Course Content
16 – Autoencoders
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001 What are autoencoders and what do they do.mp4
11:42
002 Denoising MNIST.mp4
15:48
003 CodeChallenge How many units.mp4
19:52
004 AEs for occlusion.mp4
17:55
005 The latent code of MNIST.mp4
21:57
006 Autoencoder with tied weights.mp4
24:14
31 – Python intro Text and plots
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001 Printing and string interpolation.mp4
17:17
002 Plotting dots and lines.mp4
12:54
003 Subplot geometry.mp4
16:10
004 Making the graphs look nicer.mp4
18:48
005 Seaborn.mp4
11:08
006 Images.mp4
17:59
007 Export plots in low and high resolution.mp4
07:58
30 – Python intro Flow control
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001 If-else statements.mp4
15:03
002 If-else statements, part 2.mp4
16:58
003 For loops.mp4
17:37
004 Enumerate and zip.mp4
12:11
005 Continue.mp4
07:23
006 Initializing variables.mp4
18:00
007 Single-line loops (list comprehension).mp4
15:25
008 while loops.mp4
19:30
009 Broadcasting in numpy.mp4
15:40
010 Function error checking and handling.mp4
17:42
29 – Python intro Functions
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001 Inputs and outputs.mp4
00:00
002 Python libraries (numpy).mp4
14:20
003 Python libraries (pandas).mp4
13:57
004 Getting help on functions.mp4
07:36
005 Creating functions.mp4
20:27
006 Global and local variable scopes.mp4
13:20
007 Copies and referents of variables.mp4
05:45
008 Classes and object-oriented programming.mp4
18:46
28 – Python intro Indexing, slicing
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001 Indexing.mp4
12:30
002 Slicing.mp4
11:45
27 – Python intro Data types
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001 How to learn from the Python tutorial.mp4
03:25
002 Variables.mp4
18:14
003 Math and printing.mp4
18:31
004 Lists (1 of 2).mp4
13:31
005 Lists (2 of 2).mp4
09:29
006 Tuples.mp4
07:40
007 Booleans.mp4
18:19
008 Dictionaries.mp4
11:51
26 – Where to go from here
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001 How to learn topic _X_ in deep learning.mp4
08:08
002 How to read academic DL papers.mp4
15:59
25 – Ethics of deep learning
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001 Will AI save us or destroy us.mp4
09:40
002 Example case studies.mp4
06:39
003 Some other possible ethical scenarios.mp4
10:35
004 Will deep learning take our jobs.mp4
10:27
005 Accountability and making ethical AI.mp4
11:22
24 – RNNs (Recurrent Neural Networks) (and GRULSTM)
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001 Leveraging sequences in deep learning.mp4
12:53
002 How RNNs work.mp4
15:14
003 The RNN class in PyTorch.mp4
17:44
004 Predicting alternating sequences.mp4
19:30
005 CodeChallenge sine wave extrapolation.mp4
24:49
006 More on RNNs Hidden states, embeddings.mp4
15:51
007 GRU and LSTM.mp4
23:08
008 The LSTM and GRU classes.mp4
13:26
009 Lorem ipsum.mp4
25:10
23 – Generative adversarial networks
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001 GAN What, why, and how.mp4
17:22
002 Linear GAN with MNIST.mp4
21:55
003 CodeChallenge Linear GAN with FMNIST.mp4
09:50
004 CNN GAN with Gaussians.mp4
15:06
005 CodeChallenge Gaussians with fewer layers.mp4
06:05
006 CNN GAN with FMNIST.mp4
06:23
007 CodeChallenge CNN GAN with CIFAR.mp4
07:51
22 – Style transfer
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001 What is style transfer and how does it work.mp4
04:36
002 The Gram matrix (feature activation covariance).mp4
12:37
003 The style transfer algorithm.mp4
10:58
004 Transferring the screaming bathtub.mp4
22:16
005 CodeChallenge Style transfer with AlexNet.mp4
07:14
21 – Transfer learning
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001 Transfer learning What, why, and when.mp4
16:52
002 Transfer learning MNIST – FMNIST.mp4
10:06
003 CodeChallenge letters to numbers.mp4
14:25
004 Famous CNN architectures.mp4
06:45
005 Transfer learning with ResNet-18.mp4
16:43
006 CodeChallenge VGG-16.mp4
03:41
007 Pretraining with autoencoders.mp4
20:01
008 CIFAR10 with autoencoder-pretrained model.mp4
18:11
20 – CNN milestone projects
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001 Project 1 Import and classify CIFAR10.mp4
07:15
002 Project 1 My solution.mp4
12:01
003 Project 2 CIFAR-autoencoder.mp4
04:51
004 Project 3 FMNIST.mp4
03:52
005 Project 4 Psychometric functions in CNNs.mp4
11:54
19 – Understand and design CNNs
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001 The canonical CNN architecture.mp4
10:47
002 CNN to classify MNIST digits.mp4
26:06
003 CNN on shifted MNIST.mp4
08:36
004 Classify Gaussian blurs.mp4
24:10
005 Examine feature map activations.mp4
27:50
006 CodeChallenge Softcode internal parameters.mp4
16:48
007 CodeChallenge How wide the FC.mp4
11:25
008 Do autoencoders clean Gaussians.mp4
17:10
009 CodeChallenge AEs and occluded Gaussians.mp4
09:37
010 CodeChallenge Custom loss functions.mp4
20:14
011 Discover the Gaussian parameters.mp4
16:58
012 The EMNIST dataset (letter recognition).mp4
24:59
013 Dropout in CNNs.mp4
10:14
014 CodeChallenge How low can you go.mp4
06:45
015 CodeChallenge Varying number of channels.mp4
13:39
016 So many possibilities! How to create a CNN.mp4
04:42
18 – Convolution and transformations
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001 Convolution concepts.mp4
21:33
002 Feature maps and convolution kernels.mp4
09:32
003 Convolution in code.mp4
21:04
004 Convolution parameters (stride, padding).mp4
12:14
005 The Conv2 class in PyTorch.mp4
13:23
006 CodeChallenge Choose the parameters.mp4
07:10
007 Transpose convolution.mp4
13:41
008 Maxmean pooling.mp4
18:35
009 Pooling in PyTorch.mp4
13:43
010 To pool or to stride.mp4
09:47
011 Image transforms.mp4
16:57
012 Creating and using custom DataLoaders.mp4
19:05
17 – Running models on a GPU
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001 What is a GPU and why use it.mp4
15:07
002 Implementation.mp4
10:13
003 CodeChallenge Run an experiment on the GPU.mp4
06:46
01 – Introduction
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001 How to learn from this course.mp4
09:25
002 Using Udemy like a pro.mp4
07:57
15 – Weight inits and investigations
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001 Explanation of weight matrix sizes.mp4
11:53
002 A surprising demo of weight initializations.mp4
15:52
003 Theory Why and how to initialize weights.mp4
12:46
004 CodeChallenge Weight variance inits.mp4
13:14
005 Xavier and Kaiming initializations.mp4
15:42
006 CodeChallenge Xavier vs. Kaiming.mp4
16:54
007 CodeChallenge Identically random weights.mp4
12:40
008 Freezing weights during learning.mp4
12:58
009 Learning-related changes in weights.mp4
21:54
010 Use default inits or apply your own.mp4
04:36
14 – FFN milestone projects
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001 Project 1 A gratuitously complex adding machine.mp4
07:05
002 Project 1 My solution.mp4
11:18
003 Project 2 Predicting heart disease.mp4
07:14
004 Project 2 My solution.mp4
18:20
005 Project 3 FFN for missing data interpolation.mp4
09:34
006 Project 3 My solution.mp4
08:31
13 – Measuring model performance
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001 Two perspectives of the world.mp4
07:01
002 Accuracy, precision, recall, F1.mp4
12:39
003 APRF in code.mp4
06:42
004 APRF example 1 wine quality.mp4
13:34
005 APRF example 2 MNIST.mp4
12:01
006 CodeChallenge MNIST with unequal groups.mp4
09:14
007 Computation time.mp4
09:55
008 Better performance in test than train.mp4
08:35
12 – More on data
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001 Anatomy of a torch dataset and dataloader.mp4
17:57
002 Data size and network size.mp4
16:35
003 CodeChallenge unbalanced data.mp4
20:05
004 What to do about unbalanced designs.mp4
07:45
005 Data oversampling in MNIST.mp4
16:30
006 Data noise augmentation (with devset+test).mp4
13:16
007 Data feature augmentation.mp4
19:40
008 Getting data into colab.mp4
06:05
009 Save and load trained models.mp4
06:14
010 Save the best-performing model.mp4
15:18
011 Where to find online datasets.mp4
05:32
11 – FFNs (Feed-Forward Networks)
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001 What are fully-connected and feedforward networks.mp4
04:58
002 The MNIST dataset.mp4
12:33
003 FFN to classify digits.mp4
22:20
004 CodeChallenge Binarized MNIST images.mp4
05:24
005 CodeChallenge Data normalization.mp4
16:16
006 Distributions of weights pre- and post-learning.mp4
14:48
007 CodeChallenge MNIST and breadth vs. depth.mp4
12:35
008 CodeChallenge Optimizers and MNIST.mp4
07:06
009 Scrambled MNIST.mp4
08:00
010 Shifted MNIST.mp4
11:24
011 CodeChallenge The mystery of the missing 7.mp4
10:47
012 Universal approximation theorem.mp4
08:31
10 – Metaparameters (activations, optimizers)
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013 CodeChallenge Predict sugar.mp4
17:06
024 How to pick the right metaparameters.mp4
11:47
023 Learning rate decay.mp4
12:15
022 CodeChallenge Adam with L2 regularization.mp4
07:42
021 CodeChallenge Optimizers and… something.mp4
06:57
020 Optimizers comparison.mp4
10:17
019 Optimizers (RMSprop, Adam).mp4
15:40
018 SGD with momentum.mp4
07:46
017 Optimizers (minibatch, momentum).mp4
18:41
016 More practice with multioutput ANNs.mp4
14:04
015 Loss functions in PyTorch.mp4
18:41
014 Loss functions.mp4
16:47
001 What are metaparameters.mp4
05:02
012 CodeChallenge Compare relu variants.mp4
07:48
011 Activation functions comparison.mp4
09:27
010 Activation functions in PyTorch.mp4
12:12
009 Activation functions.mp4
17:59
008 CodeChallenge Batch-normalize the qwerties.mp4
05:06
007 Batch normalization in practice.mp4
07:38
006 Batch normalization.mp4
13:15
005 The importance of data normalization.mp4
09:33
004 Data normalization.mp4
13:12
003 CodeChallenge Minibatch size in the wine dataset.mp4
15:38
002 The wine quality dataset.mp4
17:29
09 – Regularization
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001 Regularization Concept and methods.mp4
13:38
002 train() and eval() modes.mp4
07:14
003 Dropout regularization.mp4
21:56
004 Dropout regularization in practice.mp4
23:13
005 Dropout example 2.mp4
06:33
006 Weight regularization (L1L2) math.mp4
18:25
007 L2 regularization in practice.mp4
13:24
008 L1 regularization in practice.mp4
12:22
009 Training in mini-batches.mp4
11:32
010 Batch training in action.mp4
10:47
011 The importance of equal batch sizes.mp4
06:59
012 CodeChallenge Effects of mini-batch size.mp4
11:57
08 – Overfitting and cross-validation
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001 What is overfitting and is it as bad as they say.mp4
12:28
002 Cross-validation.mp4
17:13
003 Generalization.mp4
06:08
004 Cross-validation — manual separation.mp4
12:39
005 Cross-validation — scikitlearn.mp4
21:01
006 Cross-validation — DataLoader.mp4
20:27
007 Splitting data into train, devset, test.mp4
09:45
008 Cross-validation on regression.mp4
08:09
07 – ANNs (Artificial Neural Networks)
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011 Linear solutions to linear problems.mp4
08:14
021 Reflection Are DL models understandable yet.mp4
08:26
019 CodeChallenge convert sequential to class.mp4
06:37
018 Model depth vs. breadth.mp4
20:31
017 Defining models using sequential vs. class.mp4
13:17
016 Depth vs. breadth number of parameters.mp4
17:25
015 Comparing the number of hidden units.mp4
09:59
014 CodeChallenge more qwerties!.mp4
11:56
013 Multi-output ANN (iris dataset).mp4
26:59
012 Why multilayer linear models don’t exist.mp4
06:20
001 The perceptron and ANN architecture.mp4
19:50
010 Multilayer ANN.mp4
19:51
009 Learning rates comparison.mp4
23:46
008 ANN for classifying qwerties.mp4
22:22
007 CodeChallenge manipulate regression slopes.mp4
18:58
006 ANN for regression.mp4
24:09
005 ANN math part 3 (backprop).mp4
12:10
004 ANN math part 2 (errors, loss, cost).mp4
10:54
003 ANN math part 1 (forward prop).mp4
16:22
002 A geometric view of ANNs.mp4
13:38
06 – Gradient descent
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001 Overview of gradient descent.mp4
14:15
002 What about local minima.mp4
11:56
003 Gradient descent in 1D.mp4
17:11
004 CodeChallenge unfortunate starting value.mp4
11:30
005 Gradient descent in 2D.mp4
14:48
006 CodeChallenge 2D gradient ascent.mp4
05:16
007 Parametric experiments on g.d.mp4
18:56
008 CodeChallenge fixed vs. dynamic learning rate.mp4
15:33
009 Vanishing and exploding gradients.mp4
06:04
010 Tangent Notebook revision history.mp4
01:52
05 – Math, numpy, PyTorch
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011 Entropy and cross-entropy.mp4
18:18
019 Derivatives product and chain rules.mp4
10:00
018 Derivatives find minima.mp4
08:32
017 Derivatives intuition and polynomials.mp4
16:38
016 The t-test.mp4
13:57
015 Reproducible randomness via seeding.mp4
08:37
014 Random sampling and sampling variability.mp4
11:18
013 Mean and variance.mp4
15:34
012 Minmax and argminargmax.mp4
12:47
002 Introduction to this section.mp4
02:06
010 Logarithms.mp4
08:26
009 Softmax.mp4
19:26
008 Matrix multiplication.mp4
15:27
007 OMG it’s the dot product!.mp4
09:45
006 Vector and matrix transpose.mp4
06:58
005 Converting reality to numbers.mp4
06:33
004 Terms and datatypes in math and computers.mp4
07:05
003 Spectral theories in mathematics.mp4
09:15
04 – About the Python tutorial
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001 Should you watch the Python tutorial.mp4
04:25
03 – Concepts in deep learning
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001 What is an artificial neural network.mp4
16:02
002 How models learn.mp4
12:26
003 The role of DL in science and knowledge.mp4
16:43
004 Running experiments to understand DL.mp4
13:03
005 Are artificial neurons like biological neurons.mp4
17:49
02 – Download all course materials
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001 Downloading and using the code.mp4
06:29
002 My policy on code-sharing.mp4
01:38
[Last updated 8/2024] A deep understanding of deep learning (with Python intro) (Udemy – Vietsub and Engsub)
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