Code from the NNFS book (https://nnfs.io) separated by chapter. The NNFS book is written with the Python programming lanauge. If you're interested in seeing this same content, but with 40+ other programming languages, check out: https://github.com/sentdex/nnfsix
Hi in our final code we calculated Regularization Loss and then sum it with
data_Loss. Now the problem is I think we didn't add our total Loss as a final output of the last layer and then multiply partial gradients with it and instead what we did is just propagated Loss like when we don't have regularization loss.
(In summary, we didn't add the effect of Regularization Loss in forward pass phase).
And I think we should use this
loss_activation.backward(Loss, y)
instead of
loss_activation.backward(loss_activation.output, y).
Would be glad to receive your opinion.
Best Regards
Using version 1.21.0 of numpy the outputs do not match the outputs from the book. I was able to rectify this issue by finding the version you used the first video on the companion series on your Youtube channel version 1.18.2. I am not sure if you would want to add a comment on the readme, update the outputs, or update the nnfs package to work with the newer version. Adding it to the readme would be the least maintenance in the future.
In the book p 251, 271 & 276, learning_rate begins with a capitol letter "L" in the class definition, but it doesn't in this code and it shouldn't.
def init(self, learning_rate=1., decay=0., momentum=0.): self.learning_rate = learning_rate
In chapter 10 after the square root example the text is written as
Overall, the impact is the learning rates for parameters with smaller gradients are decreased slowly, while the parameters with larger gradients have their learning rates decreased faster
I am confused over this, we are not updating learning rate anywhere (other than rate decay). Yes the weights will be updated faster for parameters with bigger gradients but much slower than they would have if no normalization is used.
Am I correct or am I understanding it incorrectly.
Thank for the great book, just a minor, minor remark.. inputs = [[1, 2, 3, 2.5], [2., 5., -1., 2], <----- formatted with zeroes [-1.5, 2.7, 3.3, -0.8]]
inputs = [.. [2.0, 5.0, -1.0, 2.0], [..]]
yolox-bytetrack-sample YOLOXとByteTrackを用いたMOT(Multiple Object Tracking)のPythonサン
Code samples for "Neural Networks and Deep Learning" This repository contains code samples for my book on "Neural Networks and Deep Learning". The cod
Neural-Network-Basics Aim of this Repository: From Perceptron model to Deep Neural Network (from scratch) in Python. ** Currently working on a basic N
NuPIC Studio is an all-in-one tool that allows users create a HTM neural network from scratch, train it, collect statistics, and share it among the members of the community. It is not just a visualization tool but an HTM builder, debugger and laboratory for experiments. It is ideal for newbies with little intimacy with NuPIC code as well as experts that wish a better productivity. Among its features and advantages:
A graph neural network (GNN) model to predict protein-protein interactions (PPI) with no sample features
Complex-Valued Neural Networks (CVNN) Done by @NEGU93 - J. Agustin Barrachina Using this library, the only difference with a Tensorflow code is that y
transformer-from-scratch Code for my Medium blog post: Transformers from Scratch in PyTorch Note: This Transformer code does not include masked attent
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
PixelTransformer Code release for the ICML 2021 paper "PixelTransformer: Sample Conditioned Signal Generation". Project Page Installation Please insta
The Neural Process Family This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CN
Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks This repository contains the code that accompanies our CVPR 20
Machine Learning From Scratch About Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose
Algo-ScriptML Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The goal of this project is not t
SmallPebble Project status: experimental, unstable. SmallPebble is a minimal/toy automatic differentiation/deep learning library written from scratch
train-CLIP ?? A PyTorch Lightning solution to training CLIP from scratch. Goal ⚽ Our aim is to create an easy to use Lightning implementation of OpenA
RoBERTa base model for Marathi Language (मराठी भाषा) Pretrained model on Marathi language using a masked language modeling (MLM) objective. RoBERTa wa
Use this instead: https://github.com/facebookresearch/maskrcnn-benchmark A Pytorch Implementation of Detectron Example output of e2e_mask_rcnn-R-101-F
Mini-Keras Keras like implementation of Deep Learning architectures from scratch using numpy. How to contribute? The project contains implementations
GANs for Fun Created because I can! GOAL The goal of this repo is to be freely used by ML devs to check the GAN performances without coding from scrat
12 Nov 9, 2022
13.9k Dec 26, 2022
1 Jan 14, 2022
93 Sep 30, 2022
2 Jul 25, 2022
1 Nov 12, 2021
27 Dec 21, 2022
16.2k Dec 30, 2022
24 Dec 17, 2022
892 Dec 28, 2022
161 Dec 20, 2022
21.8k Jan 9, 2023
81 Nov 26, 2022
92 Dec 30, 2022
396 Dec 30, 2022
23 Oct 19, 2022
2.8k Dec 29, 2022
5 Oct 10, 2021
13 Jan 26, 2022