Tag: neural networks

Neural Networks from Scratch (in R)

/

From Medium: This tutorial: “is for those of you with a statistics/econometrics background but not necessarily a machine-learning one and for those of you who want some guidance in building a neural-network from scratch in R to better understand how everything fits (and how i doesn’t).” The author’s motivations for writing the tutorial are: “Understanding (by writing from scratch) the leaky abstractions behind neural-networks dramatically shifted my focus to elements … Continue Reading

Global Artificial Intelligence Conference

/

Upcoming Event:  January 17-19, 2018 Santa Clara, CA From Global AI Conference’s website: “Global Big Data Conference’s vendor agnostic Global Artificial Intelligence(AI) Conference is for all industry verticals(Finance, Retail/E-Commerce/M-Commerce, Healthcare/Pharma/BioTech, Energy, Education, Insurance, Manufacturing, Telco, Auto, Hi-Tech, Media, Agriculture, Chemical, Government, Transportation etc.). It will be the largest vendor agnostic conference in AI space. The Conference allows practitioners to discuss AI through effective use of various techniques.” 2018 Agenda Registration

Python for Data Science and Machine Learning Bootcamp

/

From Udemy: “Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! What Will I Learn? Use Python for Data Science and Machine Learning Use Spark for Big Data Analysis Implement Machine Learning Algorithms Learn to use NumPy for Numerical Data Learn to use Pandas for Data Analysis Learn to use Matplotlib for Python Plotting Learn to use Seaborn for statistical plots Use Plotly for interactive … Continue Reading

The Open Source Data Science Masters

/

“The open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary for making use of data. The Internet is Your Oyster. With Coursera, ebooks, Stack Overflow, and GitHub — all free and open — how can you afford not to take advantage of an open source education?” This program consists of a list of courses and resources. The curriculum covers: … Continue Reading

Data Science from Scratch: First Principles with Python

/

From Amazon: “Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get … Continue Reading

JuliaCon


2017 Event:  June 20th-24th – Berkeley, CA Accepted Talks and Workshops for 2017 Videos from JuliaCon 2016  

DataCamp – Deep Learning in Python

/

From DataCamp: “Artificial neural networks (ANN) are a biologically-inspired set of models that facilitate computers learning from observed data. Deep learning is a set of algorithms that use especially powerful neural networks. It is one of the hottest fields in data science, and most state-of-the-art results in robotics, image recognition and artificial intelligence (including the famous AlphaGo) use deep learning. In this course, you’ll gain hands-on, practical knowledge of how to … Continue Reading

NIPS (Neural Information Processing Systems) Conference

/

From NIPS Conference site: “The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting” Next event:  December 4 – 9, 2017 – Long Beach, CA – SOLD OUT! Videos from NIPS Conference 2016: NIPS 2016 Sessions … Continue Reading

The Elements of Statistical Learning

/

This book describes the important ideas in statistics, data mining, machine learning, and bioinformatics in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.

Pattern Recognition and Machine Learning

/

This textbook is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics, and is aimed at advanced undergraduates or 1st-year PhD students, as well as practitioners.