[GigaCourse.Com] Udemy - A deep understanding of deep learning (with Python intro)

Creation Time	June 19, 2025, 2:50 a.m.
Last Access Time	June 29, 2025, 11:27 p.m.
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19 Understand and design CNNs/005 Examine feature map activations.mp4 260.6 MB
22 Style transfer/004 Transferring the screaming bathtub.mp4 216.8 MB
19 Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4 201.3 MB
19 Understand and design CNNs/002 CNN to classify MNIST digits.mp4 200.3 MB
07 ANNs/013 Multi-output ANN (iris dataset).mp4 186.8 MB
19 Understand and design CNNs/004 Classify Gaussian blurs.mp4 185.1 MB
09 Regularization/004 Dropout regularization in practice.mp4 183.2 MB
16 Autoencoders/006 Autoencoder with tied weights.mp4 177.7 MB
18 Convolution and transformations/003 Convolution in code.mp4 173.1 MB
08 Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4 172.3 MB
23 Generative adversarial networks/002 Linear GAN with MNIST.mp4 169.9 MB
07 ANNs/009 Learning rates comparison.mp4 168.6 MB
12 More on data/003 CodeChallenge_ unbalanced data.mp4 166.3 MB
11 FFNs/003 FFN to classify digits.mp4 161.9 MB
16 Autoencoders/005 The latent code of MNIST.mp4 161.8 MB
07 ANNs/018 Model depth vs. breadth.mp4 158.9 MB
12 More on data/007 Data feature augmentation.mp4 158.3 MB
21 Transfer learning/007 Pretraining with autoencoders.mp4 156.6 MB
14 FFN milestone projects/004 Project 2_ My solution.mp4 155.7 MB
21 Transfer learning/008 CIFAR10 with autoencoder-pretrained model.mp4 153.3 MB
07 ANNs/008 ANN for classifying qwerties.mp4 151.1 MB
21 Transfer learning/005 Transfer learning with ResNet-18.mp4 148.5 MB
19 Understand and design CNNs/008 Do autoencoders clean Gaussians_.mp4 147.9 MB
15 Weight inits and investigations/009 Learning-related changes in weights.mp4 146.8 MB
07 ANNs/010 Multilayer ANN.mp4 144.7 MB
10 Metaparameters (activations, optimizers)/002 The _wine quality_ dataset.mp4 143.5 MB
08 Overfitting and cross-validation/005 Cross-validation -- scikitlearn.mp4 142.9 MB
25 Where to go from here_/002 How to read academic DL papers.mp4 141.9 MB
18 Convolution and transformations/012 Creating and using custom DataLoaders.mp4 139.5 MB
07 ANNs/007 CodeChallenge_ manipulate regression slopes.mp4 139.1 MB
16 Autoencoders/004 AEs for occlusion.mp4 138.2 MB
10 Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.mp4 138.1 MB
19 Understand and design CNNs/011 Discover the Gaussian parameters.mp4 136.7 MB
09 Regularization/003 Dropout regularization.mp4 136.0 MB
12 More on data/001 Anatomy of a torch dataset and dataloader.mp4 135.8 MB
23 Generative adversarial networks/004 CNN GAN with Gaussians.mp4 135.7 MB
12 More on data/002 Data size and network size.mp4 135.7 MB
06 Gradient descent/007 Parametric experiments on g.d.mp4 135.6 MB
07 ANNs/006 ANN for regression.mp4 135.5 MB
16 Autoencoders/003 CodeChallenge_ How many units_.mp4 135.4 MB
15 Weight inits and investigations/005 Xavier and Kaiming initializations.mp4 134.1 MB
19 Understand and design CNNs/010 CodeChallenge_ Custom loss functions.mp4 132.9 MB
07 ANNs/016 Depth vs. breadth_ number of parameters.mp4 132.1 MB
18 Convolution and transformations/011 Image transforms.mp4 129.9 MB
15 Weight inits and investigations/006 CodeChallenge_ Xavier vs. Kaiming.mp4 126.5 MB
12 More on data/010 Save the best-performing model.mp4 126.5 MB
12 More on data/005 Data oversampling in MNIST.mp4 122.6 MB
10 Metaparameters (activations, optimizers)/013 CodeChallenge_ Predict sugar.mp4 122.1 MB
15 Weight inits and investigations/002 A surprising demo of weight initializations.mp4 121.6 MB
03 Concepts in deep learning/003 The role of DL in science and knowledge.mp4 121.6 MB
19 Understand and design CNNs/006 CodeChallenge_ Softcode internal parameters.mp4 120.1 MB
06 Gradient descent/003 Gradient descent in 1D.mp4 119.3 MB
10 Metaparameters (activations, optimizers)/003 CodeChallenge_ Minibatch size in the wine dataset.mp4 118.8 MB
21 Transfer learning/003 CodeChallenge_ letters to numbers.mp4 118.7 MB
20 CNN milestone projects/002 Project 1_ My solution.mp4 118.6 MB
16 Autoencoders/002 Denoising MNIST.mp4 118.5 MB
11 FFNs/006 Distributions of weights pre- and post-learning.mp4 116.3 MB
03 Concepts in deep learning/005 Are artificial _neurons_ like biological neurons_.mp4 114.7 MB
06 Gradient descent/008 CodeChallenge_ fixed vs. dynamic learning rate.mp4 114.6 MB
09 Regularization/007 L2 regularization in practice.mp4 110.5 MB
29 Python intro_ Functions/008 Classes and object-oriented programming.mp4 108.2 MB
31 Python intro_ Text and plots/004 Making the graphs look nicer.mp4 107.7 MB
13 Measuring model performance/004 APRF example 1_ wine quality.mp4 107.3 MB
12 More on data/006 Data noise augmentation (with devset+test).mp4 106.1 MB
05 Math, numpy, PyTorch/010 Entropy and cross-entropy.mp4 106.0 MB
15 Weight inits and investigations/004 CodeChallenge_ Weight variance inits.mp4 104.0 MB
11 FFNs/002 The MNIST dataset.mp4 101.5 MB
18 Convolution and transformations/005 The Conv2 class in PyTorch.mp4 100.2 MB
30 Python intro_ Flow control/010 Function error checking and handling.mp4 99.9 MB
10 Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs.mp4 99.8 MB
14 FFN milestone projects/002 Project 1_ My solution.mp4 99.7 MB
09 Regularization/008 L1 regularization in practice.mp4 99.4 MB
13 Measuring model performance/005 APRF example 2_ MNIST.mp4 98.6 MB
08 Overfitting and cross-validation/004 Cross-validation -- manual separation.mp4 98.3 MB
10 Metaparameters (activations, optimizers)/017 Optimizers (minibatch, momentum).mp4 98.1 MB
18 Convolution and transformations/001 Convolution_ concepts.mp4 98.1 MB
10 Metaparameters (activations, optimizers)/009 Activation functions.mp4 97.0 MB
10 Metaparameters (activations, optimizers)/023 Learning rate decay.mp4 96.9 MB
21 Transfer learning/001 Transfer learning_ What, why, and when_.mp4 96.6 MB
11 FFNs/005 CodeChallenge_ Data normalization.mp4 96.3 MB
05 Math, numpy, PyTorch/008 Softmax.mp4 96.0 MB
06 Gradient descent/005 Gradient descent in 2D.mp4 95.9 MB
09 Regularization/012 CodeChallenge_ Effects of mini-batch size.mp4 95.4 MB
11 FFNs/007 CodeChallenge_ MNIST and breadth vs. depth.mp4 95.2 MB
07 ANNs/014 CodeChallenge_ more qwerties!.mp4 95.1 MB
31 Python intro_ Text and plots/001 Printing and string interpolation.mp4 94.8 MB
19 Understand and design CNNs/007 CodeChallenge_ How wide the FC_.mp4 94.1 MB
31 Python intro_ Text and plots/006 Images.mp4 93.6 MB
15 Weight inits and investigations/008 Freezing weights during learning.mp4 93.1 MB
18 Convolution and transformations/007 Transpose convolution.mp4 92.9 MB
19 Understand and design CNNs/015 CodeChallenge_ Varying number of channels.mp4 92.4 MB
10 Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.mp4 91.5 MB
30 Python intro_ Flow control/002 If-else statements, part 2.mp4 91.1 MB
30 Python intro_ Flow control/008 while loops.mp4 91.1 MB
30 Python intro_ Flow control/006 Initializing variables.mp4 91.0 MB
21 Transfer learning/002 Transfer learning_ MNIST -_ FMNIST.mp4 90.4 MB
10 Metaparameters (activations, optimizers)/014 Loss functions.mp4 90.3 MB
23 Generative adversarial networks/001 GAN_ What, why, and how.mp4 89.7 MB
07 ANNs/017 Defining models using sequential vs. class.mp4 89.5 MB
19 Understand and design CNNs/009 CodeChallenge_ AEs and occluded Gaussians.mp4 89.5 MB
09 Regularization/010 Batch training in action.mp4 89.1 MB
18 Convolution and transformations/008 Max_mean pooling.mp4 89.1 MB
17 Running models on a GPU/001 What is a GPU and why use it_.mp4 88.7 MB
29 Python intro_ Functions/005 Creating functions.mp4 88.4 MB
05 Math, numpy, PyTorch/011 Min_max and argmin_argmax.mp4 88.2 MB
08 Overfitting and cross-validation/002 Cross-validation.mp4 88.2 MB
15 Weight inits and investigations/007 CodeChallenge_ Identically random weights.mp4 88.2 MB
30 Python intro_ Flow control/003 For loops.mp4 87.1 MB
10 Metaparameters (activations, optimizers)/020 Optimizers comparison.mp4 86.9 MB
31 Python intro_ Text and plots/003 Subplot geometry.mp4 86.8 MB
05 Math, numpy, PyTorch/007 Matrix multiplication.mp4 85.7 MB
05 Math, numpy, PyTorch/013 Random sampling and sampling variability.mp4 85.4 MB
09 Regularization/006 Weight regularization (L1_L2)_ math.mp4 85.4 MB
07 ANNs/001 The perceptron and ANN architecture.mp4 83.6 MB
19 Understand and design CNNs/013 Dropout in CNNs.mp4 82.7 MB
13 Measuring model performance/007 Computation time.mp4 81.7 MB
05 Math, numpy, PyTorch/015 The t-test.mp4 81.4 MB
29 Python intro_ Functions/003 Python libraries (pandas).mp4 81.2 MB
18 Convolution and transformations/009 Pooling in PyTorch.mp4 81.0 MB
05 Math, numpy, PyTorch/012 Mean and variance.mp4 80.6 MB
05 Math, numpy, PyTorch/016 Derivatives_ intuition and polynomials.mp4 80.3 MB
09 Regularization/001 Regularization_ Concept and methods.mp4 80.0 MB
15 Weight inits and investigations/003 Theory_ Why and how to initialize weights.mp4 79.4 MB
08 Overfitting and cross-validation/007 Splitting data into train, devset, test.mp4 79.2 MB
27 Python intro_ Data types/003 Math and printing.mp4 78.5 MB
11 FFNs/010 Shifted MNIST.mp4 77.9 MB
27 Python intro_ Data types/002 Variables.mp4 77.6 MB
06 Gradient descent/004 CodeChallenge_ unfortunate starting value.mp4 77.1 MB
27 Python intro_ Data types/007 Booleans.mp4 76.8 MB
10 Metaparameters (activations, optimizers)/006 Batch normalization.mp4 76.8 MB
10 Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam).mp4 76.7 MB
17 Running models on a GPU/002 Implementation.mp4 76.6 MB
20 CNN milestone projects/005 Project 4_ Psychometric functions in CNNs.mp4 76.3 MB
14 FFN milestone projects/006 Project 3_ My solution.mp4 75.5 MB
24 Ethics of deep learning/004 Will deep learning take our jobs_.mp4 75.1 MB
30 Python intro_ Flow control/007 Single-line loops (list comprehension).mp4 75.1 MB
03 Concepts in deep learning/004 Running experiments to understand DL.mp4 74.8 MB
11 FFNs/011 CodeChallenge_ The mystery of the missing 7.mp4 74.3 MB
10 Metaparameters (activations, optimizers)/011 Activation functions comparison.mp4 73.9 MB
08 Overfitting and cross-validation/001 What is overfitting and is it as bad as they say_.mp4 73.1 MB
03 Concepts in deep learning/002 How models _learn_.mp4 72.8 MB
13 Measuring model performance/002 Accuracy, precision, recall, F1.mp4 72.6 MB
07 ANNs/015 Comparing the number of hidden units.mp4 71.2 MB
30 Python intro_ Flow control/009 Broadcasting in numpy.mp4 71.0 MB
07 ANNs/002 A geometric view of ANNs.mp4 70.9 MB
18 Convolution and transformations/002 Feature maps and convolution kernels.mp4 70.4 MB
24 Ethics of deep learning/005 Accountability and making ethical AI.mp4 70.1 MB
05 Math, numpy, PyTorch/014 Reproducible randomness via seeding.mp4 69.7 MB
15 Weight inits and investigations/001 Explanation of weight matrix sizes.mp4 69.0 MB
06 Gradient descent/001 Overview of gradient descent.mp4 68.4 MB
22 Style transfer/003 The style transfer algorithm.mp4 67.3 MB
06 Gradient descent/002 What about local minima_.mp4 67.1 MB
18 Convolution and transformations/004 Convolution parameters (stride, padding).mp4 66.9 MB
30 Python intro_ Flow control/001 If-else statements.mp4 66.8 MB
22 Style transfer/002 The Gram matrix (feature activation covariance).mp4 66.5 MB
24 Ethics of deep learning/003 Some other possible ethical scenarios.mp4 66.2 MB
29 Python intro_ Functions/006 Global and local variable scopes.mp4 66.0 MB
24 Ethics of deep learning/001 Will AI save us or destroy us_.mp4 65.9 MB
03 Concepts in deep learning/001 What is an artificial neural network_.mp4 65.4 MB
10 Metaparameters (activations, optimizers)/005 The importance of data normalization.mp4 64.6 MB
10 Metaparameters (activations, optimizers)/012 CodeChallenge_ Compare relu variants.mp4 64.0 MB
29 Python intro_ Functions/002 Python libraries (numpy).mp4 63.4 MB
23 Generative adversarial networks/003 CodeChallenge_ Linear GAN with FMNIST.mp4 62.7 MB
13 Measuring model performance/006 CodeChallenge_ MNIST with unequal groups.mp4 62.4 MB
09 Regularization/009 Training in mini-batches.mp4 62.1 MB
10 Metaparameters (activations, optimizers)/018 SGD with momentum.mp4 62.1 MB
10 Metaparameters (activations, optimizers)/007 Batch normalization in practice.mp4 61.8 MB
10 Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.mp4 61.7 MB
23 Generative adversarial networks/007 CodeChallenge_ CNN GAN with CIFAR.mp4 60.8 MB
08 Overfitting and cross-validation/008 Cross-validation on regression.mp4 60.4 MB
11 FFNs/009 Scrambled MNIST.mp4 60.2 MB
09 Regularization/011 The importance of equal batch sizes.mp4 60.1 MB
10 Metaparameters (activations, optimizers)/004 Data normalization.mp4 59.8 MB
31 Python intro_ Text and plots/005 Seaborn.mp4 59.7 MB
18 Convolution and transformations/006 CodeChallenge_ Choose the parameters.mp4 58.7 MB
30 Python intro_ Flow control/004 Enumerate and zip.mp4 58.6 MB
07 ANNs/021 Reflection_ Are DL models understandable yet_.mp4 58.6 MB
19 Understand and design CNNs/003 CNN on shifted MNIST.mp4 58.3 MB
07 ANNs/003 ANN math part 1 (forward prop).mp4 57.9 MB
19 Understand and design CNNs/001 The canonical CNN architecture.mp4 55.8 MB
12 More on data/009 Save and load trained models.mp4 55.7 MB
05 Math, numpy, PyTorch/018 Derivatives_ product and chain rules.mp4 55.6 MB
18 Convolution and transformations/010 To pool or to stride_.mp4 55.5 MB
19 Understand and design CNNs/014 CodeChallenge_ How low can you go_.mp4 55.4 MB
27 Python intro_ Data types/004 Lists (1 of 2).mp4 55.0 MB
01 Introduction/001 How to learn from this course.mp4 55.0 MB
23 Generative adversarial networks/006 CNN GAN with FMNIST.mp4 54.6 MB
01 Introduction/002 Using Udemy like a pro.mp4 54.4 MB
12 More on data/004 What to do about unbalanced designs_.mp4 54.2 MB
09 Regularization/005 Dropout example 2.mp4 53.9 MB
31 Python intro_ Text and plots/002 Plotting dots and lines.mp4 53.9 MB
22 Style transfer/005 CodeChallenge_ Style transfer with AlexNet.mp4 53.5 MB
23 Generative adversarial networks/005 CodeChallenge_ Gaussians with fewer layers.mp4 53.1 MB
10 Metaparameters (activations, optimizers)/022 CodeChallenge_ Adam with L2 regularization.mp4 53.0 MB
17 Running models on a GPU/003 CodeChallenge_ Run an experiment on the GPU.mp4 53.0 MB
24 Ethics of deep learning/002 Example case studies.mp4 52.9 MB
07 ANNs/005 ANN math part 3 (backprop).mp4 52.9 MB
13 Measuring model performance/003 APRF in code.mp4 51.8 MB
07 ANNs/019 CodeChallenge_ convert sequential to class.mp4 51.4 MB
28 Python intro_ Indexing, slicing/001 Indexing.mp4 51.1 MB
05 Math, numpy, PyTorch/002 Spectral theories in mathematics.mp4 51.1 MB
27 Python intro_ Data types/008 Dictionaries.mp4 50.7 MB
14 FFN milestone projects/003 Project 2_ Predicting heart disease.mp4 50.6 MB
07 ANNs/011 Linear solutions to linear problems.mp4 50.4 MB
05 Math, numpy, PyTorch/006 OMG it's the dot product!.mp4 50.1 MB
10 Metaparameters (activations, optimizers)/021 CodeChallenge_ Optimizers and... something.mp4 49.8 MB
11 FFNs/012 Universal approximation theorem.mp4 49.2 MB
16 Autoencoders/001 What are autoencoders and what do they do_.mp4 49.0 MB
29 Python intro_ Functions/004 Getting help on functions.mp4 48.6 MB
14 FFN milestone projects/001 Project 1_ A gratuitously complex adding machine.mp4 48.6 MB
07 ANNs/004 ANN math part 2 (errors, loss, cost).mp4 48.5 MB
28 Python intro_ Indexing, slicing/002 Slicing.mp4 48.5 MB
20 CNN milestone projects/001 Project 1_ Import and classify CIFAR10.mp4 48.4 MB
27 Python intro_ Data types/005 Lists (2 of 2).mp4 46.7 MB
11 FFNs/008 CodeChallenge_ Optimizers and MNIST.mp4 46.3 MB
02 Download all course materials/001 Downloading and using the code.mp4 45.6 MB
05 Math, numpy, PyTorch/017 Derivatives find minima.mp4 45.5 MB
14 FFN milestone projects/005 Project 3_ FFN for missing data interpolation.mp4 45.4 MB
13 Measuring model performance/008 Better performance in test than train_.mp4 44.8 MB
05 Math, numpy, PyTorch/009 Logarithms.mp4 43.9 MB
12 More on data/008 Getting data into colab.mp4 43.8 MB
31 Python intro_ Text and plots/007 Export plots in low and high resolution.mp4 43.6 MB
25 Where to go from here_/001 How to learn topic _X_ in deep learning_.mp4 42.0 MB
12 More on data/011 Where to find online datasets.mp4 41.7 MB
10 Metaparameters (activations, optimizers)/008 CodeChallenge_ Batch-normalize the qwerties.mp4 41.4 MB
21 Transfer learning/004 Famous CNN architectures.mp4 41.3 MB
11 FFNs/004 CodeChallenge_ Binarized MNIST images.mp4 40.8 MB
22 Style transfer/001 What is style transfer and how does it work_.mp4 40.6 MB
13 Measuring model performance/001 Two perspectives of the world.mp4 40.0 MB
06 Gradient descent/006 CodeChallenge_ 2D gradient ascent.mp4 39.4 MB
09 Regularization/002 train() and eval() modes.mp4 38.3 MB
05 Math, numpy, PyTorch/003 Terms and datatypes in math and computers.mp4 38.1 MB
05 Math, numpy, PyTorch/005 Vector and matrix transpose.mp4 37.7 MB
27 Python intro_ Data types/006 Tuples.mp4 35.8 MB
20 CNN milestone projects/003 Project 2_ CIFAR-autoencoder.mp4 33.4 MB
05 Math, numpy, PyTorch/004 Converting reality to numbers.mp4 33.2 MB
30 Python intro_ Flow control/005 Continue.mp4 33.0 MB
10 Metaparameters (activations, optimizers)/001 What are _metaparameters__.mp4 32.7 MB
08 Overfitting and cross-validation/003 Generalization.mp4 32.4 MB
06 Gradient descent/009 Vanishing and exploding gradients.mp4 30.2 MB
29 Python intro_ Functions/001 Inputs and outputs.mp4 29.5 MB
15 Weight inits and investigations/010 Use default inits or apply your own_.mp4 28.1 MB
07 ANNs/012 Why multilayer linear models don't exist.mp4 26.5 MB
20 CNN milestone projects/004 Project 3_ FMNIST.mp4 26.5 MB
11 FFNs/001 What are fully-connected and feedforward networks_.mp4 25.5 MB
29 Python intro_ Functions/007 Copies and referents of variables.mp4 23.8 MB
04 About the Python tutorial/001 Should you watch the Python tutorial_.mp4 23.8 MB
06 Gradient descent/010 Tangent_ Notebook revision history.mp4 22.2 MB
27 Python intro_ Data types/001 How to learn from the Python tutorial.mp4 22.0 MB
19 Understand and design CNNs/016 So many possibilities! How to create a CNN_.mp4 21.0 MB
21 Transfer learning/006 CodeChallenge_ VGG-16.mp4 20.3 MB
05 Math, numpy, PyTorch/001 Introduction to this section.mp4 11.1 MB
02 Download all course materials/002 My policy on code-sharing.mp4 10.2 MB
02 Download all course materials/003 DUDL_PythonCode.zip 700.8 KB
19 Understand and design CNNs/005 Examine feature map activations.en.srt 40.5 KB
19 Understand and design CNNs/002 CNN to classify MNIST digits.en.srt 38.0 KB
07 ANNs/013 Multi-output ANN (iris dataset).en.srt 37.5 KB
07 ANNs/009 Learning rates comparison.en.srt 36.2 KB
19 Understand and design CNNs/012 The EMNIST dataset (letter recognition).en.srt 36.1 KB
07 ANNs/006 ANN for regression.en.srt 35.8 KB
16 Autoencoders/006 Autoencoder with tied weights.en.srt 34.8 KB
19 Understand and design CNNs/004 Classify Gaussian blurs.en.srt 34.3 KB
07 ANNs/008 ANN for classifying qwerties.en.srt 34.0 KB
09 Regularization/004 Dropout regularization in practice.en.srt 33.4 KB
11 FFNs/003 FFN to classify digits.en.srt 32.9 KB
15 Weight inits and investigations/009 Learning-related changes in weights.en.srt 32.8 KB
18 Convolution and transformations/001 Convolution_ concepts.en.srt 32.5 KB
22 Style transfer/004 Transferring the screaming bathtub.en.srt 32.3 KB
23 Generative adversarial networks/002 Linear GAN with MNIST.en.srt 32.0 KB
16 Autoencoders/005 The latent code of MNIST.en.srt 31.7 KB
09 Regularization/003 Dropout regularization.en.srt 31.2 KB
07 ANNs/018 Model depth vs. breadth.en.srt 30.9 KB
29 Python intro_ Functions/005 Creating functions.en.srt 30.9 KB
18 Convolution and transformations/003 Convolution in code.en.srt 30.5 KB
08 Overfitting and cross-validation/005 Cross-validation -- scikitlearn.en.srt 30.5 KB
19 Understand and design CNNs/010 CodeChallenge_ Custom loss functions.en.srt 29.9 KB
07 ANNs/010 Multilayer ANN.en.srt 29.4 KB
12 More on data/003 CodeChallenge_ unbalanced data.en.srt 29.3 KB
16 Autoencoders/003 CodeChallenge_ How many units_.en.srt 28.9 KB
21 Transfer learning/007 Pretraining with autoencoders.en.srt 28.8 KB
08 Overfitting and cross-validation/006 Cross-validation -- DataLoader.en.srt 28.6 KB
12 More on data/007 Data feature augmentation.en.srt 28.3 KB
07 ANNs/007 CodeChallenge_ manipulate regression slopes.en.srt 28.3 KB
30 Python intro_ Flow control/008 while loops.en.srt 27.9 KB
14 FFN milestone projects/004 Project 2_ My solution.en.srt 27.8 KB
27 Python intro_ Data types/007 Booleans.en.srt 27.8 KB
05 Math, numpy, PyTorch/008 Softmax.en.srt 27.8 KB
27 Python intro_ Data types/002 Variables.en.srt 27.3 KB
06 Gradient descent/007 Parametric experiments on g.d.en.srt 27.1 KB
09 Regularization/006 Weight regularization (L1_L2)_ math.en.srt 27.1 KB
31 Python intro_ Text and plots/004 Making the graphs look nicer.en.srt 27.0 KB
10 Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.en.srt 26.9 KB
07 ANNs/001 The perceptron and ANN architecture.en.srt 26.8 KB
27 Python intro_ Data types/003 Math and printing.en.srt 26.8 KB
18 Convolution and transformations/008 Max_mean pooling.en.srt 26.7 KB
29 Python intro_ Functions/008 Classes and object-oriented programming.en.srt 26.6 KB
18 Convolution and transformations/012 Creating and using custom DataLoaders.en.srt 26.5 KB
10 Metaparameters (activations, optimizers)/009 Activation functions.en.srt 26.5 KB
12 More on data/001 Anatomy of a torch dataset and dataloader.en.srt 26.5 KB
21 Transfer learning/008 CIFAR10 with autoencoder-pretrained model.en.srt 25.9 KB
31 Python intro_ Text and plots/006 Images.en.srt 25.8 KB
07 ANNs/016 Depth vs. breadth_ number of parameters.en.srt 25.7 KB
10 Metaparameters (activations, optimizers)/002 The _wine quality_ dataset.en.srt 25.7 KB
30 Python intro_ Flow control/006 Initializing variables.en.srt 25.6 KB
25 Where to go from here_/002 How to read academic DL papers.en.srt 25.4 KB
16 Autoencoders/004 AEs for occlusion.en.srt 25.4 KB
30 Python intro_ Flow control/010 Function error checking and handling.en.srt 25.4 KB
30 Python intro_ Flow control/003 For loops.en.srt 25.2 KB
19 Understand and design CNNs/006 CodeChallenge_ Softcode internal parameters.en.srt 25.0 KB
10 Metaparameters (activations, optimizers)/013 CodeChallenge_ Predict sugar.en.srt 25.0 KB
08 Overfitting and cross-validation/002 Cross-validation.en.srt 25.0 KB
21 Transfer learning/001 Transfer learning_ What, why, and when_.en.srt 24.8 KB
06 Gradient descent/003 Gradient descent in 1D.en.srt 24.7 KB
15 Weight inits and investigations/006 CodeChallenge_ Xavier vs. Kaiming.en.srt 24.7 KB
21 Transfer learning/005 Transfer learning with ResNet-18.en.srt 24.6 KB
11 FFNs/005 CodeChallenge_ Data normalization.en.srt 24.5 KB
05 Math, numpy, PyTorch/016 Derivatives_ intuition and polynomials.en.srt 24.4 KB
19 Understand and design CNNs/008 Do autoencoders clean Gaussians_.en.srt 24.4 KB
31 Python intro_ Text and plots/001 Printing and string interpolation.en.srt 24.3 KB
10 Metaparameters (activations, optimizers)/014 Loss functions.en.srt 24.2 KB
03 Concepts in deep learning/005 Are artificial _neurons_ like biological neurons_.en.srt 24.2 KB
12 More on data/005 Data oversampling in MNIST.en.srt 24.2 KB
18 Convolution and transformations/011 Image transforms.en.srt 23.9 KB
15 Weight inits and investigations/002 A surprising demo of weight initializations.en.srt 23.9 KB
23 Generative adversarial networks/001 GAN_ What, why, and how.en.srt 23.5 KB
12 More on data/002 Data size and network size.en.srt 23.4 KB
03 Concepts in deep learning/003 The role of DL in science and knowledge.en.srt 23.3 KB
19 Understand and design CNNs/011 Discover the Gaussian parameters.en.srt 23.2 KB
31 Python intro_ Text and plots/003 Subplot geometry.en.srt 23.1 KB
06 Gradient descent/008 CodeChallenge_ fixed vs. dynamic learning rate.en.srt 23.1 KB
10 Metaparameters (activations, optimizers)/003 CodeChallenge_ Minibatch size in the wine dataset.en.srt 23.0 KB
30 Python intro_ Flow control/002 If-else statements, part 2.en.srt 22.9 KB
16 Autoencoders/002 Denoising MNIST.en.srt 22.8 KB
15 Weight inits and investigations/005 Xavier and Kaiming initializations.en.srt 22.5 KB
05 Math, numpy, PyTorch/012 Mean and variance.en.srt 22.5 KB
17 Running models on a GPU/001 What is a GPU and why use it_.en.srt 22.5 KB
23 Generative adversarial networks/004 CNN GAN with Gaussians.en.srt 22.1 KB
10 Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam).en.srt 22.1 KB
11 FFNs/006 Distributions of weights pre- and post-learning.en.srt 22.1 KB
12 More on data/010 Save the best-performing model.en.srt 22.0 KB
30 Python intro_ Flow control/007 Single-line loops (list comprehension).en.srt 21.7 KB
30 Python intro_ Flow control/001 If-else statements.en.srt 21.7 KB
06 Gradient descent/005 Gradient descent in 2D.en.srt 21.3 KB
30 Python intro_ Flow control/009 Broadcasting in numpy.en.srt 21.3 KB
03 Concepts in deep learning/001 What is an artificial neural network_.en.srt 21.3 KB
06 Gradient descent/001 Overview of gradient descent.en.srt 20.9 KB
05 Math, numpy, PyTorch/007 Matrix multiplication.en.srt 20.6 KB
21 Transfer learning/003 CodeChallenge_ letters to numbers.en.srt 20.5 KB
27 Python intro_ Data types/004 Lists (1 of 2).en.srt 20.5 KB
10 Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs.en.srt 20.3 KB
29 Python intro_ Functions/003 Python libraries (pandas).en.srt 20.3 KB
29 Python intro_ Functions/002 Python libraries (numpy).en.srt 20.0 KB
18 Convolution and transformations/007 Transpose convolution.en.srt 19.9 KB
10 Metaparameters (activations, optimizers)/004 Data normalization.en.srt 19.7 KB
29 Python intro_ Functions/006 Global and local variable scopes.en.srt 19.7 KB
18 Convolution and transformations/009 Pooling in PyTorch.en.srt 19.6 KB
19 Understand and design CNNs/015 CodeChallenge_ Varying number of channels.en.srt 19.6 KB
05 Math, numpy, PyTorch/015 The t-test.en.srt 19.4 KB
07 ANNs/002 A geometric view of ANNs.en.srt 19.4 KB
13 Measuring model performance/004 APRF example 1_ wine quality.en.srt 19.3 KB
15 Weight inits and investigations/008 Freezing weights during learning.en.srt 19.3 KB
03 Concepts in deep learning/004 Running experiments to understand DL.en.srt 19.2 KB
07 ANNs/017 Defining models using sequential vs. class.en.srt 19.2 KB
09 Regularization/001 Regularization_ Concept and methods.en.srt 19.0 KB
09 Regularization/007 L2 regularization in practice.en.srt 19.0 KB
18 Convolution and transformations/005 The Conv2 class in PyTorch.en.srt 18.9 KB
03 Concepts in deep learning/002 How models _learn_.en.srt 18.8 KB
11 FFNs/002 The MNIST dataset.en.srt 18.7 KB
10 Metaparameters (activations, optimizers)/006 Batch normalization.en.srt 18.7 KB
12 More on data/006 Data noise augmentation (with devset+test).en.srt 18.6 KB
08 Overfitting and cross-validation/004 Cross-validation -- manual separation.en.srt 18.6 KB
15 Weight inits and investigations/004 CodeChallenge_ Weight variance inits.en.srt 18.4 KB
08 Overfitting and cross-validation/001 What is overfitting and is it as bad as they say_.en.srt 18.3 KB
15 Weight inits and investigations/003 Theory_ Why and how to initialize weights.en.srt 18.2 KB
05 Math, numpy, PyTorch/011 Min_max and argmin_argmax.en.srt 18.2 KB
28 Python intro_ Indexing, slicing/001 Indexing.en.srt 18.1 KB
09 Regularization/012 CodeChallenge_ Effects of mini-batch size.en.srt 18.1 KB
18 Convolution and transformations/004 Convolution parameters (stride, padding).en.srt 18.1 KB
13 Measuring model performance/002 Accuracy, precision, recall, F1.en.srt 18.1 KB
28 Python intro_ Indexing, slicing/002 Slicing.en.srt 18.0 KB
15 Weight inits and investigations/007 CodeChallenge_ Identically random weights.en.srt 17.9 KB
10 Metaparameters (activations, optimizers)/023 Learning rate decay.en.srt 17.9 KB
07 ANNs/014 CodeChallenge_ more qwerties!.en.srt 17.8 KB
11 FFNs/007 CodeChallenge_ MNIST and breadth vs. depth.en.srt 17.7 KB
31 Python intro_ Text and plots/002 Plotting dots and lines.en.srt 17.7 KB
09 Regularization/008 L1 regularization in practice.en.srt 17.4 KB
07 ANNs/003 ANN math part 1 (forward prop).en.srt 17.4 KB
15 Weight inits and investigations/001 Explanation of weight matrix sizes.en.srt 17.2 KB
06 Gradient descent/002 What about local minima_.en.srt 17.2 KB
13 Measuring model performance/005 APRF example 2_ MNIST.en.srt 17.2 KB
20 CNN milestone projects/002 Project 1_ My solution.en.srt 17.2 KB
27 Python intro_ Data types/008 Dictionaries.en.srt 17.1 KB
14 FFN milestone projects/002 Project 1_ My solution.en.srt 17.0 KB
10 Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.en.srt 17.0 KB
16 Autoencoders/001 What are autoencoders and what do they do_.en.srt 16.9 KB
20 CNN milestone projects/005 Project 4_ Psychometric functions in CNNs.en.srt 16.9 KB
09 Regularization/009 Training in mini-batches.en.srt 16.9 KB
22 Style transfer/002 The Gram matrix (feature activation covariance).en.srt 16.8 KB
24 Ethics of deep learning/005 Accountability and making ethical AI.en.srt 16.7 KB
10 Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.en.srt 16.6 KB
19 Understand and design CNNs/007 CodeChallenge_ How wide the FC_.en.srt 16.5 KB
11 FFNs/010 Shifted MNIST.en.srt 16.5 KB
05 Math, numpy, PyTorch/013 Random sampling and sampling variability.en.srt 16.4 KB
30 Python intro_ Flow control/004 Enumerate and zip.en.srt 16.0 KB
06 Gradient descent/004 CodeChallenge_ unfortunate starting value.en.srt 16.0 KB
11 FFNs/011 CodeChallenge_ The mystery of the missing 7.en.srt 15.8 KB
31 Python intro_ Text and plots/005 Seaborn.en.srt 15.7 KB
19 Understand and design CNNs/001 The canonical CNN architecture.en.srt 15.7 KB
09 Regularization/010 Batch training in action.en.srt 15.6 KB
07 ANNs/005 ANN math part 3 (backprop).en.srt 15.2 KB
24 Ethics of deep learning/003 Some other possible ethical scenarios.en.srt 15.2 KB
22 Style transfer/003 The style transfer algorithm.en.srt 15.1 KB
24 Ethics of deep learning/004 Will deep learning take our jobs_.en.srt 14.9 KB
17 Running models on a GPU/002 Implementation.en.srt 14.8 KB
10 Metaparameters (activations, optimizers)/020 Optimizers comparison.en.srt 14.6 KB
07 ANNs/015 Comparing the number of hidden units.en.srt 14.6 KB
21 Transfer learning/002 Transfer learning_ MNIST -_ FMNIST.en.srt 14.6 KB
27 Python intro_ Data types/005 Lists (2 of 2).en.srt 14.6 KB
14 FFN milestone projects/005 Project 3_ FFN for missing data interpolation.en.srt 14.4 KB
24 Ethics of deep learning/001 Will AI save us or destroy us_.en.srt 14.4 KB
18 Convolution and transformations/010 To pool or to stride_.en.srt 14.3 KB
13 Measuring model performance/007 Computation time.en.srt 14.3 KB
19 Understand and design CNNs/013 Dropout in CNNs.en.srt 14.2 KB
19 Understand and design CNNs/009 CodeChallenge_ AEs and occluded Gaussians.en.srt 14.0 KB
18 Convolution and transformations/002 Feature maps and convolution kernels.en.srt 14.0 KB
05 Math, numpy, PyTorch/006 OMG it's the dot product!.en.srt 13.9 KB
07 ANNs/004 ANN math part 2 (errors, loss, cost).en.srt 13.9 KB
23 Generative adversarial networks/003 CodeChallenge_ Linear GAN with FMNIST.en.srt 13.9 KB
08 Overfitting and cross-validation/007 Splitting data into train, devset, test.en.srt 13.8 KB
10 Metaparameters (activations, optimizers)/005 The importance of data normalization.en.srt 13.7 KB
10 Metaparameters (activations, optimizers)/011 Activation functions comparison.en.srt 13.6 KB
05 Math, numpy, PyTorch/002 Spectral theories in mathematics.en.srt 13.6 KB
05 Math, numpy, PyTorch/018 Derivatives_ product and chain rules.en.srt 13.5 KB
13 Measuring model performance/006 CodeChallenge_ MNIST with unequal groups.en.srt 12.7 KB
07 ANNs/021 Reflection_ Are DL models understandable yet_.en.srt 12.4 KB
01 Introduction/002 Using Udemy like a pro.en.srt 12.3 KB
25 Where to go from here_/001 How to learn topic _X_ in deep learning_.en.srt 12.3 KB
05 Math, numpy, PyTorch/017 Derivatives find minima.en.srt 12.2 KB
07 ANNs/011 Linear solutions to linear problems.en.srt 12.2 KB
19 Understand and design CNNs/003 CNN on shifted MNIST.en.srt 12.1 KB
27 Python intro_ Data types/006 Tuples.en.srt 12.0 KB
08 Overfitting and cross-validation/008 Cross-validation on regression.en.srt 12.0 KB
13 Measuring model performance/008 Better performance in test than train_.en.srt 11.9 KB
14 FFN milestone projects/006 Project 3_ My solution.en.srt 11.9 KB
05 Math, numpy, PyTorch/014 Reproducible randomness via seeding.en.srt 11.8 KB
11 FFNs/012 Universal approximation theorem.en.srt 11.7 KB
23 Generative adversarial networks/007 CodeChallenge_ CNN GAN with CIFAR.en.srt 11.6 KB
10 Metaparameters (activations, optimizers)/018 SGD with momentum.en.srt 11.6 KB
05 Math, numpy, PyTorch/009 Logarithms.en.srt 11.5 KB
31 Python intro_ Text and plots/007 Export plots in low and high resolution.en.srt 11.4 KB
10 Metaparameters (activations, optimizers)/012 CodeChallenge_ Compare relu variants.en.srt 11.3 KB
11 FFNs/009 Scrambled MNIST.en.srt 11.2 KB
29 Python intro_ Functions/004 Getting help on functions.en.srt 11.1 KB
10 Metaparameters (activations, optimizers)/007 Batch normalization in practice.en.srt 11.0 KB
14 FFN milestone projects/003 Project 2_ Predicting heart disease.en.srt 11.0 KB
14 FFN milestone projects/001 Project 1_ A gratuitously complex adding machine.en.srt 10.7 KB
05 Math, numpy, PyTorch/003 Terms and datatypes in math and computers.en.srt 10.7 KB
29 Python intro_ Functions/001 Inputs and outputs.en.srt 10.6 KB
20 CNN milestone projects/001 Project 1_ Import and classify CIFAR10.en.srt 10.6 KB
22 Style transfer/005 CodeChallenge_ Style transfer with AlexNet.en.srt 10.5 KB
13 Measuring model performance/001 Two perspectives of the world.en.srt 10.3 KB
10 Metaparameters (activations, optimizers)/022 CodeChallenge_ Adam with L2 regularization.en.srt 10.3 KB
09 Regularization/002 train() and eval() modes.en.srt 10.2 KB
18 Convolution and transformations/006 CodeChallenge_ Choose the parameters.en.srt 10.1 KB
30 Python intro_ Flow control/005 Continue.en.srt 10.1 KB
05 Math, numpy, PyTorch/005 Vector and matrix transpose.en.srt 10.0 KB
19 Understand and design CNNs/014 CodeChallenge_ How low can you go_.en.srt 10.0 KB
11 FFNs/008 CodeChallenge_ Optimizers and MNIST.en.srt 9.9 KB
17 Running models on a GPU/003 CodeChallenge_ Run an experiment on the GPU.en.srt 9.8 KB
07 ANNs/019 CodeChallenge_ convert sequential to class.en.srt 9.7 KB
05 Math, numpy, PyTorch/004 Converting reality to numbers.en.srt 9.6 KB
09 Regularization/011 The importance of equal batch sizes.en.srt 9.5 KB
02 Download all course materials/001 Downloading and using the code.en.srt 9.4 KB
13 Measuring model performance/003 APRF in code.en.srt 9.4 KB
10 Metaparameters (activations, optimizers)/021 CodeChallenge_ Optimizers and... something.en.srt 9.4 KB
23 Generative adversarial networks/006 CNN GAN with FMNIST.en.srt 9.2 KB
07 ANNs/012 Why multilayer linear models don't exist.en.srt 9.2 KB
09 Regularization/005 Dropout example 2.en.srt 9.2 KB
24 Ethics of deep learning/002 Example case studies.en.srt 9.2 KB
06 Gradient descent/009 Vanishing and exploding gradients.en.srt 9.1 KB
12 More on data/009 Save and load trained models.en.srt 8.9 KB
23 Generative adversarial networks/005 CodeChallenge_ Gaussians with fewer layers.en.srt 8.9 KB
12 More on data/008 Getting data into colab.en.srt 8.8 KB
08 Overfitting and cross-validation/003 Generalization.en.srt 8.8 KB
21 Transfer learning/004 Famous CNN architectures.en.srt 8.7 KB
12 More on data/011 Where to find online datasets.en.srt 8.2 KB
06 Gradient descent/006 CodeChallenge_ 2D gradient ascent.en.srt 7.5 KB
10 Metaparameters (activations, optimizers)/008 CodeChallenge_ Batch-normalize the qwerties.en.srt 7.5 KB
11 FFNs/004 CodeChallenge_ Binarized MNIST images.en.srt 7.4 KB
10 Metaparameters (activations, optimizers)/001 What are _metaparameters__.en.srt 7.3 KB
29 Python intro_ Functions/007 Copies and referents of variables.en.srt 7.2 KB
20 CNN milestone projects/003 Project 2_ CIFAR-autoencoder.en.srt 7.0 KB
11 FFNs/001 What are fully-connected and feedforward networks_.en.srt 6.9 KB
19 Understand and design CNNs/016 So many possibilities! How to create a CNN_.en.srt 6.5 KB
22 Style transfer/001 What is style transfer and how does it work_.en.srt 6.3 KB
15 Weight inits and investigations/010 Use default inits or apply your own_.en.srt 6.3 KB
04 About the Python tutorial/001 Should you watch the Python tutorial_.en.srt 6.1 KB
20 CNN milestone projects/004 Project 3_ FMNIST.en.srt 5.1 KB
21 Transfer learning/006 CodeChallenge_ VGG-16.en.srt 5.1 KB
27 Python intro_ Data types/001 How to learn from the Python tutorial.en.srt 4.8 KB
26 Bonus section/001 Bonus content.html 4.5 KB
05 Math, numpy, PyTorch/001 Introduction to this section.en.srt 2.9 KB
06 Gradient descent/010 Tangent_ Notebook revision history.en.srt 2.8 KB
02 Download all course materials/002 My policy on code-sharing.en.srt 2.5 KB
07 ANNs/020 Diversity of ANN visual representations.html 1.4 KB
0. Websites you may like/[CourseClub.Me].url 122 bytes
06 Gradient descent/[CourseClub.Me].url 122 bytes
13 Measuring model performance/[CourseClub.Me].url 122 bytes
21 Transfer learning/[CourseClub.Me].url 122 bytes
30 Python intro_ Flow control/[CourseClub.Me].url 122 bytes
[CourseClub.Me].url 122 bytes
0. Websites you may like/[GigaCourse.Com].url 49 bytes
06 Gradient descent/[GigaCourse.Com].url 49 bytes
13 Measuring model performance/[GigaCourse.Com].url 49 bytes
21 Transfer learning/[GigaCourse.Com].url 49 bytes
30 Python intro_ Flow control/[GigaCourse.Com].url 49 bytes
[GigaCourse.Com].url 49 bytes

[GigaCourse.Com] Udemy - A deep understanding of deep learning (with Python intro)

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