Automatic Differentiation Courses and Certifications

AssemblyAI

JAX Crash Course - Accelerating Machine Learning Code

Accelerate machine learning with JAX: Learn NumPy-like operations on CPU, GPU, and TPU, automatic differentiation, and high-performance techniques for ML research in this comprehensive introduction.

• YouTube
• 27 minutes
• On-Demand
• Free Video

MITCBMM

Computational Models of Cognition - Reverse-Engineering Common Sense in the Human Mind and Brain Pt 1

Explore cognitive modeling to reverse-engineer human common sense, examining AI progress, scientific context, and probabilistic programming techniques for capturing learning and constructing models.

• YouTube
• 1 hour 7 minutes
• On-Demand
• Free Video

Association for Computing Machinery (ACM)

PyTorch - A Modern Library for Machine Learning with Adam Paszke

Explore PyTorch's capabilities in machine learning, from research to production. Learn about its innovative solutions for efficient inference and its versatility across various ML scenarios.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

GOTO Conferences

Inside Every Calculus Is A Little Algebra Waiting To Get Out

Explore algebraic approaches to automatic differentiation and integration in calculus, with Kotlin code examples. Learn how synthetic differential geometry simplifies complex mathematical concepts.

• YouTube
• 47 minutes
• On-Demand
• Conference Talk

Scala Days Conferences

Differentiable Functional Programming

Explore differentiable functional programming, covering supervised learning, gradient descent, and automatic differentiation techniques for efficient deep learning implementations in Scala.

• YouTube
• 34 minutes
• On-Demand
• Conference Talk

PyCon US

Getting Started with Automatic Differentiation

Explore automatic differentiation in Python, its applications in optimization and computational art, using libraries like JAX, TensorFlow, and PyTorch. Gain intuition for derivatives and gradients.

• YouTube
• 19 minutes
• On-Demand
• Conference Talk

Strange Loop Conference

Symbolic Numeric Programming in Julia

Explore Symbolics.jl for computer-algebra metaprogramming in Julia, enabling powerful code transforms, optimization, and automatic differentiation with symbolic techniques.

• YouTube
• 37 minutes
• On-Demand
• Conference Talk

Institute for Pure & Applied Mathematics (IPAM)

Methods for Scalable Probabilistic Inference - IPAM at UCLA

Explore scalable probabilistic inference methods for astrophysics, focusing on Gaussian Processes for time series analysis and open-source tools to accelerate Bayesian inference with large datasets.

• YouTube
• 46 minutes
• On-Demand
• Free Video

IEEE

Programming Languages for High-Assurance Vehicles

Explore secure programming for high-assurance vehicles using embedded domain-specific languages in Haskell, focusing on autopilot development, verification tools, and real-world applications.

• YouTube
• 30 minutes
• On-Demand
• Free Video

The Julia Programming Language

The State of Julia in 2021 - JuliaCon Keynote

Comprehensive overview of Julia's progress in 2021, covering threading, compiler improvements, linear algebra advancements, package updates, and future development plans.

• YouTube
• 43 minutes
• On-Demand
• Free Video

The Julia Programming Language

Transformations and Automatic Differentiation in Computational Thinking - Lecture 3

Explore transformations and automatic differentiation in Julia, covering linear and non-linear transformations, function types, and applications in machine learning, with practical examples and visualizations.

• YouTube
• 54 minutes
• On-Demand
• Free Video

The Julia Programming Language

Loop Analysis and Vectorization in Julia - JuliaCon 2020

Explore LoopVectorization.jl for efficient loop representation, cost modeling, and vectorization strategies in Julia, enhancing performance through intra-core parallelism and SIMD operations.

• YouTube
• 25 minutes
• On-Demand
• Free Video

The Julia Programming Language

Flux - The Elegant Machine Learning Library for Julia

Explore Flux, an intuitive machine learning library for Julia, featuring state-of-the-art performance, automatic differentiation, and innovative compiler technology for efficient and flexible ML development.

• YouTube
• 30 minutes
• On-Demand
• Free Video

The Julia Programming Language

Tricks and Tips in Numerical Computing - Keynote

Explore advanced numerical computing techniques, from complex differentiation to low-precision arithmetic, with insights on error analysis, matrix operations, and IEEE standards for improved computational accuracy and efficiency.

• YouTube
• 56 minutes
• On-Demand
• Free Video

Alan Turing Institute

Physics-Constrained Reservoir-Computing for Turbulence and Chaotic Learning

Explore physics-constrained reservoir-computing for turbulence and chaotic learning, combining physical principles with empirical modeling to predict and optimize complex fluid flows in aerospace propulsion and turbulence.

• YouTube
• 56 minutes
• On-Demand
• Free Video