2016 May Tutorials, Overviews
All (102) | Courses, Education (3) | Meetings (12) | News, Features (26) | Opinions, Interviews, Reports (34) | Software (5) | Tutorials, Overviews (16) | Webcasts & Webinars (6)
- Introduction to Recurrent Networks in TensorFlow - May 31, 2016.
A straightforward, introductory overview of implementing Recurrent Neural Networks in TensorFlow.
- Hadoop Key Terms, Explained - May 30, 2016.
An straightforward overview of 16 core Hadoop ecosystem concepts. No Big Picture discussion, just the facts.
- A Concise Overview of Standard Model-fitting Methods - May 27, 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.
- Doing Data Science: A Kaggle Walkthrough Part 2 – Understanding the Data - May 27, 2016.
This is the second post in a fantastic 6 part series covering the process of data science, and the application of the process to a Kaggle competition. Read on for a great overview of practicing data science.
- The Good, Bad & Ugly of TensorFlow - May 24, 2016.
A survey of six months of rapid evolution (+ tips/hacks and code to fix the ugly stuff) using TensorFlow. Get some great advice from the trenches.
- What are the Challenges of the Analytics of Things? - May 20, 2016.
without the AoT, it is difficult to realize the full potential of the IoT. We review the promise and challenges of Analytics of Things, including data, security, analytics implementation, standartization, and more.
- Doing Data Science: A Kaggle Walkthrough Part 1 – Introduction - May 19, 2016.
This is the first post in a fantastic 6 part series covering the process of data science, and the application of the process to a Kaggle competition. Very thorough, and very insightful.
- Six PAW Chicago Sessions That Show Analytics’ Long Reach - May 19, 2016.
At Chicago's Predictive Analytics World for Business conference, June 20-23, 2016, explore case studies from a range of industries and discuss best practices for infusing organizations with the power of analytics in new and innovative ways. KDnuggets subscribers enjoy $150 off!
- The Amazing Power of Word Vectors - May 18, 2016.
A fantastic overview of several now-classic papers on word2vec, the work of Mikolov et al. at Google on efficient vector representations of words, and what you can do with them.
- An Introduction to Semi-supervised Reinforcement Learning - May 17, 2016.
A great overview of semi-supervised reinforcement learning, including general discussion and implementation information.
- Troubleshooting Neural Networks: What is Wrong When My Error Increases? - May 13, 2016.
An overview of some of the things that could lead to an increased error rate in neural network implementations.
- Deep Learning and Neuromorphic Chips - May 12, 2016.
The 3 main ingredients to creating artificial intelligence are hardware, software, and data, and while we have focused historically on improving software and data, what if, instead, the hardware was drastically changed?
- How to Quantize Neural Networks with TensorFlow - May 4, 2016.
The simplest motivation for quantization is to shrink neural network representation by storing the min and max for each layer. Learn more how to perform quantization for deep neural networks.
- How to Network and Build a Personal Brand in Data Science - May 2, 2016.
SpringBoard shares some ideas on how to network and build a data career, as taken from a new guide they have put together on the topic.
- How to Use Cohort Analysis to Improve Customer Retention - May 2, 2016.
Cohort analysis is a subset of behavioral analytics that takes the user data and breaks them into related groups for analysis. Let’s understand using cohort analysis with an example of daily cohort of app users.