How I Learned to Write By Writing

I was an English major long before I learned how to write well. From early childhood through K-12, I read a lot and always participated in language and writing activities — spelling bee champion in elementary school, creative writing and newspaper class in jr. high school and AP English in high school. But I didn’t start out as an English major. I wanted to be a veterinarian or a sports medicine doctor.

A ‘B’ in Anatomy class — which I deemed not good enough! — changed my mind and I jumped to the familiarity of English class, prompted by Professor Louis Owens, a literature professor who gave me some encouragement.

Enter Mr. Goldman

In my sophomore year of college, I was playing catchup with requirements. I took Introduction to Poetry with Professor Goldman. Professor Goldman was old. He shuffled when he walked and was stooped. He wore an old 50s style hat and green checkered suits. He always had an unlit stub end of a cigar in his mouth. He was gruff and impatient in class. He had a deep voice, made gravelly from cigar smoking. And we heard rumors about him.

“I heard he is in his 80s.”

“I heard he once was the chair of the department.”

“I heard he’s dying of cancer.”

This last was true. Professor Goldman was dying of cancer.

New Criticism — criticism in a bell jar

Professor Goldman loved poetry, especially the Romantics — Keats, Shelley, Wordsworth, any of the lyric poems that could be analyzed well with the New Critical approach that was popular at the time.

The New Critical approach separated a poem from its place in history and looked at the poem qua  poem. That is, it looked at the imagery and tensions that were internal to the poem and sought to understand the poem without recourse to its historical background or the biography of the author. It was like looking at a poem under a microscope, contained in a glass jar, within a vacuum.

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What You Can Learn about Setting from Classic Sitcoms

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.

#creative-writing #writing-exercise #writing-craft #writing #writing-tips #machine learning

Google Reveals "What is being Transferred” in Transfer Learning

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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5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

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:

• Temporal difference learning (TD learning)
• Parameters
• QL & SARSA
• Comparison
• Implementation
• Conclusion

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.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning

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