Канал: LSST-DA Data Science Fellowship

Session 20: The Art of Cadence Diplomacy

Session 20: The Art of Cadence Diplomacy

Session 21: Databases, SQL, and Django

Session 21: Databases, SQL, and Django

Session 21: GPU Accelerated Python

Session 21: GPU Accelerated Python

Session 21: High Performance Python and Parallelization

Session 21: High Performance Python and Parallelization

Session 21: Endgame Essentials (Object Oriented Programming)

Session 21: Endgame Essentials (Object Oriented Programming)

Session 22: Web Based Visualization

Session 22: Web Based Visualization

Session 22: Principles of Perception

Session 22: Principles of Perception

Session 22: ML Enabled Visualization

Session 22: ML Enabled Visualization

Session 22: Introduction to Visualization

Session 22: Introduction to Visualization

Session 20: Autoregression for Time Series

Session 20: Autoregression for Time Series

Session 20: Matched Filtering

Session 20: Matched Filtering

Session 20: Regression for Time Series Part 2

Session 20: Regression for Time Series Part 2

Session 20: Regression for Time Series (Part 1)

Session 20: Regression for Time Series (Part 1)

Session 20: Classical Periodogram

Session 20: Classical Periodogram

Session 20: Lomb Scargle Periodograms (Part 2)

Session 20: Lomb Scargle Periodograms (Part 2)

Session 20: Lomb Scargle Periodograms (Part 1)

Session 20: Lomb Scargle Periodograms (Part 1)

Session 21: NoSQL Databases and MongoDB

Session 21: NoSQL Databases and MongoDB

Session 21: Object Oriented Programming Deep Dive

Session 21: Object Oriented Programming Deep Dive

Session 21: Intro to Object Oriented Programming

Session 21: Intro to Object Oriented Programming

Session 21: Data Wranglin'

Session 21: Data Wranglin'

DSFP Session 19: Introduction to Graph Neural Networks

DSFP Session 19: Introduction to Graph Neural Networks

DSFP Session 19: Introduction to Convolutional Neural Networks

DSFP Session 19: Introduction to Convolutional Neural Networks

DSFP Session 17: PolyPhorm and PolyPhy

DSFP Session 17: PolyPhorm and PolyPhy

Session 17: Physically Based Volume Rendering

Session 17: Physically Based Volume Rendering

DSFP Session 17: Visual Exploration Design and Communication

DSFP Session 17: Visual Exploration Design and Communication

DSFP Session 16: Probabilistic Graphical Models

DSFP Session 16: Probabilistic Graphical Models

DSFP Session 17: Principles of Perception

DSFP Session 17: Principles of Perception

DSFP Session 16: Hierarchical Models

DSFP Session 16: Hierarchical Models

DSFP Session 16: Connecting Models and Data

DSFP Session 16: Connecting Models and Data

DSFP Session 16: Introduction to Bayesian Statistics

DSFP Session 16: Introduction to Bayesian Statistics

DSFP Session 16: Introduction to Sampling

DSFP Session 16: Introduction to Sampling

Session 14: Probabilistic Machine Learning & Considerations for Real Data (Lecture IX)

Session 14: Probabilistic Machine Learning & Considerations for Real Data (Lecture IX)

Session 14: Introduction to Bayesian Statistics & Variational Autoencoders (Lecture VII)

Session 14: Introduction to Bayesian Statistics & Variational Autoencoders (Lecture VII)

Session 14: Dimensionality Reduction Representation Learning and Autoencoders (Lecture VII)

Session 14: Dimensionality Reduction Representation Learning and Autoencoders (Lecture VII)

Session 14: Introduction to Deep Learning (Lecture VI)

Session 14: Introduction to Deep Learning (Lecture VI)

Session 14: Building Perceptrons for Classification (Lecture V)

Session 14: Building Perceptrons for Classification (Lecture V)

Session 14: Feature Engineering (Lecture IV)

Session 14: Feature Engineering (Lecture IV)

Session 14: Introduction to Supervised Learning (Lecture III)

Session 14: Introduction to Supervised Learning (Lecture III)

Session 14: Introduction to Unsupervised Learning (Lecture II)

Session 14: Introduction to Unsupervised Learning (Lecture II)

Session 14: Introduction to Machine Learning (Lecture I)

Session 14: Introduction to Machine Learning (Lecture I)

Session 10: Model Selection (Lecture X)

Session 10: Model Selection (Lecture X)

Session 10: Missing Information and Selection Effects: Challenges for Astronomers (Lecture IX)

Session 10: Missing Information and Selection Effects: Challenges for Astronomers (Lecture IX)

Session 10: Regularization & Ridge Regression (Lecture VIII)

Session 10: Regularization & Ridge Regression (Lecture VIII)

Session 10: An Introduction to Hierarchical Bayesian Models (Lecture VII)

Session 10: An Introduction to Hierarchical Bayesian Models (Lecture VII)

Session 10: Model Building (Lecture VI)

Session 10: Model Building (Lecture VI)

Session 10: Probabilistic Graphical Models (Lecture V)

Session 10: Probabilistic Graphical Models (Lecture V)

Session 10: Advanced MCMC (Lecture IV)

Session 10: Advanced MCMC (Lecture IV)

Session 10: An Introduction to MCMC Sampling (Lecture III)

Session 10: An Introduction to MCMC Sampling (Lecture III)

Session 10: An Introduction to Bayesian Statistics (Lecture II)

Session 10: An Introduction to Bayesian Statistics (Lecture II)

Session 10: Understanding Data (Lecture I)

Session 10: Understanding Data (Lecture I)

Session 12: Effective Visuals (Lecture IV)

Session 12: Effective Visuals (Lecture IV)

Session 12: Who, What, Where, When, Why, of 3D (Lecture III)

Session 12: Who, What, Where, When, Why, of 3D (Lecture III)

Session 12: Story Telling With Data (Lecture II)

Session 12: Story Telling With Data (Lecture II)

Session 12: Building Salience In Scientific Figures (Lecture I)

Session 12: Building Salience In Scientific Figures (Lecture I)

Session 13: Signal Processing And Matched Filtering (Lecture V)

Session 13: Signal Processing And Matched Filtering (Lecture V)

Session 13: Non-parameteric Measures Of Periodicity (Lecture IV)

Session 13: Non-parameteric Measures Of Periodicity (Lecture IV)

Session 13: Introduction To The Lomb-Scargle Periodogram (Lecture III)

Session 13: Introduction To The Lomb-Scargle Periodogram (Lecture III)

Session 13: Introduction to Gaussian Processes (Lecture II)

Session 13: Introduction to Gaussian Processes (Lecture II)

Session 13: Introduction To Fourier Series And Transforms (Lecture I)

Session 13: Introduction To Fourier Series And Transforms (Lecture I)

Session 13: The Inevitability of the Time Domain (Lecture 0)

Session 13: The Inevitability of the Time Domain (Lecture 0)

Session 11: An Introduction to Visualizing Astronomical Images (Lecture VIII)

Session 11: An Introduction to Visualizing Astronomical Images (Lecture VIII)

Session 11: Image Coaddition And Subtraction (Lecture VII)

Session 11: Image Coaddition And Subtraction (Lecture VII)

Session 11: Precision Galaxy Measurements (Lecture VI)

Session 11: Precision Galaxy Measurements (Lecture VI)

Session 11: PSF Photometry (Lecture V)

Session 11: PSF Photometry (Lecture V)

Session 11: Introduction To Survey Astrometry (Lecture IV)

Session 11: Introduction To Survey Astrometry (Lecture IV)

Session 11: Sky Background Estimation and Source Detection (Lecture III)

Session 11: Sky Background Estimation and Source Detection (Lecture III)

Session 11: Sensor Effects In CCDs (Lecture II)

Session 11: Sensor Effects In CCDs (Lecture II)

Session 11: Photons != Science, and the Challenges of Turning the Former Into the Later (Lecture I)

Session 11: Photons != Science, and the Challenges of Turning the Former Into the Later (Lecture I)

Session 3: Profiling Python Software

Session 3: Profiling Python Software

Session 3: Introduction to Computation at Scale - NSF resources 2

Session 3: Introduction to Computation at Scale - NSF resources 2

Session 3: PEP8 Style Guide for Python

Session 3: PEP8 Style Guide for Python

Session 3: Data Management at Scale - trends from industry

Session 3: Data Management at Scale - trends from industry

Session 3: Introduction to Computation at Scale - NSF resources 1

Session 3: Introduction to Computation at Scale - NSF resources 1

Session 3: Coping with Streaming Data - ANTARES

Session 3: Coping with Streaming Data - ANTARES

Session 3: Introduction to DataLab

Session 3: Introduction to DataLab

Session 3: Algorithm Development and Parallel Programming in Python

Session 3: Algorithm Development and Parallel Programming in Python

Session 3: Data Management -- Big & Small

Session 3: Data Management -- Big & Small

Session 3: Making Databases Work for You

Session 3: Making Databases Work for You

Session 3: Object Oriented Programming

Session 3: Object Oriented Programming

Session 3: Software documentation

Session 3: Software documentation

Session 3: Introduction to Code Repositories

Session 3: Introduction to Code Repositories

Session 3: Unit Tests and Continuous Integration

Session 3: Unit Tests and Continuous Integration

Session 3: ReIntroduction to Databases

Session 3: ReIntroduction to Databases

Session 3: ReIntroduction to Software Engineering

Session 3: ReIntroduction to Software Engineering

Session 2: The Markov Chain Monte Carlo Method

Session 2: The Markov Chain Monte Carlo Method

Session 2: Advanced Neural Networks Talk

Session 2: Advanced Neural Networks Talk

Session2: Developing the machine-learning workflow

Session2: Developing the machine-learning workflow

Session 2: Basic Neural Networks Talk

Session 2: Basic Neural Networks Talk

Session 2: Visualizing Images

Session 2: Visualizing Images

Session 2: How to interpret (or not) machine-learning models

Session 2: How to interpret (or not) machine-learning models

Session 2: Intro to Machine Learning in Astronomy

Session 2: Intro to Machine Learning in Astronomy

Session 2: Expectation Maximization

Session 2: Expectation Maximization

Session 2: Unsupervised Machine Learning

Session 2: Unsupervised Machine Learning

Session 2: Visualization for Communication

Session 2: Visualization for Communication

Session 2: A (Re)Introduction To Machine Learning

Session 2: A (Re)Introduction To Machine Learning

Session 1: Intro to databases

Session 1: Intro to databases

Session 1: Intro to Visualization

Session 1: Intro to Visualization

Session 1: Databases hands-on

Session 1: Databases hands-on

Session 1: Intro to Image Processing

Session 1: Intro to Image Processing

Session 1: Intro to Image Processing - 2

Session 1: Intro to Image Processing - 2

Session 1: Bayesian models break-out

Session 1: Bayesian models break-out

Session 1: Introduction to advanced inference

Session 1: Introduction to advanced inference

Session 1: Intro to Statistics - 2

Session 1: Intro to Statistics - 2

Session 1: Intro to Statistics

Session 1: Intro to Statistics

Session 1: Everything an Astronomer Needs to Know About Software Engineering

Session 1: Everything an Astronomer Needs to Know About Software Engineering

Session 1: Hands-on introduction to git - 2

Session 1: Hands-on introduction to git - 2

Session 1: Hands-on introduction to git - 3

Session 1: Hands-on introduction to git - 3

Session 1: Hands-on introduction to git - 1

Session 1: Hands-on introduction to git - 1

Session 1: Intro to LSST & Goals of the DSFP

Session 1: Intro to LSST & Goals of the DSFP

Introduction to Session 1

Introduction to Session 1