Implementing a machine learning algorithm will give you a deep and practical appreciation for how the algorithm works. This knowledge can also help you to internalize the mathematical description of the algorithm by thinking of the vectors and matrices as arrays and the computational intuitions for t...
In this post I’ll share with you the strategy I have been using for years to learn and build up a structured description of an algorithm in a step-by-step manner that I can add to, refine and refer back to again and again. I even used it to write a book. This was just a stra...
A Step-By-Step Guide What is Machine Learning? Machine Learning allows systems to learn and improve from past data and experiences without having to be programmed. It generally focuses on the overall development of numerous computer programs to make them capable of accessing and processing large am...
To calculate the design, Huang had strapped Ko into a virtual-reality headset and then attached the headset to a rack of Nvidia G.P.U.s, so that Ko could track the flow of light. “This is the world’s first building that needed a supercomputerto be possible,” Huang said. Following ...
Along with this guidance, keep other requirements in mind when choosing a machine learning algorithm. Following are additional factors to consider, such as the accuracy, training time, linearity, number of parameters and number of features.
Along with this guidance, keep other requirements in mind when choosing a machine learning algorithm. Following are additional factors to consider, such as the accuracy, training time, linearity, number of parameters and number of features.
Learn what are machine learning models, the different types of models, and how to build and use them. Get images of machine learning models with applications.
"Givenan audience, an explainable AI is one that produces details or reasons to make its functioning clear or easy to understand." 给定一个受众,可解释的人工智能是指能够提供细节或理由,使其功能清晰或易于理解的人工智能。 这里为什么要强调给定一个受众呢,因为对于不同人来说,用来解释的细节和原因是不...
Did we implement and use gradient checking to make sure that our implementation is correct? Do we use a random weight initialization scheme (e.g., from a random normal distribution multiplied by a small coefficient < 0) vs. initializing the model parameters to all-zero weights?
I am having a question on how to label training data for YOLO algorithm. Let's say that each label Y, we need to specify [Pc, bx, by, bh, bw], where Pc is the indicator for presence(1=present, 0=not present), (bx, by) is relative position of the center of the object-of-in...