- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 5 - Jul 16, 2021.
Training efficient deep learning models with any software tool is nothing without an infrastructure of robust and performant compute power. Here, current software and hardware ecosystems are reviewed that you might consider in your development when the highest performance possible is needed.
Deep Learning, Efficiency, Google, Hardware, Machine Learning, NVIDIA, PyTorch, Scalability, TensorFlow
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 4 - Jul 9, 2021.
With the right software, hardware, and techniques at your fingertips, your capability to effectively develop high-performing models now hinges on leveraging automation to expedite the experimental process and building with the most efficient model architectures for your data.
Attention, Convolution, Deep Learning, Efficiency, Hyperparameter, Machine Learning, Scalability
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 3 - Jul 2, 2021.
Now that you are ready to efficiently build advanced deep learning models with the right software and hardware tools, the techniques involved in implementing such efforts must be explored to improve model quality and obtain the performance that your organization desires.
Compression, Deep Learning, Efficiency, Machine Learning, Scalability
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 2 - Jun 25, 2021.
As your organization begins to consider building advanced deep learning models with efficiency in mind to improve the power delivered through your solutions, the software and hardware tools required for these implementations are foundational to achieving high-performance.
Deep Learning, Efficiency, Machine Learning, Scalability
- High Performance Deep Learning, Part 1 - Jun 18, 2021.
Advancing deep learning techniques continue to demonstrate incredible potential to deliver exciting new AI-enhanced software and systems. But, training the most powerful models is expensive--financially, computationally, and environmentally. Increasing the efficiency of such models will have profound impacts in many ways, so developing future models with this intension in mind will only help to further expand the reach, applicability, and value of what deep learning has to offer.
Deep Learning, Efficiency, History, Machine Learning
- Vision Transformers: Natural Language Processing (NLP) Increases Efficiency and Model Generality - Feb 2, 2021.
Why do we hear so little about transformer models applied to computer vision tasks? What about attention in computer vision networks?
Attention, Efficiency, Modeling, NLP, Transformer
- 2 Things You Need to Know about Reinforcement Learning – Computational Efficiency and Sample Efficiency - Apr 7, 2020.
Experimenting with different strategies for a reinforcement learning model is crucial to discovering the best approach for your application. However, where you land can have significant impact on your system's energy consumption that could cause you to think again about the efficiency of your computations.
Efficiency, Machine Learning, Reinforcement Learning
- How Bad Data is Affecting Your Organization’s Operational Efficiency - Mar 5, 2020.
Despite recognizing the importance of data quality, many companies still fail to implement a data quality framework that could protect them from making costly mistakes. Poor data does not just cause revenue loss – it’s the reason your company could lose employees, customers and reputation!
Business, Data Management, Data Operations, Data Quality, Efficiency
- Get a 2–6x Speed-up on Your Data Pre-processing with Python - Oct 23, 2018.
Get a 2–6x speed-up on your pre-processing with these 3 lines of code!
Data Preprocessing, Efficiency, Programming, Python
- 5 “Clean Code” Tips That Will Dramatically Improve Your Productivity - Oct 15, 2018.
TL;DR: If it isn’t tested, it’s broken; Choose meaningful names; Classes and functions should be small and obey the Single Responsibility Principle (SRP); Catch and handle exceptions, even if you don’t think you need to; Logs, logs, logs
Efficiency, Programming
- A Gentle Introduction to Bloom Filter - Aug 24, 2016.
The Bloom Filter is a probabilistic data structure which can make a tradeoff between space and false positive rate. Read more, and see an implementation from scratch, in this post.
Algorithms, Efficiency, Python
- Determining the Value of Insights - Jan 30, 2014.
With the value of Consumer Insights being questioned to justify ROI, the Market Research professionals need to figure out ways to quantify the value of those insights. Determining the value of insights is no easy task and requires focus on three key components.
Efficiency, Insight Effectiveness, Insight Quality, Market Research
