Udemy deep understanding of deep learning with Python intro 2023 | CA1ECAFFAFE6A30AFA413D2399728A0D1CD7A426

Udemy deep understanding of deep learning with Python intro 2023

Name	Udemy - A deep understanding of deep learning (with Python intro) 7-2023	DOWNLOAD Copy Link
Total Size	17.2 GB
Total Files	525
Last Seen	2024-07-08 23:43
Hash	CA1ECAFFAFE6A30AFA413D2399728A0D1CD7A426

/19 - Understand and design CNNs/
005 Examine feature map activations.mp4	263.6 MB
001 The canonical CNN architecture.mp4	25.0 MB
001 The canonical CNN architecture_en.srt	15.5 KB
002 CNN to classify MNIST digits.mp4	151.9 MB
002 CNN to classify MNIST digits_en.srt	37.5 KB
003 CNN on shifted MNIST.mp4	43.4 MB
003 CNN on shifted MNIST_en.srt	11.9 KB
004 Classify Gaussian blurs.mp4	184.6 MB
004 Classify Gaussian blurs_en.srt	33.8 KB
005 Examine feature map activations_en.srt	39.9 KB
006 CodeChallenge Softcode internal parameters.mp4	119.2 MB
006 CodeChallenge Softcode internal parameters_en.srt	24.8 KB
007 CodeChallenge How wide the FC.mp4	95.0 MB
007 CodeChallenge How wide the FC_en.srt	16.7 KB
008 Do autoencoders clean Gaussians.mp4	135.1 MB
008 Do autoencoders clean Gaussians_en.srt	24.1 KB
009 CodeChallenge AEs and occluded Gaussians.mp4	82.4 MB
009 CodeChallenge AEs and occluded Gaussians_en.srt	13.8 KB
010 CodeChallenge Custom loss functions.mp4	103.5 MB
010 CodeChallenge Custom loss functions_en.srt	29.5 KB
011 Discover the Gaussian parameters.mp4	143.3 MB
011 Discover the Gaussian parameters_en.srt	22.9 KB
012 The EMNIST dataset (letter recognition).mp4	150.9 MB
012 The EMNIST dataset (letter recognition)_en.srt	35.6 KB
013 Dropout in CNNs.mp4	74.1 MB
013 Dropout in CNNs_en.srt	14.0 KB
014 CodeChallenge How low can you go.mp4	41.1 MB
014 CodeChallenge How low can you go_en.srt	9.8 KB
015 CodeChallenge Varying number of channels.mp4	70.6 MB
015 CodeChallenge Varying number of channels_en.srt	19.4 KB
016 So many possibilities! How to create a CNN.mp4	9.7 MB
016 So many possibilities! How to create a CNN_en.srt	6.4 KB
/01 - Introduction/
001 How to learn from this course.mp4	57.6 MB
001 How to learn from this course_en.srt	12.8 KB
002 Using Udemy like a pro.mp4	26.9 MB
002 Using Udemy like a pro_en.srt	12.1 KB
/02 - Download all course materials/
001 Code-on-my-github-site.url	0.1 KB
001 Downloading and using the code.mp4	35.3 MB
001 Downloading and using the code_en.srt	9.3 KB
001 DUDL-PythonCode.zip	1.4 MB
002 My policy on code-sharing.mp4	4.1 MB
002 My policy on code-sharing_en.srt	2.5 KB
external-links.txt	0.1 KB
/03 - Concepts in deep learning/
001 What is an artificial neural network.mp4	30.8 MB
001 What is an artificial neural network_en.srt	21.1 KB
002 How models learn.mp4	37.1 MB
002 How models learn_en.srt	18.5 KB
003 The role of DL in science and knowledge.mp4	92.0 MB
003 The role of DL in science and knowledge_en.srt	23.0 KB
004 Running experiments to understand DL.mp4	78.5 MB
004 Running experiments to understand DL_en.srt	18.9 KB
005 Are artificial neurons like biological neurons.mp4	59.0 MB
005 Are artificial neurons like biological neurons_en.srt	23.8 KB
/04 - About the Python tutorial/
001 Should you watch the Python tutorial.mp4	9.8 MB
001 Should you watch the Python tutorial_en.srt	6.1 KB
/05 - Math, numpy, PyTorch/
001 PyTorch or TensorFlow.html	1.1 KB
002 Introduction to this section.mp4	4.7 MB
002 Introduction to this section_en.srt	2.9 KB
003 Spectral theories in mathematics.mp4	46.0 MB
003 Spectral theories in mathematics_en.srt	13.4 KB
004 Terms and datatypes in math and computers.mp4	16.6 MB
004 Terms and datatypes in math and computers_en.srt	10.5 KB
005 Converting reality to numbers.mp4	14.1 MB
005 Converting reality to numbers_en.srt	9.4 KB
006 Vector and matrix transpose.mp4	18.7 MB
006 Vector and matrix transpose_en.srt	9.9 KB
007 OMG it's the dot product!.mp4	20.8 MB
007 OMG it's the dot product!_en.srt	13.8 KB
008 Matrix multiplication.mp4	47.7 MB
008 Matrix multiplication_en.srt	20.3 KB
009 Softmax.mp4	73.6 MB
009 Softmax_en.srt	27.4 KB
010 Logarithms.mp4	21.9 MB
010 Logarithms_en.srt	11.3 KB
011 Entropy and cross-entropy.mp4	61.6 MB
011 Entropy and cross-entropy_en.srt	25.0 KB
012 Minmax and argminargmax.mp4	47.9 MB
012 Minmax and argminargmax_en.srt	17.9 KB
013 Mean and variance.mp4	34.5 MB
013 Mean and variance_en.srt	22.3 KB
014 Random sampling and sampling variability.mp4	43.3 MB
014 Random sampling and sampling variability_en.srt	16.1 KB
015 Reproducible randomness via seeding.mp4	51.5 MB
015 Reproducible randomness via seeding_en.srt	11.6 KB
016 The t-test.mp4	62.6 MB
016 The t-test_en.srt	19.1 KB
017 Derivatives intuition and polynomials.mp4	33.7 MB
017 Derivatives intuition and polynomials_en.srt	24.0 KB
018 Derivatives find minima.mp4	19.6 MB
018 Derivatives find minima_en.srt	12.1 KB
019 Derivatives product and chain rules.mp4	27.1 MB
019 Derivatives product and chain rules_en.srt	13.9 KB
/06 - Gradient descent/
001 Overview of gradient descent.mp4	42.0 MB
001 Overview of gradient descent_en.srt	20.6 KB
002 What about local minima.mp4	26.9 MB
002 What about local minima_en.srt	16.9 KB
003 Gradient descent in 1D.mp4	92.1 MB
003 Gradient descent in 1D_en.srt	24.3 KB
004 CodeChallenge unfortunate starting value.mp4	59.8 MB
004 CodeChallenge unfortunate starting value_en.srt	15.7 KB
005 Gradient descent in 2D.mp4	101.1 MB
005 Gradient descent in 2D_en.srt	21.2 KB
006 CodeChallenge 2D gradient ascent.mp4	29.2 MB
006 CodeChallenge 2D gradient ascent_en.srt	7.4 KB
007 Parametric experiments on g.d.mp4	103.5 MB
007 Parametric experiments on g.d_en.srt	26.8 KB
008 CodeChallenge fixed vs. dynamic learning rate.mp4	88.1 MB
008 CodeChallenge fixed vs. dynamic learning rate_en.srt	23.1 KB
009 Vanishing and exploding gradients.mp4	23.4 MB
009 Vanishing and exploding gradients_en.srt	9.1 KB
010 Tangent Notebook revision history.mp4	15.5 MB
010 Tangent Notebook revision history_en.srt	2.7 KB
/07 - ANNs (Artificial Neural Networks)/
001 The perceptron and ANN architecture.mp4	38.9 MB
001 The perceptron and ANN architecture_en.srt	27.6 KB
002 A geometric view of ANNs.mp4	31.3 MB
002 A geometric view of ANNs_en.srt	19.2 KB
003 ANN math part 1 (forward prop).mp4	34.4 MB
003 ANN math part 1 (forward prop)_en.srt	21.9 KB
004 ANN math part 2 (errors, loss, cost).mp4	39.1 MB
004 ANN math part 2 (errors, loss, cost)_en.srt	13.7 KB
005 ANN math part 3 (backprop).mp4	29.3 MB
005 ANN math part 3 (backprop)_en.srt	15.1 KB
006 ANN for regression.mp4	77.8 MB
006 ANN for regression_en.srt	35.3 KB
007 CodeChallenge manipulate regression slopes.mp4	106.0 MB
007 CodeChallenge manipulate regression slopes_en.srt	27.9 KB
008 ANN for classifying qwerties.mp4	136.7 MB
008 ANN for classifying qwerties_en.srt	34.1 KB
009 Learning rates comparison.mp4	176.8 MB
009 Learning rates comparison_en.srt	35.7 KB
010 Multilayer ANN.mp4	110.4 MB
010 Multilayer ANN_en.srt	29.0 KB
011 Linear solutions to linear problems.mp4	38.5 MB
011 Linear solutions to linear problems_en.srt	12.0 KB
012 Why multilayer linear models don't exist.mp4	20.2 MB
012 Why multilayer linear models don't exist_en.srt	9.1 KB
013 Multi-output ANN (iris dataset).mp4	148.9 MB
013 Multi-output ANN (iris dataset)_en.srt	39.6 KB
014 CodeChallenge more qwerties!.mp4	85.8 MB
014 CodeChallenge more qwerties!_en.srt	17.5 KB
015 Comparing the number of hidden units.mp4	33.4 MB
015 Comparing the number of hidden units_en.srt	14.4 KB
016 Depth vs. breadth number of parameters.mp4	102.4 MB
016 Depth vs. breadth number of parameters_en.srt	25.3 KB
017 Defining models using sequential vs. class.mp4	69.0 MB
018 Model depth vs. breadth.mp4	120.5 MB
018 Model depth vs. breadth_en.srt	30.4 KB
019 CodeChallenge convert sequential to class.mp4	38.3 MB
019 CodeChallenge convert sequential to class_en.srt	9.6 KB
020 Diversity of ANN visual representations.html	0.5 KB
021 Reflection Are DL models understandable yet.mp4	54.2 MB
021 Reflection Are DL models understandable yet_en.srt	12.4 KB
/08 - Overfitting and cross-validation/
001 What is overfitting and is it as bad as they say.mp4	56.9 MB
001 What is overfitting and is it as bad as they say_en.srt	17.8 KB
002 Cross-validation.mp4	51.4 MB
002 Cross-validation_en.srt	24.6 KB
003 Generalization.mp4	13.9 MB
003 Generalization_en.srt	8.7 KB
004 Cross-validation -- manual separation.mp4	73.8 MB
004 Cross-validation -- manual separation_en.srt	18.3 KB
005 Cross-validation -- scikitlearn.mp4	111.0 MB
005 Cross-validation -- scikitlearn_en.srt	30.0 KB
006 Cross-validation -- DataLoader.mp4	127.1 MB
006 Cross-validation -- DataLoader_en.srt	28.5 KB
007 Splitting data into train, devset, test.mp4	59.0 MB
007 Splitting data into train, devset, test_en.srt	13.6 KB
008 Cross-validation on regression.mp4	27.6 MB
008 Cross-validation on regression_en.srt	11.8 KB
/09 - Regularization/
001 Regularization Concept and methods.mp4	64.5 MB
001 Regularization Concept and methods_en.srt	18.8 KB
002 train() and eval() modes.mp4	16.4 MB
002 train() and eval() modes_en.srt	10.1 KB
003 Dropout regularization.mp4	108.7 MB
003 Dropout regularization_en.srt	31.1 KB
004 Dropout regularization in practice.mp4	137.1 MB
004 Dropout regularization in practice_en.srt	32.9 KB
005 Dropout example 2.mp4	40.0 MB
005 Dropout example 2_en.srt	9.0 KB
006 Weight regularization (L1L2) math.mp4	51.7 MB
006 Weight regularization (L1L2) math_en.srt	26.7 KB
007 L2 regularization in practice.mp4	82.3 MB
007 L2 regularization in practice_en.srt	18.7 KB
008 L1 regularization in practice.mp4	74.4 MB
008 L1 regularization in practice_en.srt	17.2 KB
009 Training in mini-batches.mp4	25.3 MB
009 Training in mini-batches_en.srt	16.6 KB
010 Batch training in action.mp4	80.1 MB
010 Batch training in action_en.srt	15.4 KB
011 The importance of equal batch sizes.mp4	53.8 MB
011 The importance of equal batch sizes_en.srt	9.3 KB
012 CodeChallenge Effects of mini-batch size.mp4	87.3 MB
012 CodeChallenge Effects of mini-batch size_en.srt	17.8 KB
/10 - Metaparameters (activations, optimizers)/
001 What are metaparameters.mp4	13.0 MB
001 What are metaparameters_en.srt	7.3 KB
002 The wine quality dataset.mp4	130.7 MB
002 The wine quality dataset_en.srt	25.4 KB
003 CodeChallenge Minibatch size in the wine dataset.mp4	108.6 MB
003 CodeChallenge Minibatch size in the wine dataset_en.srt	22.8 KB
004 Data normalization.mp4	47.6 MB
004 Data normalization_en.srt	19.4 KB
005 The importance of data normalization.mp4	50.1 MB
005 The importance of data normalization_en.srt	13.6 KB
006 Batch normalization.mp4	41.0 MB
006 Batch normalization_en.srt	18.4 KB
007 Batch normalization in practice.mp4	47.4 MB
007 Batch normalization in practice_en.srt	11.4 KB
008 CodeChallenge Batch-normalize the qwerties.mp4	41.8 MB
008 CodeChallenge Batch-normalize the qwerties_en.srt	7.4 KB
009 Activation functions.mp4	89.0 MB
009 Activation functions_en.srt	26.1 KB
010 Activation functions in PyTorch.mp4	70.3 MB
010 Activation functions in PyTorch_en.srt	16.7 KB
011 Activation functions comparison.mp4	74.0 MB
011 Activation functions comparison_en.srt	13.4 KB
012 CodeChallenge Compare relu variants.mp4	67.1 MB
012 CodeChallenge Compare relu variants_en.srt	11.1 KB
013 CodeChallenge Predict sugar.mp4	93.7 MB
013 CodeChallenge Predict sugar_en.srt	24.6 KB
014 Loss functions.mp4	71.9 MB
014 Loss functions_en.srt	24.0 KB
015 Loss functions in PyTorch.mp4	106.7 MB
015 Loss functions in PyTorch_en.srt	26.5 KB
016 More practice with multioutput ANNs.mp4	75.4 MB
016 More practice with multioutput ANNs_en.srt	20.0 KB
017 Optimizers (minibatch, momentum).mp4	44.3 MB
017 Optimizers (minibatch, momentum)_en.srt	27.0 KB
018 SGD with momentum.mp4	65.1 MB
018 SGD with momentum_en.srt	11.4 KB
019 Optimizers (RMSprop, Adam).mp4	39.9 MB
019 Optimizers (RMSprop, Adam)_en.srt	21.8 KB
020 Optimizers comparison.mp4	64.8 MB
020 Optimizers comparison_en.srt	14.6 KB
021 CodeChallenge Optimizers and... something.mp4	38.3 MB
021 CodeChallenge Optimizers and... something_en.srt	9.4 KB
022 CodeChallenge Adam with L2 regularization.mp4	41.9 MB
022 CodeChallenge Adam with L2 regularization_en.srt	10.2 KB
023 Learning rate decay.mp4	72.4 MB
023 Learning rate decay_en.srt	17.6 KB
024 How to pick the right metaparameters.mp4	26.8 MB
024 How to pick the right metaparameters_en.srt	16.6 KB
/11 - FFNs (Feed-Forward Networks)/
001 What are fully-connected and feedforward networks.mp4	13.3 MB
001 What are fully-connected and feedforward networks_en.srt	6.9 KB
002 The MNIST dataset.mp4	93.0 MB
002 The MNIST dataset_en.srt	18.1 KB
003 FFN to classify digits.mp4	123.0 MB
003 FFN to classify digits_en.srt	32.4 KB
004 CodeChallenge Binarized MNIST images.mp4	30.1 MB
004 CodeChallenge Binarized MNIST images_en.srt	7.3 KB
005 CodeChallenge Data normalization.mp4	74.4 MB
005 CodeChallenge Data normalization_en.srt	24.1 KB
006 Distributions of weights pre- and post-learning.mp4	94.0 MB
006 Distributions of weights pre- and post-learning_en.srt	21.4 KB
007 CodeChallenge MNIST and breadth vs. depth.mp4	94.8 MB
007 CodeChallenge MNIST and breadth vs. depth_en.srt	17.5 KB
008 CodeChallenge Optimizers and MNIST.mp4	34.8 MB
008 CodeChallenge Optimizers and MNIST_en.srt	9.8 KB
009 Scrambled MNIST.mp4	63.1 MB
009 Scrambled MNIST_en.srt	11.0 KB
010 Shifted MNIST.mp4	60.1 MB
010 Shifted MNIST_en.srt	16.5 KB
011 CodeChallenge The mystery of the missing 7.mp4	56.0 MB
011 CodeChallenge The mystery of the missing 7_en.srt	15.5 KB
012 Universal approximation theorem.mp4	25.4 MB
012 Universal approximation theorem_en.srt	11.5 KB
/12 - More on data/
001 Anatomy of a torch dataset and dataloader.mp4	105.7 MB
001 Anatomy of a torch dataset and dataloader_en.srt	26.0 KB
002 Data size and network size.mp4	102.0 MB
002 Data size and network size_en.srt	23.1 KB
003 CodeChallenge unbalanced data.mp4	123.6 MB
003 CodeChallenge unbalanced data_en.srt	28.9 KB
004 What to do about unbalanced designs.mp4	19.7 MB
004 What to do about unbalanced designs_en.srt	11.0 KB
005 Data oversampling in MNIST.mp4	93.6 MB
005 Data oversampling in MNIST_en.srt	23.8 KB
006 Data noise augmentation (with devset+test).mp4	79.8 MB
006 Data noise augmentation (with devset+test)_en.srt	18.4 KB
007 Data feature augmentation.mp4	119.9 MB
007 Data feature augmentation_en.srt	28.2 KB
008 Getting data into colab.mp4	33.5 MB
008 Getting data into colab_en.srt	8.8 KB
009 Save and load trained models.mp4	40.6 MB
009 Save and load trained models_en.srt	8.8 KB
010 Save the best-performing model.mp4	94.5 MB
010 Save the best-performing model_en.srt	21.7 KB
011 Where to find online datasets.mp4	29.8 MB
011 Where to find online datasets_en.srt	8.3 KB
/13 - Measuring model performance/
001 Two perspectives of the world.mp4	19.8 MB
001 Two perspectives of the world_en.srt	10.2 KB
002 Accuracy, precision, recall, F1.mp4	66.8 MB
002 Accuracy, precision, recall, F1_en.srt	17.7 KB
003 APRF in code.mp4	40.0 MB
003 APRF in code_en.srt	9.2 KB
004 APRF example 1 wine quality.mp4	108.0 MB
004 APRF example 1 wine quality_en.srt	19.0 KB
005 APRF example 2 MNIST.mp4	99.1 MB
005 APRF example 2 MNIST_en.srt	16.9 KB
006 CodeChallenge MNIST with unequal groups.mp4	61.9 MB
006 CodeChallenge MNIST with unequal groups_en.srt	12.8 KB
007 Computation time.mp4	73.9 MB
007 Computation time_en.srt	14.1 KB
008 Better performance in test than train.mp4	19.1 MB
008 Better performance in test than train_en.srt	11.8 KB
/14 - FFN milestone projects/
001 Project 1 A gratuitously complex adding machine.mp4	27.2 MB
001 Project 1 A gratuitously complex adding machine_en.srt	10.6 KB
002 Project 1 My solution.mp4	73.2 MB
002 Project 1 My solution_en.srt	16.7 KB
003 Project 2 Predicting heart disease.mp4	24.8 MB
003 Project 2 Predicting heart disease_en.srt	10.8 KB
004 Project 2 My solution.mp4	163.3 MB
004 Project 2 My solution_en.srt	27.3 KB
005 Project 3 FFN for missing data interpolation.mp4	20.6 MB
005 Project 3 FFN for missing data interpolation_en.srt	14.2 KB
006 Project 3 My solution.mp4	55.5 MB
006 Project 3 My solution_en.srt	11.7 KB
/15 - Weight inits and investigations/
001 Explanation of weight matrix sizes.mp4	62.5 MB
001 Explanation of weight matrix sizes_en.srt	17.0 KB
002 A surprising demo of weight initializations.mp4	90.1 MB
002 A surprising demo of weight initializations_en.srt	23.6 KB
003 Theory Why and how to initialize weights.mp4	77.2 MB
003 Theory Why and how to initialize weights_en.srt	18.0 KB
004 CodeChallenge Weight variance inits.mp4	76.4 MB
004 CodeChallenge Weight variance inits_en.srt	18.2 KB
005 Xavier and Kaiming initializations.mp4	101.0 MB
005 Xavier and Kaiming initializations_en.srt	22.2 KB
006 CodeChallenge Xavier vs. Kaiming.mp4	114.8 MB
006 CodeChallenge Xavier vs. Kaiming_en.srt	24.3 KB
007 CodeChallenge Identically random weights.mp4	68.4 MB
007 CodeChallenge Identically random weights_en.srt	17.7 KB
008 Freezing weights during learning.mp4	92.5 MB
008 Freezing weights during learning_en.srt	19.0 KB
009 Learning-related changes in weights.mp4	113.2 MB
009 Learning-related changes in weights_en.srt	32.3 KB
010 Use default inits or apply your own.mp4	11.5 MB
010 Use default inits or apply your own_en.srt	6.3 KB
/16 - Autoencoders/
001 What are autoencoders and what do they do.mp4	22.2 MB
001 What are autoencoders and what do they do_en.srt	16.7 KB
002 Denoising MNIST.mp4	90.7 MB
002 Denoising MNIST_en.srt	22.5 KB
003 CodeChallenge How many units.mp4	104.9 MB
003 CodeChallenge How many units_en.srt	28.5 KB
004 AEs for occlusion.mp4	144.9 MB
004 AEs for occlusion_en.srt	25.5 KB
005 The latent code of MNIST.mp4	123.5 MB
005 The latent code of MNIST_en.srt	30.9 KB
006 Autoencoder with tied weights.mp4	137.9 MB
006 Autoencoder with tied weights_en.srt	34.3 KB
/17 - Running models on a GPU/
001 What is a GPU and why use it.mp4	52.8 MB
001 What is a GPU and why use it_en.srt	22.1 KB
002 Implementation.mp4	41.6 MB
002 Implementation_en.srt	14.6 KB
003 CodeChallenge Run an experiment on the GPU.mp4	38.7 MB
003 CodeChallenge Run an experiment on the GPU_en.srt	9.7 KB
/18 - Convolution and transformations/
001 Convolution concepts.mp4	92.7 MB
001 Convolution concepts_en.srt	31.9 KB
002 Feature maps and convolution kernels.mp4	56.2 MB
002 Feature maps and convolution kernels_en.srt	13.8 KB
003 Convolution in code.mp4	173.8 MB
003 Convolution in code_en.srt	30.1 KB
004 Convolution parameters (stride, padding).mp4	28.7 MB
004 Convolution parameters (stride, padding)_en.srt	17.8 KB
005 The Conv2 class in PyTorch.mp4	79.2 MB
005 The Conv2 class in PyTorch_en.srt	18.7 KB
006 CodeChallenge Choose the parameters.mp4	19.9 MB
006 CodeChallenge Choose the parameters_en.srt	10.0 KB
007 Transpose convolution.mp4	72.8 MB
007 Transpose convolution_en.srt	19.6 KB
008 Maxmean pooling.mp4	53.7 MB
008 Maxmean pooling_en.srt	26.3 KB
009 Pooling in PyTorch.mp4	46.4 MB
009 Pooling in PyTorch_en.srt	19.8 KB
010 To pool or to stride.mp4	51.6 MB
010 To pool or to stride_en.srt	14.4 KB
011 Image transforms.mp4	130.7 MB
011 Image transforms_en.srt	23.4 KB
012 Creating and using custom DataLoaders.mp4	107.4 MB
012 Creating and using custom DataLoaders_en.srt	26.0 KB
/
Readme.txt	0.1 KB
/20 - CNN milestone projects/
001 Project 1 Import and classify CIFAR10.mp4	38.4 MB
001 Project 1 Import and classify CIFAR10_en.srt	10.5 KB
002 Project 1 My solution.mp4	85.2 MB
002 Project 1 My solution_en.srt	17.0 KB
003 Project 2 CIFAR-autoencoder.mp4	30.7 MB
003 Project 2 CIFAR-autoencoder_en.srt	6.9 KB
004 Project 3 FMNIST.mp4	20.4 MB
004 Project 3 FMNIST_en.srt	5.1 KB
005 Project 4 Psychometric functions in CNNs.mp4	80.2 MB
005 Project 4 Psychometric functions in CNNs_en.srt	16.7 KB
/21 - Transfer learning/
001 Transfer learning What, why, and when.mp4	42.4 MB
001 Transfer learning What, why, and when_en.srt	24.4 KB
002 Transfer learning MNIST - FMNIST.mp4	82.0 MB
002 Transfer learning MNIST - FMNIST_en.srt	14.4 KB
003 CodeChallenge letters to numbers.mp4	89.0 MB
003 CodeChallenge letters to numbers_en.srt	21.3 KB
004 Famous CNN architectures.mp4	23.3 MB
004 Famous CNN architectures_en.srt	8.6 KB
005 Transfer learning with ResNet-18.mp4	134.5 MB
005 Transfer learning with ResNet-18_en.srt	24.2 KB
006 CodeChallenge VGG-16.mp4	21.3 MB
006 CodeChallenge VGG-16_en.srt	5.0 KB
007 Pretraining with autoencoders.mp4	142.6 MB
007 Pretraining with autoencoders_en.srt	28.4 KB
008 CIFAR10 with autoencoder-pretrained model.mp4	114.2 MB
008 CIFAR10 with autoencoder-pretrained model_en.srt	25.5 KB
/22 - Style transfer/
001 What is style transfer and how does it work.mp4	17.6 MB
001 What is style transfer and how does it work_en.srt	6.3 KB
002 The Gram matrix (feature activation covariance).mp4	69.7 MB
002 The Gram matrix (feature activation covariance)_en.srt	16.6 KB
003 The style transfer algorithm.mp4	28.0 MB
004 Transferring the screaming bathtub.mp4	220.6 MB
004 Transferring the screaming bathtub_en.srt	31.8 KB
005 CodeChallenge Style transfer with AlexNet.mp4	53.4 MB
005 CodeChallenge Style transfer with AlexNet_en.srt	10.3 KB
/23 - Generative adversarial networks/
001 GAN What, why, and how.mp4	40.6 MB
001 GAN What, why, and how_en.srt	23.2 KB
002 Linear GAN with MNIST.mp4	127.4 MB
003 CodeChallenge Linear GAN with FMNIST.mp4	61.4 MB
003 CodeChallenge Linear GAN with FMNIST_en.srt	13.7 KB
004 CNN GAN with Gaussians.mp4	137.8 MB
004 CNN GAN with Gaussians_en.srt	21.8 KB
005 CodeChallenge Gaussians with fewer layers.mp4	53.8 MB
005 CodeChallenge Gaussians with fewer layers_en.srt	8.8 KB
006 CNN GAN with FMNIST.mp4	49.2 MB
006 CNN GAN with FMNIST_en.srt	9.1 KB
007 CodeChallenge CNN GAN with CIFAR.mp4	45.3 MB
007 CodeChallenge CNN GAN with CIFAR_en.srt	11.5 KB
/24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/
001 Leveraging sequences in deep learning.mp4	67.0 MB
001 Leveraging sequences in deep learning_en.srt	18.6 KB
002 How RNNs work.mp4	34.2 MB
002 How RNNs work_en.srt	21.5 KB
003 The RNN class in PyTorch.mp4	94.0 MB
003 The RNN class in PyTorch_en.srt	26.6 KB
004 Predicting alternating sequences.mp4	161.2 MB
004 Predicting alternating sequences_en.srt	28.4 KB
005 CodeChallenge sine wave extrapolation.mp4	174.7 MB
005 CodeChallenge sine wave extrapolation_en.srt	38.9 KB
006 More on RNNs Hidden states, embeddings.mp4	98.8 MB
006 More on RNNs Hidden states, embeddings_en.srt	22.6 KB
007 GRU and LSTM.mp4	105.2 MB
007 GRU and LSTM_en.srt	32.9 KB
008 The LSTM and GRU classes.mp4	88.4 MB
008 The LSTM and GRU classes_en.srt	19.7 KB
009 Lorem ipsum.mp4	148.5 MB
009 Lorem ipsum_en.srt	36.9 KB
/25 - Ethics of deep learning/
001 Will AI save us or destroy us.mp4	25.0 MB
001 Will AI save us or destroy us_en.srt	14.2 KB
002 Example case studies.mp4	40.3 MB
002 Example case studies_en.srt	9.0 KB
003 Some other possible ethical scenarios.mp4	61.1 MB
003 Some other possible ethical scenarios_en.srt	15.0 KB
004 Will deep learning take our jobs.mp4	35.5 MB
005 Accountability and making ethical AI.mp4	64.2 MB
005 Accountability and making ethical AI_en.srt	16.5 KB
/26 - Where to go from here/
001 How to learn topic _X_ in deep learning.mp4	18.3 MB
001 How to learn topic _X_ in deep learning_en.srt	12.2 KB
002 How to read academic DL papers.mp4	144.0 MB
002 How to read academic DL papers_en.srt	25.1 KB
/27 - Python intro Data types/
001 How to learn from the Python tutorial.mp4	12.9 MB
002 Variables.mp4	43.1 MB
002 Variables_en.srt	26.8 KB
003 Math and printing.mp4	37.7 MB
003 Math and printing_en.srt	26.4 KB
004 Lists (1 of 2).mp4	26.1 MB
004 Lists (1 of 2)_en.srt	20.1 KB
005 Lists (2 of 2).mp4	24.7 MB
005 Lists (2 of 2)_en.srt	14.3 KB
006 Tuples.mp4	16.1 MB
006 Tuples_en.srt	11.8 KB
007 Booleans.mp4	48.3 MB
007 Booleans_en.srt	28.4 KB
008 Dictionaries.mp4	24.4 MB
008 Dictionaries_en.srt	16.8 KB
/28 - Python intro Indexing, slicing/
001 Indexing.mp4	24.5 MB
001 Indexing_en.srt	17.8 KB
002 Slicing.mp4	30.4 MB
/29 - Python intro Functions/
001 Inputs and outputs.mp4	14.1 MB
001 Inputs and outputs_en.srt	10.4 KB
002 Python libraries (numpy).mp4	29.3 MB
002 Python libraries (numpy)_en.srt	19.7 KB
003 Python libraries (pandas).mp4	63.8 MB
003 Python libraries (pandas)_en.srt	20.0 KB
004 Getting help on functions.mp4	26.0 MB
004 Getting help on functions_en.srt	10.9 KB
005 Creating functions.mp4	42.1 MB
005 Creating functions_en.srt	30.4 KB
006 Global and local variable scopes.mp4	41.1 MB
006 Global and local variable scopes_en.srt	19.4 KB
007 Copies and referents of variables.mp4	11.2 MB
007 Copies and referents of variables_en.srt	7.1 KB
008 Classes and object-oriented programming.mp4	63.6 MB
008 Classes and object-oriented programming_en.srt	26.2 KB
/30 - Python intro Flow control/
001 If-else statements.mp4	31.6 MB
001 If-else statements_en.srt	21.3 KB
002 If-else statements, part 2.mp4	56.3 MB
002 If-else statements, part 2_en.srt	23.7 KB
003 For loops.mp4	46.9 MB
003 For loops_en.srt	24.9 KB
004 Enumerate and zip.mp4	61.4 MB
004 Enumerate and zip_en.srt	15.8 KB
005 Continue.mp4	15.0 MB
005 Continue_en.srt	9.9 KB
006 Initializing variables.mp4	48.7 MB
006 Initializing variables_en.srt	25.2 KB
007 Single-line loops (list comprehension).mp4	46.2 MB
007 Single-line loops (list comprehension)_en.srt	21.4 KB
008 while loops.mp4	50.5 MB
008 while loops_en.srt	27.5 KB
009 Broadcasting in numpy.mp4	38.9 MB
009 Broadcasting in numpy_en.srt	21.0 KB
010 Function error checking and handling.mp4	80.7 MB
010 Function error checking and handling_en.srt	25.0 KB
/31 - Python intro Text and plots/
001 Printing and string interpolation.mp4	49.5 MB
001 Printing and string interpolation_en.srt	24.0 KB
002 Plotting dots and lines.mp4	30.3 MB
002 Plotting dots and lines_en.srt	17.7 KB
003 Subplot geometry.mp4	51.1 MB
003 Subplot geometry_en.srt	22.8 KB
004 Making the graphs look nicer.mp4	61.9 MB
004 Making the graphs look nicer_en.srt	26.6 KB
005 Seaborn.mp4	36.0 MB
005 Seaborn_en.srt	15.7 KB
006 Images.mp4	74.5 MB
006 Images_en.srt	25.4 KB
007 Export plots in low and high resolution.mp4	39.2 MB
007 Export plots in low and high resolution_en.srt	11.2 KB
/32 - Bonus section/
001 Bonus content.html	4.7 KB
Total files 525