Python code accompanying the course "A deep understanding of deep learning (with Python intro)"
Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.
See https://www.udemy.com/course/deeplearning_x/?couponCode=202302 for more details, preview videos, and to enroll in the full course.
- Deep Understanding Of Deep Learning
- Math
- Gradient Descent
- ANN
- Overfitting and cross-validation
- Regularization
- Meta Parameters (activations,optimizers)
- FFN (Feed-Forward-Networks)
- More On Data
- Measuring Model Performance
- FFN Milestone Projects
- Weight Inits And Investigation
- Autoencoders
- Running models on a GPU
- Convolution And Transformation
- Understanding CNN And Design CNNs
- CNN Milestone Projects
- Transfer Learning
- Style Transfer
- GANs
- RNNs
- UDL_math_transpose.ipynb
- UDL_math_dotproduct.ipynb
- UDL_math_matrixMult.ipynb
- UDL_math_softmax.ipynb
- UDL_math_log.ipynb
- UDL_math_entropy.ipynb
- UDL_math_argmin.ipynb
- UDL_math_meanvar.ipynb
- UDL_math_sampling.ipynb
- UDL_math_randomseed.ipynb
- UDL_math_ttest.ipynb
- UDL_math_derivatives1.ipynb
- UDL_math_derivatives2.ipynb
- UDL_GradientDescent_1D.ipynb
- UDL_GradientDescent_CodeChallengeStartValue.ipynb
- UDL_GradientDescent_2D.ipynb
- UDL_GradientDescent_experiment.ipynb
- UDL_GradientDescent_codeChallenge_lr.ipynb
- UDL_ANN_regression.ipynb
- UDL_ANN_codeChallenge_regression.ipynb
- UDL_ANN_classifyQwerties.ipynb
- UDL_ANN_learningrates.ipynb
- UDL_ANN_multilayer.ipynb
- UDL_ANN_multioutput.ipynb
- UDL_ANN_codeChallengeQwerties.ipynb
- UDL_ANN_nHiddenUnits.ipynb
- UDL_ANN_numParameters.ipynb
- UDL_ANN_breadthVsDepth.ipynb
- UDL_ANN_seqVsClass.ipynb
- UDL_ANN_codeChallengeSeq2class.ipynb
- UDL_overfitting_manual.ipynb
- UDL_overfitting_scikitlearn.ipynb
- UDL_overfitting_dataLoader.ipynb
- UDL_overfitting_trainDevsetTest.ipynb
- UDL_overfitting_regression.ipynb
- UDL_regular_dropout.ipynb
- UDL_regular_dropoutInPytorch.ipynb
- UDL_regular_dropout_example2.ipynb
- UDL_regular_L1regu.ipynb
- UDL_regular_L2regu.ipynb
- UDL_regular_minibatch.ipynb
- UDL_regular_testBatchT2.ipynb
- UDL_regular_codeChallenge_minibatch.ipynb
- UDL_metaparams_intro2winedata.ipynb
- UDL_metaparams_codeChallengeDropout.ipynb
- UDL_metaparams_batchNorm.ipynb
- UDL_metaparams_CodeChallengeBatches.ipynb
- UDL_metaparams_ActivationFuns.ipynb
- UDL_metaparams_ActivationComparisons.ipynb
- UDL_metaparams_CodeChallengeRelus.ipynb
- UDL_metaparams_CodeChallenge_sugar.ipynb
- UDL_metaparams_loss.ipynb
- UDL_metaparams_multioutput.ipynb
- UDL_metaparams_momentum.ipynb
- UDL_metaparams_optimizersComparison.ipynb
- UDL_metaparams_CodeChallengeOptimizers.ipynb
- UDL_metaparams_CodeChallengeAdamL2.ipynb
- UDL_metaparams_learningRateDecay.ipynb
- UDL_FFN_aboutMNIST.ipynb
- UDL_FFN_FFNonMNIST.ipynb
- UDL_FFN_CodeChallenge_binMNIST.ipynb
- UDL_FFN_CodeChallenge_normalization.ipynb
- UDL_FFN_weightHistograms.ipynb
- UDL_FFN_CodeChallengeBreadthDepth.ipynb
- UDL_FFN_CodeChallenge_optimizers.ipynb
- UDL_FFN_scrambledMNIST.ipynb
- UDL_FFN_shiftedMNIST.ipynb
- UDL_FFN_CodeChallenge_missing7.ipynb
- UDL_data_datasetLoader.ipynb
- UDL_data_dataVsDepth.ipynb
- UDL_data_CodeChallengeUnbalanced.ipynb
- UDL_data_oversampling.ipynb
- UDL_data_noiseAugmentation.ipynb
- UDL_data_featureAugmentation.ipynb
- UDL_data_data2colab.ipynb
- UDL_data_saveLoadModels.ipynb
- UDL_data_saveTheBest.ipynb
- UDL_measurePerformance_APRF.ipynb
- UDL_measurePerformance_APRFexample1.ipynb
- UDL_measurePerformance_example2.ipynb
- UDL_measurePerformance_codeChallenge_unequal.ipynb
- UDL_measurePerformance_time.ipynb
- UDL_weights_matrixsizes.ipynb
- UDL_weights_demoinits.ipynb
- UDL_weights_codeChallenge_weightstd.ipynb
- UDL_weights_XavierKaiming.ipynb
- UDL_weights_CodeChallenge_XavierKaiming.ipynb
- UDL_weights_codeChallenge_identicalRandom.ipynb
- UDL_weights_freezeWeights.ipynb
- UDL_weights_weightchanges.ipynb
- UDL_autoenc_denoisingMNIST.ipynb
- UDL_autoenc_codeChallenge_Nunits.ipynb
- UDL_autoenc_occlusion.ipynb
- UDL_autoenc_MNISTlatentCode.ipynb
- UDL_autoenc_tiedWeights.ipynb
- UDL_convolution_convInCode.ipynb
- UDL_convolution_conv2.ipynb
- UDL_convolution_codeChallenge.ipynb
- UDL_convolution_conv2transpose.ipynb
- UDL_convolution_meanMaxPool.ipynb
- UDL_convolution_transforms.ipynb
- UDL_convolution_customDataSet.ipynb
- UDL_CNN_CNN4MNIST.ipynb
- UDL_CNN_shiftedMNIST.ipynb
- UDL_CNN_GaussClass.ipynb
- UDL_CNN_GaussClassFeatureMaps.ipynb
- UDL_CNN_codeChallengeSoftcoding.ipynb
- UDL_CNN_CodeChallengeLinearUnits.ipynb
- UDL_CNN_GaussAE.ipynb
- UDL_CNN_CodeChallengeAEocclusion.ipynb
- UDL_CNN_codeChallengeCustomLoss.ipynb
- UDL_CNN_findGauss.ipynb
- UDL_CNN_EMNIST.ipynb
- UDL_CNN_codeChallengeBeatThis.ipynb
- UDL_CNN_codeChallengeNumChans.ipynb
- UDL_CNNmilestone_project1.ipynb
- UDL_CNNmilestone_project2.ipynb
- UDL_CNNmilestone_project3.ipynb
- UDL_CNNmilestone_project4.ipynb
- UDL_transfer_MNISTtoFMNIST.ipynb
- UDL_transfer_codeChallenge_letters2numbers.ipynb
- UDL_transfer_resnet.ipynb
- UDL_transfer_codeChallengeVGG16.ipynb
- UDL_transfer_pretrainFMNIST.ipynb
- UDL_transfer_PretrainCIFAR.ipynb