About Sebastian Raschka

Data Scientist and Machine Learning enthusiast with big passion for Python & open source. Author of "Python Machine Learning" book. Tweets at @rasbt.

Sebastian Raschka Posts (15)

• 3 different types of machine learning - 01 Nov 2017
  
  In this extract from “Python Machine Learning” a top data scientist Sebastian Raschka explains 3 main types of machine learning: Supervised, Unsupervised and Reinforcement Learning. Use code PML250KDN to save 50% off the book cost.
• How to Learn Machine Learning in 10 Days - 01 May 2017
  
  10 days may not seem like a lot of time, but with proper self-discipline and time-management, 10 days can provide enough time to gain a survey of the basic of machine learning, and even allow a new practitioner to apply some of these skills to their own project.
• Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed - 25 Nov 2016
  
  Sebastian Raschka weighs in on how to battle stress as a beginner in the data science world. His insight is to-the-point, so reading it should be a stress-free endeavour.
• What is the Role of the Activation Function in a Neural Network? - 30 Aug 2016
  
  Confused as to exactly what the activation function in a neural network does? Read this overview, and check out the handy cheat sheet at the end.
• What is Softmax Regression and How is it Related to Logistic Regression? - 01 Jul 2016
  
  An informative exploration of softmax regression and its relationship with logistic regression, and situations in which each would be applicable.
• Regularization in Logistic Regression: Better Fit and Better Generalization? - 24 Jun 2016
  
  A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.
• A Visual Explanation of the Back Propagation Algorithm for Neural Networks - 17 Jun 2016
  
  A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.
• How to Select Support Vector Machine Kernels - 13 Jun 2016
  
  Support Vector Machine kernel selection can be tricky, and is dataset dependent. Here is some advice on how to proceed in the kernel selection process.
• What is the Difference Between Deep Learning and “Regular” Machine Learning? - 03 Jun 2016
  
  Another concise explanation of a machine learning concept by Sebastian Raschka. This time, Sebastian explains the difference between Deep Learning and "regular" machine learning.
• A Concise Overview of Standard Model-fitting Methods - 27 May 2016
  
  A very concise overview of 4 standard model-fitting methods, focusing on their differences: closed-form equations, gradient descent, stochastic gradient descent, and mini-batch learning.