6.4. Multiple Input and Multiple Output Channels — Dive into Deep Learning 0.17.5 documentation

While we have described the multiple channels that comprise each image (e.g., color images have the standard RGB channels to indicate the amount of red, green and blue) and convolutional layers for multiple channels in Section 6.1.4.1, until now, we simplified all of our numerical examples by working with just a single input and a single output channel. This has allowed us to think of our inputs, convolution kernels, and outputs each as two-dimensional tensors.

7.4. Multiple Input and Multiple Output Channels — Dive into Deep Learning 1.0.3 documentation 7.4. Multiple Input and Multiple Output Channels ¶ Colab [pytorch] Open the notebook in Colab Colab [mxnet] Open the notebook in Colab Colab [jax] Open the notebook in Colab Colab [tensorflow] Open the notebook in Colab SageMaker Studio Lab Open the notebook in SageMaker Studio Lab While we described the multiple channels that comprise each image (e.g., color images have the standard RGB channels to indicate the amount of red, green and blue) and convolutional layers for multiple channels in Se

saved by

• Denizhan Akar

related reading

• CS231n Deep Learning for Computer Visioncs231n.github.io
• matlab - In Convolutional Neural Networks (CNN), how we can decide number of kernels between input and hidden layer? - Cross Validatedstats.stackexchange.com
• A Beginner's Guide To Understanding Convolutional Neural Networks – Adit Deshpande – Engineering at Forward | UCLA CS '19adeshpande3.github.io
• Convolutional Neural Networks, Explained | Towards Data Sciencetowardsdatascience.com
• Neural networks and deep learningneuralnetworksanddeeplearning.com
• Tiny TPUtinytpu.com
• reinforcement learning - How does one stack multiple observations in the input layer of a convolutional neural network? - Artificial Intelligence Stack Exchangeai.stackexchange.com
• A Beginner's Guide To Understanding Convolutional Neural Networks Part 2 – Adit Deshpande – Engineering at Forward | UCLA CS '19adeshpande3.github.io
• The Little Book of Deep Learningfleuret.org
• Feature Visualizationdistill.pub
• Neural Networks, Types, and Functional Programming -- colah's blogcolah.github.io
• Intuitive Guide to Convolution – BetterExplainedbetterexplained.com