Computerized batch processing is a method of running software programs called jobs in batches automatically. While users are required to submit the jobs, no other interaction by the user is required to process the batch. Batches may automatically be run at scheduled times as well as being run contingent on the availability of computer resources. (Wikipedia).
Filter, epoch, baseline subtraction, referencing
This lecture provides a brief overview of EEG preprocessing steps. For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/
From playlist OLD ANTS #6) Data pre-processing and cleaning
Scheduling: The List Processing Algorithm Part 1
This lesson explains and provides an example of the list processing algorithm to make a schedule given a priority list. Site: http://mathispower4u.com
From playlist Scheduling
Introduction to Signal Processing
http://AllSignalProcessing.com for free e-book on frequency relationships and more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Introductory overview of the field of signal processing: signals, signal processing and applications, phi
From playlist Introduction and Background
Live Stream: Process control in C
Using fork(), exec(), and wait() in C programs. And, if there's time and interest, threading in Java.
From playlist C Programming
Compilation - Part One: Overview of the Stages of Compilation
This is part one of a series of videos about compilation. As you will see when you watch this series, compilation involves a diverse range of themes in the field of computer science including high and low level programming paradigms, the definition of context free grammars, the application
From playlist Compilation
Group Normalization (Paper Explained)
The dirty little secret of Batch Normalization is its intrinsic dependence on the training batch size. Group Normalization attempts to achieve the benefits of normalization without batch statistics and, most importantly, without sacrificing performance compared to Batch Normalization. htt
From playlist Papers Explained
Overview of compiling a program
Compiling a program takes place over several stages. This video is an overview of the compilation process: scanner/lexer, parser, semantic analyzer, code generator, and optimizer. An introduction to token streams and abstract syntax trees.
From playlist Discrete Structures
(ML 19.1) Gaussian processes - definition and first examples
Definition of a Gaussian process. Elementary examples of Gaussian processes.
From playlist Machine Learning
Heap Sort - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Batch Normalization (“batch norm”) explained
Let's discuss batch normalization, otherwise known as batch norm, and show how it applies to training artificial neural networks. We also briefly review general normalization and standardization techniques, and we then see how to implement batch norm in code with Keras. 🕒🦎 VIDEO SECTIONS
From playlist Deep Learning Fundamentals - Intro to Neural Networks
RailsConf 2018: Human Powered Rails: Automated Crowdsourcing In Your RoR App by Andy Glass
RailsConf 2018: Human Powered Rails: Automated Crowdsourcing In Your RoR App by Andy Glass Machine learning and AI are all the rage, but there’s often no replacement for real human input. This talk will explore how to automate the integration of human-work directly into a RoR app, by enab
From playlist RailsConf 2018
Spark Tutorial For Beginners | Big Data Spark Tutorial | Apache Spark Tutorial | Simplilearn
🔥Professional Certificate Program In Data Engineering: https://www.simplilearn.com/pgp-data-engineering-certification-training-course?utm_campaign=BigData-QaoJNXW6SQo&utm_medium=DescriptionFirstFold&utm_source=youtube This Spark Tutorial For Beginners will give an overview on the history
From playlist Big Data Hadoop Tutorial Videos | Simplilearn [2022 Updated]
Faster Neural Network Training with Data Echoing (Paper Explained)
CPUs are often bottlenecks in Machine Learning pipelines. Data fetching, loading, preprocessing and augmentation can be slow to a point where the GPUs are mostly idle. Data Echoing is a technique to re-use data that is already in the pipeline to reclaim this idle time and keep the GPUs bus
From playlist Papers Explained
Mod-01 Lec-03 Design Equations – I
Advanced Chemical Reaction Engineering (PG) by Prof. H.S.Shankar,Department of Chemical Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Bombay: Advanced Chemical Reaction Engineering | CosmoLearning.org
Image Preparation for Convolutional Neural Networks with TensorFlow's Keras API
In this episode, we'll go through all the necessary image preparation and processing steps to get set up to train our first convolutional neural network (CNN) using TensorFlow's Keras API. 🕒🦎 VIDEO SECTIONS 🦎🕒 00:00 Welcome to DEEPLIZARD - Go to deeplizard.com for learning resources 00:2
From playlist TensorFlow - Python Deep Learning Neural Network API
From playlist fastai v2 code walk-thrus
Mini Batch Gradient Descent | Deep Learning | with Stochastic Gradient Descent
Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of updating the weight parameters after assessing the entire dataset, Mini Batch Gradient Descent updates weight parameters after assessing the small batch of the datase
From playlist Optimizers in Machine Learning
Live CEOing Ep 345: RemoteSubmit in Wolfram Language
Stephen Wolfram discusses the design of the RemoteSubmit Wolfram Language function. If you'd like to contribute to the discussion in future videos and livestreams, you can participate through this YouTube channel or through the official Twitch channel of Stephen Wolfram here: https://www.t
From playlist Behind the Scenes in Real-Life Software Design
Batch optimization of expensive functions (i.e. simulations)
This video is #5 in the Adaptive Experimentation series presented at the 18th IEEE Conference on eScience in Salt Lake City, UT (October 10-14, 2022). In this video, Sterling Baird presents how to optimize machine learning models using Ax in situations where experiments are expensive, such
From playlist Optimization tutorial
C Programming: Sorting and searching arrays of structs
In this session we learn how to sort an array of structs, then search it using the built-in binary search (bsearch) function.
From playlist C Programming