Privacy and machine learning

In machine learning applications, privacy should be considered an ally, not an enemy. With the improvement of technology. Differential privacy is likely to be an effective regularization tool that produces a better behavioral model. For machine learning researchers, even if they don't understand the knowledge of privacy protection, they can protect the training data in machine learning through the PATE framework.

Comparison of 6 platforms such as AWS, Google Cloud and IBM

Start-up company Rare Technologies recently released a hyperscale machine learning benchmark that focuses on GPUs and compares the performance of machine learning costs, ease of use, stability, scalability and performance with several popular hardware providers.

The Future of Machine Learning - Deep Feature Fusion

The concept of machine learning was first born in science fiction, and its new features were quickly discovered and applied, but with the inevitable limitations.

Machine learning and Docker containers

Machine learning (ML) and artificial intelligence (AI) are now hot topics in the IT industry. Similarly, containers have become one of the hot topics. We introduce both machine learning and containers into the image, and experiment to verify that they will work together to accomplish the classification task.

How to choose the appropriate machine learning algorithm

Machine learning is a combination of art and science. No machine learning algorithm can solve all the problems. There are several factors that can influence your decision to choose a machine learning algorithm.

Ten truths you must know about machine learning

Machine learning means learning from data; AI is a buzzword. Machine learning is not like the hype of hype: by providing the appropriate training data to the appropriate learning algorithms, you can solve countless problems.

Several algorithmic common sense you have to know in machine learning

We already know that we want to have a generalization ability of models learned through machine learning. In a straightforward way, it is that the learned model not only works well in the training samples, but also works in new samples well.

Machine Learning Algorithm Selection Guide

There are many articles on machine learning algorithms that detail the related algorithms, it is still very difficult to make the most appropriate choices.

8 neural network architectures that machine learning researchers need to understand

In this article, I want to share with you 8 neural network architectures. I believe that any machine learning researcher should be familiar with this process to promote their work.

How to choose the most suitable machine learning algorithm for your regression problem

The performance of different machine learning algorithms depends on the size and structure of the data. Therefore, unless we use traditional trial and error experiments, we have no clear way to prove that a choice is right.

Why machine learning is difficult to apply

Developing new machine learning algorithms and describing how they work and why work is a science is often not necessary when developing a learning system.

Python Language Machine Learning Library Top 10

With the development and popularity of artificial intelligence technology, Python has surpassed many other programming languages and has become one of the most popular and most commonly used programming languages in the field of machine learning.

Machine learning = statistics on "new bottled old wine"

This paper raises objections to this view, thinking that machine learning ≠ data statistics, deep learning has made a significant contribution to our handling of complex unstructured data problems, and artificial intelligence should be appreciated.

How to get more out of machine learning data

For machine learning, the right data set and the right model structure are critical. Choosing the wrong data set or the wrong model structure may result in a poorly performing network model, and may even get a non-converged network model.

Machine learning, deep learning and AI: What is the difference

The article is about machine learning, deep learning and AI: What is the difference? When it comes to new data processing techniques, we often hear many different terms. Some people say that they are using machine learning, while others call it artificial intelligence.

Advantages and Disadvantages of 13 Algorithms for Machine Learning

In this article we analyzed the advantages and disadvantages of 13 algorithms of machine learning, including: Regularization Algorithms, Ensemble Algorithms, Decision Tree Algorithm, Artificial Neural Network, Deep Learning, etc.

Important aspects of machine learning

Machine learning sounds like a wonderful concept, and it does, but there are some processes in machine learning that are not so automated. In fact, when designing a solution, many times manual operations are required.

Machine learning gentle guide

Machine learning is the most advanced aspect of the field of artificial intelligence today, and more beginners have begun to enter this field.

Learn Python for Machine learning algorithms

Machine learning uses algorithms to extract information from raw data and present it in some type of model. We use this model to infer other data that has not been modeled.

Machine learning algorithms and Python learning

In the past decade, there has been a surge in interest in machine learning. Almost every day, we can see discussions about machine learning in a variety of computer science courses, industry conferences, the Wall Street Journal, and more.

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