Deep Learning with MATLAB: Transfer Learning in 10 Lines of MATLAB Code Video

Watch this 4 minute video and learn to use #deeplearning with your own data.

  • Watch a quick demonstration of how to use MATLAB® for transfer learning which is a practical way to apply deep learning to your problems.
  • This demo teaches you how to use transfer learning to re-train AlexNet, a pretrained deep convolutional neural network (CNN or ConvNet) to recognize snack food such as hot dogs, cup cakes and apple pie.

“Learn how to use transfer learning in MATLAB to re-train deep learning networks created by experts for your own data or task. ”
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Scikit-Learn Cheat Sheet: Python Machine Learning

Scikit-learn cheat sheet: #machinelearning with #Python -

  • Most of you who are learning data science with Python will have definitely heard already about , the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.
  • If you’re still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist.
  • This  cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you’ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
  • In short, this cheat sheet will kickstart your data science projects: with the help of code examples, you’ll have created, validated and tuned your machine learning models in no time.
  • In addition, you’ll make use of Python’s data visualization library  to visualize your results.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.
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Scikit-Learn Cheat Sheet: Python Machine Learning

Cheat sheet: #machinelearning in #Python with scikit-learn -

  • The cheat sheet will kickstart your data science projects: with the help of code examples, you’ll have created, validated and tuned your machine learning models in no time.
  • Begin with our scikit-learn tutorial for beginners , in which you’ll learn in an easy, step-by-step way how to explore handwritten digits data, how to create a model for it, how to fit your data to your model and how to predict target values.
  • If you still have no idea about how scikit-learn works, this machine learning cheat sheet might come in handy to get a quick first idea of the basics that you need to know to get started.
  • The scikit-learn cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you’ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
  • If you’re still quite new to the field, you should be aware that machine learning, and also this Python library, belong to the must-knows for every aspiring data scientist.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.
Continue reading “Scikit-Learn Cheat Sheet: Python Machine Learning”

Introduction to Machine Learning & Face Detection in Python

Introduction to Machine Learning & Face Detection in Python
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  • The last chapters will be about SVM and Neural Networks: the most important approaches in machine learning.
  • In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together.
  • Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very very good guess about stock prices movement in the market.
  • The topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking.
  • The course is about the fundamental concepts of machine learning, focusing on neural networks, SVM and decision trees.

Learn the most up to date techniques in data mining from regression to neural networks
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Practical Machine Learning Tutorial with Python

Practical Machine Learning Tutorial with Python
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  • In the tutorial, we discuss the optimization problem that is the Support Vector Machine, as well as how we intend to solve it ourselves.
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In this tutorial, we discuss the optimization problem that is the Support Vector Machine, as well as how we intend to solve it ourselves.
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