Giving your novel a strong sense of place is vital to doing your part to engage the readers without confusing or frustrating them. Setting is a big part of this (though not the whole enchilada — there is also social context and historic period), and I often find writing students and consulting clients erring on one of two extremes.
**Either: **Every scene is set in a different, elaborately-described place from the last. This leads to confusion (and possibly exhaustion and impatience) for the reader, because they have no sense of what they need to actually pay attention to for later and what’s just…there. Are the details of that forest in chapter 2 important? Will I ever be back in this castle again? Is there a reason for this character to be in this particular room versus the one she was in the last time I saw her? Who knows!
Or: There are few or no clues at all as to where the characters are in a scene. What’s in the room? Are they even in a room? Are there other people in th — ope, yes, there are, someone just materialized, what is happening? This all leads to the dreaded “brains in jars” syndrome. That is, characters are only their thoughts and words, with no grounding in the space-time continuum. No one seems to be in a place, in a body, at a time of day.
Everything aspect of writing a novel comes with its difficulties, and there are a lot of moving pieces to manage and deploy in the right balance. When you’re a newer writer, especially, there’s something to be said for keeping things simple until you have a handle on how to manage the arc and scope of a novel-length work. And whether you tend to overdo settings or underdo them, you can learn something from TV, especially classic sitcoms.
Your basic “live studio audience” sitcoms are performed and filmed on sets built inside studios vs. on location. This helps keep production expenses in check and helps the viewer feel at home — there’s a reliable and familiar container to hold the story of any given episode. The writers on the show don’t have to reinvent the wheel with every script.
Often, a show will have no more than two or three basic sets that are used episode to episode, and then a few other easily-understood sets (characters’ workplaces, restaurants, streets scenes) are also used regularly but not every episode.
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Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.
The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.
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These are the 10 highest paying jobs you can learn without needing a college degree. Jobs that pay $75,000 and higher.
📺 The video in this post was made by Andrei Jikh
The origin of the article: https://www.youtube.com/watch?v=QIGDA2JRz8w
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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In today’s world, Computer Vision technologies are everywhere. They are embedded within many of the tools and applications that we use on a daily basis. However, we often pay little attention to those underlaying Computer Vision technologies because they tend to run in the background. As a result, only a small fraction of those outside the tech industries know about the importance of those technologies. Therefore, the goal of this article is to provide an overview of Computer Vision to those with little to no knowledge about the field. I attempt to achieve this goal by answering three questions: What is Computer Vision?, Why should you learn Computer Vision? and How you can get started?
Figure 1: Portrait of Larry Roberts.
The field of Computer Vision dates back to the 1960s when Larry Roberts, who is now widely considered as the “Father of Computer Vision”, published his paper _Machine Perception of Three-Dimensional Solids _detailing how a computer can infer 3D shapes from a 2D image (Roberts, 1995). Since then, other researchers have made amazing contributions to the field. These advances, however, have not changed the underlaying goal of Computer Vision which is to mimic the human visual system. From an engineering point of view, this means being able to build autonomous systems that can do things a human visual system can do such as detecting and recognizing objects, recognizing faces and facial expressions, etc. (Huang, 1996). Traditionally, many approaches in Computer Vision involves manual feature extraction. This means manually finding some unique features/characteristics (edges, shapes, etc) that are only present in an object to be able to detect and recognize what that object is. Unfortunately, one major issue arises when trying to detect and recognize variations (sizes, lightning conditions, etc) of that same object. It is difficult to find features that can uniquely identify an object across all variations. Fortunately, this problem is now solved with the introduction of Machine Learning, particularly a sub-field of Machine Learning called Deep Learning. Deep Learning utilizes a form of Neural Networks called Convolutional Neural Networks (CNNs). Unlike the traditional methods, methods that utilize CNNs are able to extract features automatically. Instead of trying to figure out which features can represent an object manually, a CNN can learn those features automatically by looking at many variations of that same object. As result, many recent advancements in the field of Computer Vision involves the use of CNNs.
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Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.
Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.
This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.
The outline of this post include:
We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)
The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.
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