Premium Post Deep Learning TensorFlow 2.0 Tutorial in 10 Minutes TensorFlow is inevitably the package to use for Deep Learning, if you want the easiest deployment possible. On top of that, Keras is the standard API and is easy to use, which makes TensorFlow powerful for you and everyone else using it.
Premium Post Deep Learning 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.
Premium Post Machine Learning 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?
Premium Post Deep Learning 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.
Premium Post Deep Learning 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.
Premium Post Machine Learning 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.
Premium Post Machine Learning 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
Premium Post Machine Learning 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.
Premium Post Machine Learning 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
Premium Post Machine Learning 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
Premium Post Machine Learning 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.
Premium Post Machine Learning 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,
Premium Post Machine Learning 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
Premium Post Machine Learning 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)
Premium Post Machine Learning 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,
