kai
Programming Machine Learning:
Programming Machine Learning
From Coding to Deep Learning
German edition
Chapter 11
Training the network
Page 191
def back(X, Y, y_hat, w2, h):
w2_gradient = np.matmul(prepend_bias(h).T, y_hat - Y) / X.shape[0]
a_gradient = np.matmul(y_hat - Y, w2[1:].T) * sigmoid_gradient(h)
w1_gradient = np.matmul(prepend_bias(X).T, a_gradient) / X.shape[0]
return (w1_gradient, w2_gradient)
Hi Paolo,
can you explain me how to come to the decision to multiply this expression:
np.matmul(y_hat - Y, w2[1:].T) * sigmoid_gradient(h)
element by element?
Sincerely,
Kai
Most Liked
kai
As I understand it, there is no recipe to derive compositions of functions, whose inputs are matrices, according to them, as it is the case with a scalar.
Thank you for giving me an answer that helped my understanding anyway.
I am fascinated by mathematics and its power. But to be able to see its beauty, it requires a lot of time. That is why I asked such a question.
Your book was recommended to me and declared very worthwhile, which I can confirm 100%. It is my introduction to ML. Thank you for it!
Translated with DeepL Translate: The world's most accurate translator (free version)
Popular Pragmatic Bookshelf topics
Modern Front-End Development for Rails - application does not start after run bin/setup (page xviii)
Other popular topics
Categories:
Sub Categories:
Popular Portals
- /elixir
- /rust
- /wasm
- /ruby
- /erlang
- /phoenix
- /keyboards
- /python
- /js
- /rails
- /security
- /go
- /swift
- /vim
- /clojure
- /java
- /emacs
- /haskell
- /svelte
- /onivim
- /typescript
- /kotlin
- /c-plus-plus
- /crystal
- /tailwind
- /react
- /gleam
- /ocaml
- /elm
- /flutter
- /vscode
- /ash
- /html
- /opensuse
- /zig
- /centos
- /deepseek
- /php
- /scala
- /react-native
- /lisp
- /sublime-text
- /textmate
- /nixos
- /debian
- /agda
- /deno
- /django
- /kubuntu
- /arch-linux
- /nodejs
- /ubuntu
- /spring
- /revery
- /manjaro
- /lua
- /julia
- /diversity
- /markdown
- /slackware
Devtalk Sponsors
Practical resources that improve the lives of professional developers.
We build trusted fault-tolerant systems that scale to billions of users.
Real-time error tracking, performance insights, and observability for devs.
Supporting innovation across the BEAM ecosystem.
We build reliable cloud platforms for business-critical systems.
Develop your skills with books, videos, and courses.
Catch errors, track performance, monitor hosts and more.
Enabling companies to succeed by building software people love.
Courses that move you from confusion to "Aha, now I get it!"
Publishing Linux Magazine, ADMIN, and MakerSpace for 25+ years.