Dec
2016 Jan Tutorials, Overviews
http likes 63All (120) | Courses, Education (11) | Meetings (12) | News, Features (27) | Opinions, Interviews, Reports (35) | Publications (10) | Software (5) | Top Tweets (3) | Tutorials, Overviews (11) | Webcasts (6)
- Python Data Science with Pandas vs Spark DataFrame: Key Differences
- Jan 29, 2016.
A post describing the key differences between Pandas and Spark's DataFrame format, including specifics on important regular processing features, with code samples.
- Deep Learning with Spark and TensorFlow
- Jan 28, 2016.
The integration of TensorFlow with Spark leverages the distributed framework for hyperparameter tuning and model deployment at scale. Both time savings and improved error rates are demonstrated.
- How to Check Hypotheses with Bootstrap and Apache Spark
- Jan 28, 2016.
Learn how to leverage bootstrap sampling to test hypotheses, and how to implement in Apache Spark and Scala with a complete code example.
- Implementing Your Own k-Nearest Neighbor Algorithm Using Python
- Jan 27, 2016.
A detailed explanation of one of the most used machine learning algorithms, k-Nearest Neighbors, and its implementation from scratch in Python. Enhance your algorithmic understanding with this hands-on coding exercise.
- 7 Common Data Science Mistakes and How to Avoid Them
- Jan 26, 2016.
Data scientist in business is as similar as to that of a detective: discovering the unknown. But, while venturing onto this journey they do tend to fall into the pitfalls. Understand, how these mistakes are made and how you can avoid them.
- Learning to Code Neural Networks
- Jan 22, 2016.
Learn how to code a neural network, by taking advantage of someone else's experiences learning how to code a neural network.
- Scikit-learn and Python Stack Tutorials: Introduction, Implementing Classifiers
- Jan 18, 2016.
A small collection of introductory scikit-learn and Python stack tutorials for those with an existing understanding of machine learning looking to jump right into using a new set of tools.
- Hitchhikers Guide to Azure Machine Learning Studio
- Jan 15, 2016.
Learn Azure ML Studio through this brief hands-on tutorial. This step-by-step guide will help you get a quick-start and grasp the basics of this Predictive Modeling tool.
- Attention and Memory in Deep Learning and NLP
- Jan 12, 2016.
An overview of attention mechanisms and memory in deep neural networks and why they work, including some specific applications in natural language processing and beyond.
- 7 Steps to Understanding Deep Learning
- Jan 11, 2016.
There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps!
- Understanding Rare Events and Anomalies: Why streaks patterns change
- Jan 8, 2016.
We often look back at the past year and an overall history of rare events, and try to then extrapolate future odds of the same rare event, based on that. We illustrate here, that rare past events have no usefulness in understanding the rarity of the same events in the future!
Automating Every Aspect of Your Python Project
Autograd: The Best Machine Learning Library You’re Not Using?