Free Machine Learning eBooks

Free #MachineLearning eBooks - March 2017 #abdsc

  • Machine learning is one of the fastest growing areas of computer science, with far-reaching applications.
  • The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
  • The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
  • These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
  • Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Here are three eBooks available for free.
MACHINE LEARNING
Edited by Abdelhamid Mellouk and Abdennacer Chebira
Machine Learning can be defined in various ways…
Continue reading “Free Machine Learning eBooks”

Concise Visual Summary of Deep Learning Architectures

Concise Visual Summary of #DeepLearning Architectures #abdsc

  • With new neural network architectures popping up every now and then, it’s hard to keep track of them all.
  • RNNs sometimes refer to recursive neural networks, but most of the time they refer to recurrent neural networks.
  • That’s not the end of it though, in many places you’ll find RNN used as placeholder for any recurrent architecture, including LSTMs, GRUs and even the bidirectional variants.
  • Many abbreviations also vary in the amount of “N”s to add at the end, because you could call it a convolutional neural network but also simply a convolutional network (resulting in CNN or CN).
  • Composing a complete list is practically impossible, as new architectures are invented all the time.

This article was written by Fjodor Van Veen. 
With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing…
Continue reading “Concise Visual Summary of Deep Learning Architectures”

Free Machine Learning eBooks

Free Machine Learning eBooks - March 2017 | #DataScience #MachineLearning #RT

  • Machine learning is one of the fastest growing areas of computer science, with far-reaching applications.
  • The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
  • The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
  • These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
  • Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Here are three eBooks available for free.
MACHINE LEARNING
Edited by Abdelhamid Mellouk and Abdennacer Chebira
Machine Learning can be defined in various ways…
Continue reading “Free Machine Learning eBooks”

Concise Visual Summary of Deep Learning Architectures

Concise summary of deep learning neural networks #AI

  • With new neural network architectures popping up every now and then, it’s hard to keep track of them all.
  • RNNs sometimes refer to recursive neural networks, but most of the time they refer to recurrent neural networks.
  • That’s not the end of it though, in many places you’ll find RNN used as placeholder for any recurrent architecture, including LSTMs, GRUs and even the bidirectional variants.
  • Many abbreviations also vary in the amount of “N”s to add at the end, because you could call it a convolutional neural network but also simply a convolutional network (resulting in CNN or CN).
  • Composing a complete list is practically impossible, as new architectures are invented all the time.

This article was written by Fjodor Van Veen. 
With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing…
Continue reading “Concise Visual Summary of Deep Learning Architectures”

Free Machine Learning eBooks

Free #MachineLearning eBooks:  #abdsc #BigData #DataScience via @eelrekab

  • Machine learning is one of the fastest growing areas of computer science, with far-reaching applications.
  • The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
  • The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
  • These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
  • Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Here are three eBooks available for free.
MACHINE LEARNING
Edited by Abdelhamid Mellouk and Abdennacer Chebira
Machine Learning can be defined in various ways…
Continue reading “Free Machine Learning eBooks”

Plotting Earthquakes with D3.js + Leaflet

Plotting Earthquakes with D3.js + Leaflet  #MachineLearning #DataScience

  • The visualization showed significant earthquakes around the world since 1900s.
  • The attribute contains all of the characteristic of our geographic data.
  • Using geoPandas only involved declaring that the data were a geoPandas data frame.
  • Last time, I wrote about how folium allows us to seamlessly integrate leaflet maps for visualization.
  • In the last post , I used Folium to make a visualization of earthquakes around the world.

Last time, I wrote about how folium allows us to seamlessly integrate leaflet maps for visualization. This time, I wanted to challenge myself a bit more. I d…
Continue reading “Plotting Earthquakes with D3.js + Leaflet”