My Data Science and Big Data blog
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My Data Science and Big Data blog
5y ago
Defense against adversarial attacks using machine learning and cryptography ..read more
My Data Science and Big Data blog
5y ago
When Bayes, Ockham, and Shannon come together to define machine learning ..read more
My Data Science and Big Data blog
5y ago
Deploying Keras Deep Learning Models with Flask: It first introduces an example using Flask to set up an endpoint with Python, and then shows some of issues to work around when building a Keras endpoint for predictions with Flask ..read more
My Data Science and Big Data blog
5y ago
9 Things You Should Know About TensorFlow: New features of TensorFlow ..read more
My Data Science and Big Data blog
5y ago
Applications of Reinforcement Learning in Real World: Reinforcement Learning (RL) as a framework for computational neuroscience to model decision making process seems to be undervalued. Besides, there seems to be very little resources detailing how RL is applied in different industries. Despite the criticisms about RL’s weaknesses, RL should never be neglected in the space of corporate research given its huge potentials in assisting decision making ..read more
My Data Science and Big Data blog
5y ago
The Brilliant Ways Kimberly-Clark Uses Big Data, IoT & Artificial Intelligence To Boost Performance: Kimberly-Clark is a Fortune 500 company. It’s personal care product brands, including Huggies, Kleenex, and Scott, touch nearly 1 of every 4 people each day in 175 countries. Through Kimberly-Clark Professional the company offers products and solutions to create healthier, safer and more productive workplaces in a variety of industries including food services, healthcare, manufacturing, office buildings and more. As an industry leader, Kimberly-Clark is committed to driving digital innovati ..read more
My Data Science and Big Data blog
5y ago
Hyperparameter Optimization with Keras – Towards Data Science: With the right process in place, it will not be difficult to find state-of-the-art hyperparameter configuration for a given prediction task. Out of the three approaches — manual, machine-assisted, and algorithmic — this article will focus on machine-assisted. The article will cover how I do it, get to the proof that the method works, and provide the understanding of why it works. The main principle is simplicity.
Few Words on Performance
The first point about performance relates to the issue of accuracy (and other more robust met ..read more
My Data Science and Big Data blog
5y ago
Deep Learning Tips and Tricks – Towards Data Science: Deep Learning Techniques
Here are a few ways you can improve your fit time and accuracy with pre-trained models:
Research the ideal pre-trained architecture: Learn about the benefits of transfer learning, or browse some powerful CNN architectures. Consider domains that may not seem like obvious fits, but share potential latent features.
Use a smaller learning rate: Since pre-trained weights are usually better than randomly initialized weights, modify more delicately! Your choice here depends on the learning landscape and how well the pre ..read more