Deep Learning / 16 October 2019 Optimizers Explained - Adam, Momentum and Stochastic Gradient Descent Picking the right optimizer with the right parameters, can help you squeeze the last bit of accuracy out of your neural network model.
Machine Learning / 15 September 2019 How to use Grid Search CV in sklearn, Keras, XGBoost, LightGBM in Python GridSearchCV is a brute force on finding the best hyperparameters for a specific dataset and model. Why not automate it to the extend we can?
Deep Learning / 22 August 2019 Activation Functions Explained - GELU, SELU, ELU, ReLU and more Better optimized neural network; choose the right activation function, and your neural network can perform vastly better. 6 activation functions explained.
Deep Learning / 5 August 2019 Neural Networks: Feedforward and Backpropagation Explained & Optimization What is neural networks? Developers should understand backpropagation, to figure out why their code sometimes does not work. Visual and down to earth explanation of the math of backpropagation.
Machine Learning / 30 May 2019 Nested Cross-Validation Python Code Code for nested cross-validation in machine learning - unbiased estimation of true error. What is nested cross-validation, and the why and when to use it.
Machine Learning / 25 April 2019 Random Forest with GridSearchCV in Python and Decision Trees explained Perhaps one of the most common algorithms in Kaggle competitions, and machine learning in general, is the random forest algorithm. It performs well in almost all scenarios and is mostly impossible to overfit,
Machine Learning / 14 April 2019 How to do Linear Regression and Logistic Regression in Machine Learning? Wondering how Linear Regression or Logistic Regression works in Machine Learning? Python code and a walkthrough of both concepts are available here.
Machine Learning / 31 March 2019 Probability Distributions and Maximum Likelihood Estimation (MLE) Probability distributions is all about how we can represent the distributions of probabilities of data. There is fundamentally a formula for each distribution, but I like to visualize the sort of distribution of
Machine Learning / 24 March 2019 Probability Theory: Bayes Theorem, Sum Rule and Product Rule This post is where you need to listen and really learn the fundamentals. All modern approaches to Machine Learning uses probability theory. AlphaStar is an example, where DeepMind made many different AIs using
Machine Learning / 16 March 2019 Measures of Distance - Similarity and Dissimilarity Anytime we do machine learning, measures of distance can be important. An example comes to mind, when a machine has to distinguish between two object, say a pig or cow. Well then it
Machine Learning / 9 March 2019 Statistics Basics - Variance and Standard Deviation We want to be able to say something about our data using some statistical measures, because it is important information. There are many ways to use statistics in machine learning, and the mean,
Machine Learning / 4 March 2019 Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) This post will keep us on the level of Machine Learning Introduction, but it will try to give a clearer image of what happens behind, i.e. what PCA and SVD does. Psst.
Machine Learning / 24 February 2019 Data Basics and Data Manipulation What is data, a dataset, and how do we describe it?What we say that data is something, which is held in a dataset, that just contains N observations (rows) and M features
Machine Learning / 12 February 2019 Introductory Terms in Machine Learning This will be a series of introduction to Machine Learning, starting from the very foundation of machine learning and then moving into topics within the field. I will cover datasets, principal component analysis,
Linear Algebra / 5 February 2019 Cheat Sheet for Linear Algebra This is a continuously updated cheat sheet for the Linear Algebra I covered, as well as for future posts. Currently included are intuition, notation and formulas. Notation like vector, scalar, matrix, m x n, basis vectors, mapping in space, determinant, cross product, dot product and much more.
Linear Algebra / 28 January 2019 Linear Algebra Basics 4: Determinant, Cross Product and Dot Product I visualized the determinant, cross product and dot product can be hard. Come read the intuitive way of understanding these three pieces from Linear Algebra.
Linear Algebra / 6 January 2019 Linear Algebra Basics 3: Linear Transformations and Matrix Multiplication What is Linear Transformations? What is Vector Space? How to do Vector Multiplication (Matrix Multiplication)? Conceptualizing a Linear Transformation is also key to understanding a transformation, so ..
Linear Algebra / 29 December 2018 Linear Algebra Basics 2: Basis Vectors, Span and Linear Combinations What are basis vectors? Why are they so important? What is Span and a Linear Combination? Come read, quick recap at the bottom.
Linear Algebra / 16 December 2018 Linear Algebra Basics 1: Vectors, Vector Addition and Scalars What is a vector? How to think about vectors? What is vector addition? What is a scalar? Come find the answers here.
