Any ideas on how to do cherry picking automatically rather then human effort? Maybe using some metrics (e.g. Inception Score) ?

Hi all. I am working on a project where I want to identify chess pieces. I found that several people have solved this in different ways. Originally I wanted to identify real chess pieces from an overhead camera but I think now I will instead use flat pieces like this https://smile.amazon.com/gp/product/B0019F8QHO/ref=crt_ewc_title_srh_2?ie=UTF8&psc=1&smid=A3IXHP9WB8UQLD to make my job a bit easier and get something working. What do you guys think would be a good way to approach this problem?

So my scenario is:

1. I want to edit on my Mac
2. I have a powerful (Windows) PC that I’d like to run code on.
3. Is there a way to bring the two together?

I’d like another opinion before I get started with one solution. I’m considering:

• Remote desktop (very slow and sluggish, but could work)
• Run a Jupyter docker image on Windows and access it on my Mac (My go-to IDE for Python is PyCharm, and I’ve never used Jupyter so I’m not sure if that is a good compromise)

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Any suggestions?

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Edit:

• I am now considering running the “powerful” pc as a Linux server and configuring a “remote interpreter” using PyCharm. https://www.jetbrains.com/help/pycharm/configuring-remote-interpreters-via-ssh.html

Abstract: Classic theories of reinforcement learning and neuromodulation rely on reward prediction errors. A new machine learning technique relies on neuromodulatory signals that are optimized for specific tasks, which may lead to better AI and better explanations of neuroscience data.

Paper: https://www.nature.com/articles/s42256-019-0053-0

Do you still use Conditional Random Fields for your sequence labeling tasks? If you do, when do you prefer them over bidirectional RNNs?

Are CRFs still relevant for computer vision tasks?

I’m thinking about the need of explaining CRFs in the additional materials for my book’s wiki. Not sure if CRF is still worth being known in detail.

Looking for advice to help me prepare for a Master’s degree in Computer Science later this year. In undergrad I majored Physics and Applied Math, and in the years since saw cool realms of application for ML in industry and research. Unfortunately because of my background experience I now have some imposters syndrome setting in, well before I’ve stepped foot in any classroom. Hoping people can look at my goals, courses, and experience to try to suggest steps to better prepare.

I’m hoping to gain essentially three things from this degree:

• Knowledge of the computational foundations of popular or common ML algorithms (because its cool)
• basic skills and best practices of an ML practitioner (because its practical)
• Some modest amount of experience in projects and homework for the above (because its practical)

I would hope that I can get a job as an ML engineer/scientist/whatever afterwards, but preferably not closely tied to the ops side of design and implementation.

Summary of any related experience:

• Classification and clustering algorithms applied in medical imaging/histology field in a mish mash of R scripts, some published but never really rigorous or vetted I guess
• Using numpy to implement Q-learning in a little baby neural net on the OpenAI cart-pole example
• Helping write components of a django app that does lots of data integration from multiple sources
• I still really like math after undergrad

Any and all advice appreciated. Should I dust off a linear algebra textbook? Should I start in on a class textbook before the term starts? Try to play with a big sample dataset with different learning tools?

Some example classes I want to take below:

Machine learning

This course will introduce the field of machine learning, in particular focusing on the core concepts of supervised and unsupervised learning. In supervised learning we will discuss algorithms which are trained on input data labelled with a desired output, for instance an image of a face and the name of the person whose face it is, and learn a function mapping from the input to the output. Unsupervised learning aims to discover latent structure in an input signal where no output labels are available, an example of which is grouping web-pages based on the topics they discuss. Students will learn the algorithms which underpin many popular machine learning techniques, as well as developing an understanding of the theoretical relationships between these algorithms. The practicals will concern the application of machine learning to a range of real-world problems.

Foundations of CS (since I have no formal CS training)

Students taking this course will gain background knowledge that will be useful in the course on:

Theory of Data & Knowledge Bases Automata, Logics & Games Software Verification Categories, Proofs & Processes Game Semantics Computer-Aided Formal Verification Lambda Calculus & Types Logic of Multi-Agent Information Flow

Artificial Intelligence (intro)

This is an introductory course into the field of artificial intelligence (AI), with particular focus on search as the fundamental technique for solving AI problems.

This course also deals with optimization problems. For example, the optimization version of the n‐queens problem is to arrange n queens on an n x n chessboard while minimizing the number of pairs of queens that are under attack. Such problems can be effectively solved by search techniques introduced in the course such as hill climbing, simulated annealing, and genetic algorithms…

Computational learning theory (I see this as directly applicable to my goals)

The course will begin by providing a statistical and computational toolkit, such as concentration inequalities, fundamental algorithms, and methods to analyse learning algorithms. We will cover questions such as when can we generalise well from limited amounts of data, how can we develop algorithms that are computationally efficient, and understand statistical and computational trade-offs in learning algorithms. We will also discuss new models designed to address relevant practical questions of the day, such as learning with limited memory, communication, privacy, and labelled and unlabelled data. In addition to core concepts from machine learning, we will make connections to principal ideas from information theory, game theory and optimisation.