A series on automatic differentiation in Julia. Part 5 shows how the MicroGrad.jl code can be used for a machine learning framework like Flux.jl. The working...| liorsinai.github.io
A series on automatic differentiation in Julia. Part 4 extends part 3 to handle maps, getfield and anonymous functions. It creates a generic gradient descent...| liorsinai.github.io
A series on automatic differentiation in Julia. Part 3 uses metaprogramming based on IRTools.jl to generate a modified (primal) forward pass and to reverse d...| liorsinai.github.io
A series on automatic differentiation in Julia. Part 2 uses metaprogramming to generate a modified (primal) forward pass and to reverse differentiate it into...| liorsinai.github.io
Quantifying how likely each birthday is present (covered) in some large group of people.| liorsinai.github.io
Syllabus| karpathy.ai