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Title: GitHub - dataflowr/notebooks: code for deep learning courses

Open Graph Title: GitHub - dataflowr/notebooks: code for deep learning courses

X Title: GitHub - dataflowr/notebooks: code for deep learning courses

Description: code for deep learning courses. Contribute to dataflowr/notebooks development by creating an account on GitHub.

Open Graph Description: code for deep learning courses. Contribute to dataflowr/notebooks development by creating an account on GitHub.

X Description: code for deep learning courses. Contribute to dataflowr/notebooks development by creating an account on GitHub.

Opengraph URL: https://github.com/dataflowr/notebooks

X: @github

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Apache-2.0 license	https://patch-diff.githubusercontent.com/dataflowr/notebooks
Dataflowr: Deep Learning DIY	https://www.dataflowr.com/
	https://patch-diff.githubusercontent.com/dataflowr/notebooks#dataflowr-deep-learning-diy
	https://dataflowr.github.io/website/
dataflowr	https://www.dataflowr.com/
	https://patch-diff.githubusercontent.com/dataflowr/notebooks#sunflowersessionone-finetuning-vgg
Module 1 - Introduction & General Overview	https://dataflowr.github.io/website/modules/1-intro-general-overview/
	https://patch-diff.githubusercontent.com/dataflowr/notebooks#sunflowersessiontwo-pytorch-tensors-and-autodiff
Module 2a - PyTorch tensors	https://dataflowr.github.io/website/modules/2a-pytorch-tensors/
Module 2b - Automatic differentiation	https://dataflowr.github.io/website/modules/2b-automatic-differentiation/
HW1	https://dataflowr.github.io/website/homework/1-mlp-from-scratch/
another look at autodiff with dual numbers and Julia	https://github.com/dataflowr/notebooks/blob/master/Module2/AD_with_dual_numbers_Julia.ipynb
broadcasting	https://numpy.org/doc/stable/user/basics.broadcasting.html
	https://patch-diff.githubusercontent.com/dataflowr/notebooks#sunflowersessionthree
Module 3 - Loss function for classification	https://dataflowr.github.io/website/modules/3-loss-functions-for-classification/
Module 4 - Optimization for deep learning	https://dataflowr.github.io/website/modules/4-optimization-for-deep-learning/
Module 5 - Stacking layers	https://dataflowr.github.io/website/modules/5-stacking-layers/
Stacking_layers_MLP_CIFAR10.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module5/Stacking_layers_MLP_CIFAR10.ipynb
Module 6: Convolutional neural network	https://dataflowr.github.io/website/modules/6-convolutional-neural-network/
Module 15 - Dropout	https://dataflowr.github.io/website/modules/15-dropout/
	https://patch-diff.githubusercontent.com/dataflowr/notebooks#sunflowersessionfour
Module 7 - Dataloading	https://dataflowr.github.io/website/modules/7-dataloading/
Module 8a - Embedding layers	https://dataflowr.github.io/website/modules/8a-embedding-layers/
Module 8b - Collaborative filtering	https://dataflowr.github.io/website/modules/8b-collaborative-filtering/
08_collaborative_filtering_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module8/08_collaborative_filtering_empty.ipynb
08_collaborative_filtering_1M.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module8/08_collaborative_filtering_1M.ipynb
Module 8c - Word2vec	https://dataflowr.github.io/website/modules/8c-word2vec/
08_Word2vec_pytorch_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module8/08_Word2vec_pytorch_empty.ipynb
Module 16 - Batchnorm	https://dataflowr.github.io/website/modules/16-batchnorm/
16_simple_batchnorm_eval.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module16/16_simple_batchnorm_eval.ipynb
16_batchnorm_simple.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module16/16_batchnorm_simple.ipynb
Module 17 - Resnets	https://dataflowr.github.io/website/modules/17-resnets/
ODIN_mobilenet_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module17/ODIN_mobilenet_empty.ipynb
Homework 2: Class Activation Map and adversarial examples	https://dataflowr.github.io/website/homework/2-CAM-adversarial/
	https://patch-diff.githubusercontent.com/dataflowr/notebooks#sunflowersessionfive
Module 9a: Autoencoders	https://dataflowr.github.io/website/modules/9a-autoencoders/
09_AE_NoisyAE.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module9/09_AE_NoisyAE.ipynb
Module 10: Generative Adversarial Networks	https://patch-diff.githubusercontent.com/dataflowr/notebooks/blob/master
10_GAN_double_moon.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module10/10_GAN_double_moon.ipynb
Module 13: Siamese Networks and Representation Learning	https://dataflowr.github.io/website/modules/13-siamese/
Homework 3: VAE for MNIST clustering and generation	https://dataflowr.github.io/website/homework/3-VAE/
	https://patch-diff.githubusercontent.com/dataflowr/notebooks#sunflowersessionsix
Module 11a - Recurrent Neural Networks theory	https://dataflowr.github.io/website/modules/11a-recurrent-neural-networks-theory/
Module 11b - Recurrent Neural Networks practice	https://dataflowr.github.io/website/modules/11b-recurrent-neural-networks-practice/
11_predictions_RNN_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module11/11_predictions_RNN_empty.ipynb
Module 11c - Batches with sequences in Pytorch	https://dataflowr.github.io/website/modules/11c-batches-with-sequences/
	https://patch-diff.githubusercontent.com/dataflowr/notebooks#sunflowersessionseven
Module 12 - Attention and Transformers	https://dataflowr.github.io/website/modules/12-attention/
12_seq2seq_attention.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module12/12_seq2seq_attention.ipynb
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	https://patch-diff.githubusercontent.com/dataflowr/notebooks#sunflowersessioneight
Module 9b - UNets	https://dataflowr.github.io/website/modules/9b-unet/
Module 9c - Flows	https://dataflowr.github.io/website/modules/9c-flows/
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Module 18a - Denoising Diffusion Probabilistic Models	https://dataflowr.github.io/website/modules/18a-diffusion/
ddpm_nano_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module18/ddpm_nano_empty.ipynb
ddpm_micro_sol.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module18/ddpm_micro_sol.ipynb
	https://twitter.com/marc_lelarge
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Module 1: Introduction & General Overview	https://dataflowr.github.io/website/modules/1-intro-general-overview/
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Module 2: Pytorch tensors and automatic differentiation	https://dataflowr.github.io/website/modules/2a-pytorch-tensors/
02a_basics.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module2/02a_basics.ipynb
02b_linear_reg.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module2/02b_linear_reg.ipynb
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autodiff_functional_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module2/autodiff_functional_empty.ipynb
autodiff_functional_sol.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module2/autodiff_functional_sol.ipynb
linear_regression_jax.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module2/linear_regression_jax.ipynb
AD_with_dual_numbers_Julia.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module2/AD_with_dual_numbers_Julia.ipynb
Homework 1: MLP from scratch	https://dataflowr.github.io/website/homework/1-mlp-from-scratch/
hw1_mlp.ipynb	https://github.com/dataflowr/notebooks/blob/master/HW1/hw1_mlp.ipynb
hw1_mlp_sol.ipynb	https://github.com/dataflowr/notebooks/blob/master/HW1/sol/hw1_mlp_sol.ipynb
Module 3: Loss functions for classification	https://dataflowr.github.io/website/modules/3-loss-functions-for-classification/
03_polynomial_regression.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module3/03_polynomial_regression.ipynb
Module 4: Optimization for deep leaning	https://dataflowr.github.io/website/modules/4-optimization-for-deep-learning/
04_gradient_descent_optimization_algorithms_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module4/04_gradient_descent_optimization_algorithms_empty.ipynb
04_gradient_descent_optimization_algorithms_sol.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module4/sol/04_gradient_descent_optimization_algorithms_sol.ipynb
Module 5: Stacking layers	https://dataflowr.github.io/website/modules/5-stacking-layers/
Stacking_layers_MLP_CIFAR10.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module5/Stacking_layers_MLP_CIFAR10.ipynb
MLP_CIFAR10.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module5/sol/MLP_CIFAR10.ipynb
Module 6: Convolutional neural network	https://dataflowr.github.io/website/modules/6-convolutional-neural-network/
06_convolution_digit_recognizer.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module6/06_convolution_digit_recognizer.ipynb
Homework 2: Class Activation Map and adversarial examples	https://dataflowr.github.io/website/homework/2-CAM-adversarial/
HW2_CAM_Adversarial.ipynb	https://github.com/dataflowr/notebooks/blob/master/HW2/HW2_CAM_Adversarial.ipynb
Module 8: Embedding layers	https://dataflowr.github.io/website/modules/8a-embedding-layers/
Collaborative filtering	https://dataflowr.github.io/website/modules/8b-collaborative-filtering/
Word2vec	https://dataflowr.github.io/website/modules/8c-word2vec/
08_collaborative_filtering_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module8/08_collaborative_filtering_empty.ipynb
08_collaborative_filtering_1M.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module8/08_collaborative_filtering_1M.ipynb
08_Word2vec_pytorch_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module8/08_Word2vec_pytorch_empty.ipynb
08_Playing_with_word_embedding.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module8/08_Playing_with_word_embedding.ipynb
Module 9a: Autoencoders	https://dataflowr.github.io/website/modules/9-autoencoders/
09_AE_NoisyAE.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module9/09_AE_NoisyAE.ipynb
Module 9b - UNets	https://dataflowr.github.io/website/modules/9b-unet/
UNet_image_seg.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module9/UNet_image_seg.ipynb
Module 9c - Flows	https://dataflowr.github.io/website/modules/9c-flows/
Normalizing_flows_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module9/Normalizing_flows_empty.ipynb
Normalizing_flows_sol.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module9/Normalizing_flows_sol.ipynb
Module 10 - Generative Adversarial Networks	https://dataflowr.github.io/website/modules/10-generative-adversarial-networks/
10_GAN_double_moon.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module10/10_GAN_double_moon.ipynb
Module 11 - Recurrent Neural Networks	https://dataflowr.github.io/website/modules/11b-recurrent-neural-networks-practice/
Batches with sequences in Pytorch	https://dataflowr.github.io/website/modules/11c-batches-with-sequences/
11_RNN.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module11/11_RNN.ipynb
11_predictions_RNN_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module11/11_predictions_RNN_empty.ipynb
Module 12 - Attention and Transformers	https://dataflowr.github.io/website/modules/12-attention/
PyTorch tutorial	https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html
12_seq2seq_attention.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module12/12_seq2seq_attention.ipynb
solution	https://github.com/dataflowr/notebooks/blob/master/Module12/12_seq2seq_attention_solution.ipynb
GPT_hist.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module12/GPT_hist.ipynb
solution	https://github.com/dataflowr/notebooks/blob/master/Module12/GPT_hist_sol.ipynb
Module 13 - Siamese Networks and Representation Learning	https://dataflowr.github.io/website/modules/13-siamese/
13_siamese_triplet_mnist_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module13/13_siamese_triplet_mnist_empty.ipynb
Module 15 - Dropout	https://dataflowr.github.io/website/modules/15-dropout/
15a_dropout_intro.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module15/15a_dropout_intro.ipynb
15b_dropout_mnist.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module15/15b_dropout_mnist.ipynb
Module 16 - Batchnorm	https://dataflowr.github.io/website/modules/16-batchnorm/
16_batchnorm_simple.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module16/16_batchnorm_simple.ipynb
16_simple_batchnorm_eval.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module16/16_simple_batchnorm_eval.ipynb
Module 18a - Denoising Diffusion Probabilistic Models	https://dataflowr.github.io/website/modules/18a-diffusion/
ddpm_nano_empty.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module18/ddpm_nano_empty.ipynb
ddpm_nano_sol.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module18/ddpm_nano_sol.ipynb
ddpm_micro_sol.ipynb	https://github.com/dataflowr/notebooks/blob/master/Module18/ddpm_micro_sol.ipynb
Module - Deep Learning on graphs	https://dataflowr.github.io/website/modules/graph0/
GCN_inductivebias_spectral.ipynb	https://github.com/dataflowr/notebooks/blob/master/graphs/GCN_inductivebias_spectral.ipynb
GCN_inductivebias_spectral-colab.ipynb	https://github.com/dataflowr/notebooks/blob/master/graphs/GCN_inductivebias_spectral-colab.ipynb
spectral_gnn.ipynb	https://github.com/dataflowr/notebooks/blob/master/graphs/spectral_gnn.ipynb
tiny_nerf_extended.ipynb	https://github.com/dataflowr/notebooks/blob/master/nerf/tiny_nerf_extended.ipynb
	https://patch-diff.githubusercontent.com/dataflowr/notebooks#usage
Module 0 - Running the notebooks locally	https://dataflowr.github.io/website/modules/0-sotfware-installation/
	https://patch-diff.githubusercontent.com/dataflowr/notebooks#2020-version-of-the-course
www.dataflowr.com	http://www.dataflowr.com
deep-learning	https://patch-diff.githubusercontent.com/topics/deep-learning
tutorials	https://patch-diff.githubusercontent.com/topics/tutorials
pytorch	https://patch-diff.githubusercontent.com/topics/pytorch
Readme	https://patch-diff.githubusercontent.com/dataflowr/notebooks#readme-ov-file
Apache-2.0 license	https://patch-diff.githubusercontent.com/dataflowr/notebooks#Apache-2.0-1-ov-file
Please reload this page	https://patch-diff.githubusercontent.com/dataflowr/notebooks
Activity	https://patch-diff.githubusercontent.com/dataflowr/notebooks/activity
Custom properties	https://patch-diff.githubusercontent.com/dataflowr/notebooks/custom-properties
1.2k stars	https://patch-diff.githubusercontent.com/dataflowr/notebooks/stargazers
27 watching	https://patch-diff.githubusercontent.com/dataflowr/notebooks/watchers
331 forks	https://patch-diff.githubusercontent.com/dataflowr/notebooks/forks
Report repository	https://patch-diff.githubusercontent.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fdataflowr%2Fnotebooks&report=dataflowr+%28user%29
Releases	https://patch-diff.githubusercontent.com/dataflowr/notebooks/releases
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Contributors 7	https://patch-diff.githubusercontent.com/dataflowr/notebooks/graphs/contributors
Please reload this page	https://patch-diff.githubusercontent.com/dataflowr/notebooks
Jupyter Notebook 99.7%	https://patch-diff.githubusercontent.com/dataflowr/notebooks/search?l=jupyter-notebook
Python 0.3%	https://patch-diff.githubusercontent.com/dataflowr/notebooks/search?l=python
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