A Comprehensive Guide Chang In Moon #data-science CommentsBuying a home is a significant investment, and it’s easy to get swept up in the excitement of finding the perfect property.
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The Truth About Speed Reading: Techniques and Tips to Increase Your Reading Speed Chang In Moon #machine-learning CommentsAre you tired of spending hours reading through piles of books, research papers, or novel assignments?
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Understanding the Four Personality Tendencies for Better Study Habits Chang In Moon #data-science CommentsDo you ever find that studying strategies that work for one person simply don’t work for you?
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How to Give a Genuine Apology: Steps for Making Amends Chang In Moon #data-science CommentsTo give a good apology, it’s important to understand the elements that make up a genuine apology. According to researchers, the following elements are crucial for a good apology:
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The Benefits of Napping: How to Nap for Maximum Cognitive Benefits Chang In Moon #data-science CommentsAre you feeling exhausted during the day and struggling to stay awake?
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The Power of Information: What Really Leads to Success Chang In Moon #data-science CommentsInformation is crucial when it comes to achieving success. Knowledge is power, and the more we know, the better equipped we are to make informed decisions and…
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Understanding Procrastination: The Science Behind Delaying Tasks Chang In Moon #data-science CommentsIn this article, we’ll explore why we procrastinate, how it affects us, and what we can do to break the cycle.
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Mental Health and Studying: How to Strike a Balance Chang In Moon #data-science CommentsStudying can be a real challenge, especially when you’re struggling with mental health.
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The 100 Interaction Challenge: Boost Your Social Skills in Just One Month Chang In Moon #data-science CommentsHave you ever felt socially awkward or nervous around new people?
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The Dark Side of Dopamine: How Our Addictions to Social Media and Gaming are Rewiring Our Brains Chang In Moon #data-science CommentsDo you find it difficult to work on productive tasks, such as studying or building a business, but easy to engage in less beneficial activities like watching…
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Atomic Habits: Small Changes, Big Results Chang In Moon #data-science CommentsAtomic Habits is a book that provides a clear and practical guide for building good habits and breaking bad ones.
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Choosing the Right Partner for Marriage: A Guide to Finding True Love Chang In Moon #data-science CommentsThe foundation of a successful marriage is trust. You have to trust your partner to be there for you through thick and thin. This means that you must be able…
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Why Honesty is Key in a Long-Lasting Relationship Chang In Moon #data-science CommentsAre you in a relationship where your partner is doing something that is irritating you?
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Bad Money Habits Keeping You Poor Chang In Moon #data-science CommentsAre you tired of living paycheck to paycheck, always feeling like you’re just barely scraping by?
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From Procrastination to Productivity: How to Overcome Impulsiveness and Delay Chang In Moon #data-science CommentsIn this article, we will discuss the equation and how to use it to our advantage to overcome procrastination.
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Maximizing Your Morning: A Guide to Productivity and Happiness Chang In Moon #data-science CommentsAre you someone who starts your day by immediately checking your phone, email, or social media?
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Revolutionize Your Study Habits with Active Learning Chang In Moon #data-science CommentsAre you struggling to retain information from your courses? Do you find yourself studying for long hours but still can’t seem to earn the grades you want? If…
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Unleashing Your Inner Strength: Building Self-Discipline to Overcome Temptation Chang In Moon #data-science CommentsDo you ever find yourself giving in to temptation or laziness, only to regret it later?
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Why Following Your Passion Is Not Enough to Build a Successful Career Chang In Moon #data-science CommentsPassion is an emotion that we all experience. We love to follow it and indulge in activities that make us feel good. It’s a natural human tendency to associate…
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Why Paying Cash for a Car is Not Always the Best Option Chang In Moon #data-science CommentsAre you considering paying cash for a car? While having the money upfront might seem like an excellent idea, it might not always be in your best interest,…
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How to Turn ‘What’s Your Greatest Weakness’ into Your Greatest Strength Chang In Moon #data-science CommentsAsking about your weaknesses is a common job interview question that often leaves candidates feeling nervous and uneasy.
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Improve Your Performance: 5 Pillars for Success Chang In Moon #data-science CommentsWhen you want to improve your performance, it is essential to focus on all five pillars that support it.
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Mastering the Tricky Job Interview Question: What Are Your Salary Expectations? Chang In Moon #data-science CommentsWhen you go to a job interview, there are some questions that you know are coming.
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Maximizing Productivity: Simple Habits to Adopt Chang In Moon #data-science CommentsAre you often caught in a cycle of unproductivity? Do you find yourself struggling to complete tasks and reach your goals? Developing healthy habits can help…
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5 Pitfalls That Can Ruin Your Productivity Chang In Moon #data-science CommentsAre you struggling to stay productive and focused throughout the day?
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5 Simple Steps for Crafting an Engaging Presentation Chang In Moon #statistics CommentsAre you struggling to engage your audience during your presentations?
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A Louisiana Delight: Perfecting the Crawfish Boil Recipe Chang In Moon #data-science CommentsIf you’re looking for a delicious and flavorful crawfish boil recipe, then you’re in the right place!
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How to Answer the Common Job Interview Question: Why Should We Hire You? Chang In Moon #data-science CommentsIf you’ve been to a job interview, then you’ve probably been asked the common question, “Why should we hire you?” This question can come in many forms, such as…
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How to Set and Achieve Meaningful Life Goals: A Comprehensive Guide Chang In Moon #data-science CommentsAt some point in our lives, we all want to accomplish something that holds value and significance to us.
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The Art of Negotiation: How to Get the Salary and Perks You Deserve Chang In Moon #data-science CommentsHave you ever been in a situation where you feel like you deserve a higher salary, but you’re not sure how to go about getting one?
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The Ultimate Guide to Answering the Most Common Job Interview Question Chang In Moon #data-science CommentsWhen it comes to job interviews, there’s one question that is almost always asked — “Tell me about yourself.” While it may seem like an icebreaker, it’s…
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The importance of uncertainty sampling: A deep dive into active learning query strategies Chang In Moon #python#machine-learning CommentsThere are several ways to measure uncertainty in active learning using uncertainty sampling such as least confidence, margin-based, entropy-based sampling.
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Unlocking the power of active learning with modAL: A beginner’s guide Chang In Moon #python#machine-learning CommentsBefore getting started, you will need to install the modAL library by running the following command:
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Securing Your Place in a PhD Program: My Experience Acing the Graduate School Interview Chang In Moon #data-science CommentsIn 2019, before the COVID-19 pandemic, I interviewed for several graduate school positions with the goal of studying data science and computational biology.
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The All-Rounder vs. Expert Dilemma: How to Choose the Right Path as a Data Scientist Chang In Moon #machine-learning#statistics CommentsWe understand that as a data scientist, choosing between being an all-rounder or an expert can be a daunting task.
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How to filter a DataFrame based on a list of strings in python pandas Chang In Moon #python CommentsTo filter a DataFrame based on a list of strings, you can use the .isin() method combined with the operator.
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How to combine multiple dataframe rows together with different column sizes in python pandas Chang In Moon #python CommentsIn this tutorial, we will look at how to combine multiple dataframe rows together with different column sizes using the Python Pandas library.
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An Introduction to E1071: The Machine Learning Package in R Chang In Moon #r#machine-learning CommentsMachine learning is a powerful tool that allows us to analyze and make predictions based on data.
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How to do semi-supervised classification by low density Separation in python scikit-learn Chang In Moon #python#machine-learning CommentsSemi-supervised classification by low density separation is a technique for performing classification tasks using both labeled and unlabeled data.
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Multivariate Analysis of Variance (MANOVA) in R: A Step-by-Step Walkthrough Chang In Moon #r#statistics CommentsIn this tutorial, we will go over how to perform a MANOVA in R using the manova function from the stats package.
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Maximizing Machine Learning Model Performance through Hyperparameter Optimization in Python Chang In Moon #python#machine-learning CommentsFirst, we need to import the necessary libraries for this tutorial. We will be using numpy, pandas, and scikit-learn.
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The Art of Label Propagation in Python scikit-learn and GridSearchCV Chang In Moon #python#machine-learning CommentsTo perform label propagation with grid search cross-validation (CV) in Python, you can follow these steps:
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Linear Regression 101: A Python scikit-learn Tutorial Chang In Moon #python#machine-learning#statistics CommentsLinear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
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Logistic Regression 101: A Beginner’s Guide with Python Chang In Moon #python#machine-learning#statistics CommentsWe will start by importing the necessary libraries and loading the data.
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The Art of Feature Selection in Python: Removing Highly Correlative Features Chang In Moon #python CommentsThe first step in removing highly correlative redundant features is to calculate the correlation between all the features.
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Determining the Suitability of Your Model: Goodness-of-Fit Tests in R Chang In Moon #r#statistics CommentsIn this tutorial, we will learn how to perform goodness-of-fit tests in R using the stats package.
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Goodness-of-Fit Testing in Python: Tips and Tricks for Data Scientists Chang In Moon #python#statistics CommentsIn this tutorial, we will learn how to perform goodness-of-fit tests in Python using the scipy module.
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Understanding and Using the Wilcoxon Test in Python Chang In Moon #python#statistics CommentsBefore we can start, we need to make sure that we have the necessary libraries installed.
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Semi-Supervised Learning: The Middle Ground of Machine Learning Chang In Moon #machine-learning CommentsOne of the main advantages of semi-supervised learning is that it can improve the performance of a model with a limited amount of labeled data.
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The Fundamentals of Supervised Learning Chang In Moon #machine-learning#statistics CommentsThere are two main types of supervised learning: classification and regression.
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Data Wrangling Made Easy with dplyr in R Chang In Moon #r#statistics CommentsSome of the key features of dplyr include:
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Exploring the World of Machine Learning with mlr3 in R Chang In Moon #r#deep-learning#machine-learning CommentsSome of the key features of mlr3 include:
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A Beginner’s Guide to Cross-Validation in Python’s scikit-learn Chang In Moon #python#machine-learning#statistics CommentsIn this tutorial, we will learn how to perform cross-validation in Python using the scikit-learn library.
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A Beginner’s Guide to Splitting Machine Learning Datasets in Python Chang In Moon #python#machine-learning#statistics CommentsSplitting a machine learning dataset into training and test sets is an important step in the model-building process, as it allows us to evaluate the model’s performance on unseen data.
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Balancing the Scale: A Comprehensive Approach to Imbalanced Datasets in Python Chang In Moon #python#machine-learning#statistics CommentsIn this tutorial, we will learn how to deal with imbalanced datasets in the context of machine learning.
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Mastering Feature Selection in Python scikit-learn: A Step-by-Step Guide Chang In Moon #python#machine-learning CommentsFirst, we need to import the necessary libraries and the dataset that we will be using for this tutorial.
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From Novice to Expert: A Step-by-Step Guide to UNIX Terminal Commands Chang In Moon #data-science CommentsWelcome to this tutorial on how to operate Unix commands in the terminal!
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From Zero to Hero: A Comprehensive Guide to Feature Engineering in Python scikit-learn Chang In Moon #python#machine-learning#statistics CommentsBefore we begin, it is important to understand that feature engineering is an iterative process and requires a good understanding of the problem and the data.
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R-evel in the Insights Hidden in Your Data: A Comprehensive Guide to Exploratory Data Analysis in R Chang In Moon #r#statistics CommentsExploratory Data Analysis (EDA) is a crucial step in the data analysis process that involves understanding and summarizing the characteristics of a dataset.
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Say Goodbye to Missing Values: A Beginner’s Guide to Feature Imputation in Python Chang In Moon #python#machine-learning#statistics CommentsOne solution to this problem is to simply drop rows or columns with missing values from the dataset.
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Standardizing Your Data: A Step-by-Step Guide to Feature Normalization in Python Chang In Moon #python#machine-learning#statistics CommentsThere are several different methods for normalizing features, each with its own advantages and disadvantages.
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Transforming Your Data: A Hands-On Guide to Feature Encoding in Python Chang In Moon #python#machine-learning CommentsThere are several different methods for encoding categorical variables, each with its own advantages and disadvantages.
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From Structured to Unstructured: The Different Types of Machine Learning Data Chang In Moon #machine-learning CommentsMachine learning algorithms rely on data to learn and make predictions or decisions.
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Microarray vs RNA-seq: A Comparison of Gene Expression Technologies Chang In Moon #bioinformatics CommentsMicroarrays and RNA-seq are two techniques used to study gene expression, which refers to the process by which the genetic information encoded in DNA is…
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Uncovering the Hidden Insights of Your Data: A Guide to Exploratory Data Analysis in Python Chang In Moon #python#machine-learning#statistics CommentsThe first step in exploratory data analysis is to familiarize yourself with the data.
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Data Collection 101: Where to Find Data for Your Machine Learning Projects Chang In Moon #machine-learning CommentsOne of the key components of any machine learning project is the data.
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Machine Learning 101: A Practical Guide for Getting Started Chang In Moon #deep-learning#machine-learning#statistics CommentsAre you interested in learning about machine learning, but aren’t sure where to start?