primary care, cancer and drug research, biology, robotics, space, financial services, neighborhood networks – everything. - Amazon CEO Andy Jassy in 2024 Amazon Shareholder Letter – 4/25 The chance to perception, but for path planning and vehicle controls. We replaced 330,000 lines of C++ code with neural nets. It's really quite remarkable. So, as a side note, I think Tesla is probably the most probably

shuffle(1:3000)[1:2000]; julia> function iterated_neural_network(A, x, depth) for _ in 1:depth x .= max.(0, A * x) end argmax(x) end julia> @time iterated_neural_network(view(A, inds, inds), x, 10) 0.324903 324903 seconds (12 allocations: 157.562 KiB) 1569 julia> @time iterated_neural_network(A[inds, inds], x, 10) 0.054576 seconds (13 allocations: 30.671 MiB, 13.33% gc time) 1569 Provided there is enough

shuffle(1:3000)[1:2000]; julia> function iterated_neural_network(A, x, depth) for _ in 1:depth x .= max.(0, A * x) end argmax(x) end julia> @time iterated_neural_network(view(A, inds, inds), x, 10) 0.324903 324903 seconds (12 allocations: 157.562 KiB) 1569 julia> @time iterated_neural_network(A[inds, inds], x, 10) 0.054576 seconds (13 allocations: 30.671 MiB, 13.33% gc time) 1569 Provided there is enough

PERFORMANCE TIPS 466 julia> function iterated_neural_network(A, x, depth) for _ in 1:depth x .= max.(0, A * x) end argmax(x) end julia> @time iterated_neural_network(view(A, inds, inds), x, 10) 0.324903 324903 seconds (12 allocations: 157.562 KiB) 1569 julia> @time iterated_neural_network(A[inds, inds], x, 10) 0.054576 seconds (13 allocations: 30.671 MiB, 13.33% gc time) 1569 Provided there is enough

PERFORMANCE TIPS 466 julia> function iterated_neural_network(A, x, depth) for _ in 1:depth x .= max.(0, A * x) end argmax(x) end julia> @time iterated_neural_network(view(A, inds, inds), x, 10) 0.324903 324903 seconds (12 allocations: 157.562 KiB) 1569 julia> @time iterated_neural_network(A[inds, inds], x, 10) 0.054576 seconds (13 allocations: 30.671 MiB, 13.33% gc time) 1569 Provided there is enough

PERFORMANCE TIPS 466 julia> function iterated_neural_network(A, x, depth) for _ in 1:depth x .= max.(0, A * x) end argmax(x) end julia> @time iterated_neural_network(view(A, inds, inds), x, 10) 0.324903 324903 seconds (12 allocations: 157.562 KiB) 1569 julia> @time iterated_neural_network(A[inds, inds], x, 10) 0.054576 seconds (13 allocations: 30.671 MiB, 13.33% gc time) 1569 Provided there is enough

julia> function iterated_neural_network(A, x, depth) for _ in 1:depth x .= max.(0, A * x) endCHAPTER 35. PERFORMANCE TIPS 480 argmax(x) end julia> @time iterated_neural_network(view(A, inds, inds) inds), x, 10) 0.324903 seconds (12 allocations: 157.562 KiB) 1569 julia> @time iterated_neural_network(A[inds, inds], x, 10) 0.054576 seconds (13 allocations: 30.671 MiB, 13.33% gc time) 1569 Provided

julia> function iterated_neural_network(A, x, depth) for _ in 1:depth x .= max.(0, A * x) endCHAPTER 35. PERFORMANCE TIPS 481 argmax(x) end julia> @time iterated_neural_network(view(A, inds, inds) inds), x, 10) 0.324903 seconds (12 allocations: 157.562 KiB) 1569 julia> @time iterated_neural_network(A[inds, inds], x, 10) 0.054576 seconds (13 allocations: 30.671 MiB, 13.33% gc time) 1569 Provided

julia> function iterated_neural_network(A, x, depth) for _ in 1:depth x .= max.(0, A * x) endCHAPTER 35. PERFORMANCE TIPS 480 argmax(x) end julia> @time iterated_neural_network(view(A, inds, inds) inds), x, 10) 0.324903 seconds (12 allocations: 157.562 KiB) 1569 julia> @time iterated_neural_network(A[inds, inds], x, 10) 0.054576 seconds (13 allocations: 30.671 MiB, 13.33% gc time) 1569 Provided

julia> function iterated_neural_network(A, x, depth) for _ in 1:depth x .= max.(0, A * x) endCHAPTER 35. PERFORMANCE TIPS 480 argmax(x) end julia> @time iterated_neural_network(view(A, inds, inds) inds), x, 10) 0.324903 seconds (12 allocations: 157.562 KiB) 1569 julia> @time iterated_neural_network(A[inds, inds], x, 10) 0.054576 seconds (13 allocations: 30.671 MiB, 13.33% gc time) 1569 Provided