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Udemy - A deep understanding of deep learning (with Python intro) 7-2023
magnet:?xt=urn:btih:ca1ecaffafe6a30afa413d2399728a0d1cd7a426&dn=Udemy - A deep understanding of deep learning (with Python intro) 7-2023
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ca1ecaffafe6a30afa413d2399728a0d1cd7a426
infohash:
262
文件数量
16.03 GB
文件大小
2024-2-15 03:15
创建日期
2024-5-18 07:44
最后访问
相关分词
Udemy
-
A
deep
understanding
of
deep
learning
with
Python
intro
7-2023
19 - Understand and design CNNs/005 Examine feature map activations.mp4 251.42 MB
01 - Introduction/001 How to learn from this course.mp4 54.97 MB
01 - Introduction/002 Using Udemy like a pro.mp4 25.66 MB
02 - Download all course materials/001 Downloading and using the code.mp4 33.71 MB
02 - Download all course materials/002 My policy on code-sharing.mp4 3.88 MB
03 - Concepts in deep learning/001 What is an artificial neural network.mp4 29.4 MB
03 - Concepts in deep learning/002 How models learn.mp4 35.36 MB
03 - Concepts in deep learning/003 The role of DL in science and knowledge.mp4 87.75 MB
03 - Concepts in deep learning/004 Running experiments to understand DL.mp4 74.84 MB
03 - Concepts in deep learning/005 Are artificial neurons like biological neurons.mp4 56.29 MB
04 - About the Python tutorial/001 Should you watch the Python tutorial.mp4 9.38 MB
05 - Math, numpy, PyTorch/002 Introduction to this section.mp4 4.45 MB
05 - Math, numpy, PyTorch/003 Spectral theories in mathematics.mp4 43.9 MB
05 - Math, numpy, PyTorch/004 Terms and datatypes in math and computers.mp4 15.83 MB
05 - Math, numpy, PyTorch/005 Converting reality to numbers.mp4 13.44 MB
05 - Math, numpy, PyTorch/006 Vector and matrix transpose.mp4 17.83 MB
05 - Math, numpy, PyTorch/007 OMG it's the dot product!.mp4 19.84 MB
05 - Math, numpy, PyTorch/008 Matrix multiplication.mp4 45.49 MB
05 - Math, numpy, PyTorch/009 Softmax.mp4 70.21 MB
05 - Math, numpy, PyTorch/010 Logarithms.mp4 20.84 MB
05 - Math, numpy, PyTorch/011 Entropy and cross-entropy.mp4 58.76 MB
05 - Math, numpy, PyTorch/012 Minmax and argminargmax.mp4 45.66 MB
05 - Math, numpy, PyTorch/013 Mean and variance.mp4 32.91 MB
05 - Math, numpy, PyTorch/014 Random sampling and sampling variability.mp4 41.27 MB
05 - Math, numpy, PyTorch/015 Reproducible randomness via seeding.mp4 49.13 MB
05 - Math, numpy, PyTorch/016 The t-test.mp4 59.68 MB
05 - Math, numpy, PyTorch/017 Derivatives intuition and polynomials.mp4 32.09 MB
05 - Math, numpy, PyTorch/018 Derivatives find minima.mp4 18.65 MB
05 - Math, numpy, PyTorch/019 Derivatives product and chain rules.mp4 25.85 MB
06 - Gradient descent/001 Overview of gradient descent.mp4 40.06 MB
06 - Gradient descent/002 What about local minima.mp4 25.64 MB
06 - Gradient descent/003 Gradient descent in 1D.mp4 87.82 MB
06 - Gradient descent/004 CodeChallenge unfortunate starting value.mp4 57.01 MB
06 - Gradient descent/005 Gradient descent in 2D.mp4 96.38 MB
06 - Gradient descent/006 CodeChallenge 2D gradient ascent.mp4 27.84 MB
06 - Gradient descent/007 Parametric experiments on g.d.mp4 98.75 MB
06 - Gradient descent/008 CodeChallenge fixed vs. dynamic learning rate.mp4 84.02 MB
06 - Gradient descent/009 Vanishing and exploding gradients.mp4 22.33 MB
06 - Gradient descent/010 Tangent Notebook revision history.mp4 14.79 MB
07 - ANNs (Artificial Neural Networks)/001 The perceptron and ANN architecture.mp4 37.14 MB
07 - ANNs (Artificial Neural Networks)/002 A geometric view of ANNs.mp4 29.84 MB
07 - ANNs (Artificial Neural Networks)/003 ANN math part 1 (forward prop).mp4 32.79 MB
07 - ANNs (Artificial Neural Networks)/004 ANN math part 2 (errors, loss, cost).mp4 37.33 MB
07 - ANNs (Artificial Neural Networks)/005 ANN math part 3 (backprop).mp4 27.97 MB
07 - ANNs (Artificial Neural Networks)/006 ANN for regression.mp4 74.2 MB
07 - ANNs (Artificial Neural Networks)/007 CodeChallenge manipulate regression slopes.mp4 101.06 MB
07 - ANNs (Artificial Neural Networks)/008 ANN for classifying qwerties.mp4 130.39 MB
07 - ANNs (Artificial Neural Networks)/009 Learning rates comparison.mp4 168.64 MB
07 - ANNs (Artificial Neural Networks)/010 Multilayer ANN.mp4 105.28 MB
07 - ANNs (Artificial Neural Networks)/011 Linear solutions to linear problems.mp4 36.76 MB
07 - ANNs (Artificial Neural Networks)/012 Why multilayer linear models don't exist.mp4 19.28 MB
07 - ANNs (Artificial Neural Networks)/013 Multi-output ANN (iris dataset).mp4 142.02 MB
07 - ANNs (Artificial Neural Networks)/014 CodeChallenge more qwerties!.mp4 81.86 MB
07 - ANNs (Artificial Neural Networks)/015 Comparing the number of hidden units.mp4 31.83 MB
07 - ANNs (Artificial Neural Networks)/016 Depth vs. breadth number of parameters.mp4 97.7 MB
07 - ANNs (Artificial Neural Networks)/017 Defining models using sequential vs. class.mp4 65.76 MB
07 - ANNs (Artificial Neural Networks)/018 Model depth vs. breadth.mp4 114.95 MB
07 - ANNs (Artificial Neural Networks)/019 CodeChallenge convert sequential to class.mp4 36.5 MB
07 - ANNs (Artificial Neural Networks)/021 Reflection Are DL models understandable yet.mp4 51.72 MB
08 - Overfitting and cross-validation/001 What is overfitting and is it as bad as they say.mp4 54.3 MB
08 - Overfitting and cross-validation/002 Cross-validation.mp4 49.06 MB
08 - Overfitting and cross-validation/003 Generalization.mp4 13.26 MB
08 - Overfitting and cross-validation/004 Cross-validation -- manual separation.mp4 70.36 MB
08 - Overfitting and cross-validation/005 Cross-validation -- scikitlearn.mp4 105.85 MB
08 - Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4 121.26 MB
08 - Overfitting and cross-validation/007 Splitting data into train, devset, test.mp4 56.26 MB
08 - Overfitting and cross-validation/008 Cross-validation on regression.mp4 26.33 MB
09 - Regularization/001 Regularization Concept and methods.mp4 61.53 MB
09 - Regularization/002 train() and eval() modes.mp4 15.67 MB
09 - Regularization/003 Dropout regularization.mp4 103.65 MB
09 - Regularization/004 Dropout regularization in practice.mp4 130.74 MB
09 - Regularization/005 Dropout example 2.mp4 38.12 MB
09 - Regularization/006 Weight regularization (L1L2) math.mp4 49.29 MB
09 - Regularization/007 L2 regularization in practice.mp4 78.5 MB
09 - Regularization/008 L1 regularization in practice.mp4 70.93 MB
09 - Regularization/009 Training in mini-batches.mp4 24.13 MB
09 - Regularization/010 Batch training in action.mp4 76.4 MB
09 - Regularization/011 The importance of equal batch sizes.mp4 51.33 MB
09 - Regularization/012 CodeChallenge Effects of mini-batch size.mp4 83.29 MB
10 - Metaparameters (activations, optimizers)/001 What are metaparameters.mp4 12.39 MB
10 - Metaparameters (activations, optimizers)/002 The wine quality dataset.mp4 124.62 MB
10 - Metaparameters (activations, optimizers)/003 CodeChallenge Minibatch size in the wine dataset.mp4 103.54 MB
10 - Metaparameters (activations, optimizers)/004 Data normalization.mp4 45.4 MB
10 - Metaparameters (activations, optimizers)/005 The importance of data normalization.mp4 47.77 MB
10 - Metaparameters (activations, optimizers)/006 Batch normalization.mp4 39.12 MB
10 - Metaparameters (activations, optimizers)/007 Batch normalization in practice.mp4 45.22 MB
10 - Metaparameters (activations, optimizers)/008 CodeChallenge Batch-normalize the qwerties.mp4 39.88 MB
10 - Metaparameters (activations, optimizers)/009 Activation functions.mp4 84.92 MB
10 - Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.mp4 67.03 MB
10 - Metaparameters (activations, optimizers)/011 Activation functions comparison.mp4 70.58 MB
10 - Metaparameters (activations, optimizers)/012 CodeChallenge Compare relu variants.mp4 63.97 MB
10 - Metaparameters (activations, optimizers)/013 CodeChallenge Predict sugar.mp4 89.36 MB
10 - Metaparameters (activations, optimizers)/014 Loss functions.mp4 68.57 MB
10 - Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.mp4 101.71 MB
10 - Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs.mp4 71.9 MB
10 - Metaparameters (activations, optimizers)/017 Optimizers (minibatch, momentum).mp4 42.22 MB
10 - Metaparameters (activations, optimizers)/018 SGD with momentum.mp4 62.1 MB
10 - Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam).mp4 38.02 MB
10 - Metaparameters (activations, optimizers)/020 Optimizers comparison.mp4 61.81 MB
10 - Metaparameters (activations, optimizers)/021 CodeChallenge Optimizers and... something.mp4 36.55 MB
10 - Metaparameters (activations, optimizers)/022 CodeChallenge Adam with L2 regularization.mp4 39.95 MB
10 - Metaparameters (activations, optimizers)/023 Learning rate decay.mp4 69.09 MB
10 - Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.mp4 25.54 MB
11 - FFNs (Feed-Forward Networks)/001 What are fully-connected and feedforward networks.mp4 12.65 MB
11 - FFNs (Feed-Forward Networks)/002 The MNIST dataset.mp4 88.68 MB
11 - FFNs (Feed-Forward Networks)/003 FFN to classify digits.mp4 117.29 MB
11 - FFNs (Feed-Forward Networks)/004 CodeChallenge Binarized MNIST images.mp4 28.68 MB
11 - FFNs (Feed-Forward Networks)/005 CodeChallenge Data normalization.mp4 70.98 MB
11 - FFNs (Feed-Forward Networks)/006 Distributions of weights pre- and post-learning.mp4 89.65 MB
11 - FFNs (Feed-Forward Networks)/007 CodeChallenge MNIST and breadth vs. depth.mp4 90.37 MB
11 - FFNs (Feed-Forward Networks)/008 CodeChallenge Optimizers and MNIST.mp4 33.21 MB
11 - FFNs (Feed-Forward Networks)/009 Scrambled MNIST.mp4 60.17 MB
11 - FFNs (Feed-Forward Networks)/010 Shifted MNIST.mp4 57.33 MB
11 - FFNs (Feed-Forward Networks)/011 CodeChallenge The mystery of the missing 7.mp4 53.42 MB
11 - FFNs (Feed-Forward Networks)/012 Universal approximation theorem.mp4 24.23 MB
12 - More on data/001 Anatomy of a torch dataset and dataloader.mp4 100.77 MB
12 - More on data/002 Data size and network size.mp4 97.23 MB
12 - More on data/003 CodeChallenge unbalanced data.mp4 117.83 MB
12 - More on data/004 What to do about unbalanced designs.mp4 18.83 MB
12 - More on data/005 Data oversampling in MNIST.mp4 89.28 MB
12 - More on data/006 Data noise augmentation (with devset+test).mp4 76.14 MB
12 - More on data/007 Data feature augmentation.mp4 114.34 MB
12 - More on data/008 Getting data into colab.mp4 31.93 MB
12 - More on data/009 Save and load trained models.mp4 38.72 MB
12 - More on data/010 Save the best-performing model.mp4 90.08 MB
12 - More on data/011 Where to find online datasets.mp4 28.46 MB
13 - Measuring model performance/001 Two perspectives of the world.mp4 18.86 MB
13 - Measuring model performance/002 Accuracy, precision, recall, F1.mp4 63.72 MB
13 - Measuring model performance/003 APRF in code.mp4 38.19 MB
13 - Measuring model performance/004 APRF example 1 wine quality.mp4 103 MB
13 - Measuring model performance/005 APRF example 2 MNIST.mp4 94.47 MB
13 - Measuring model performance/006 CodeChallenge MNIST with unequal groups.mp4 59.04 MB
13 - Measuring model performance/007 Computation time.mp4 70.49 MB
13 - Measuring model performance/008 Better performance in test than train.mp4 18.24 MB
14 - FFN milestone projects/001 Project 1 A gratuitously complex adding machine.mp4 25.95 MB
14 - FFN milestone projects/002 Project 1 My solution.mp4 69.82 MB
14 - FFN milestone projects/003 Project 2 Predicting heart disease.mp4 23.67 MB
14 - FFN milestone projects/004 Project 2 My solution.mp4 155.73 MB
14 - FFN milestone projects/005 Project 3 FFN for missing data interpolation.mp4 19.61 MB
14 - FFN milestone projects/006 Project 3 My solution.mp4 52.94 MB
15 - Weight inits and investigations/001 Explanation of weight matrix sizes.mp4 59.62 MB
15 - Weight inits and investigations/002 A surprising demo of weight initializations.mp4 85.9 MB
15 - Weight inits and investigations/003 Theory Why and how to initialize weights.mp4 73.64 MB
15 - Weight inits and investigations/004 CodeChallenge Weight variance inits.mp4 72.9 MB
15 - Weight inits and investigations/005 Xavier and Kaiming initializations.mp4 96.29 MB
15 - Weight inits and investigations/006 CodeChallenge Xavier vs. Kaiming.mp4 109.44 MB
15 - Weight inits and investigations/007 CodeChallenge Identically random weights.mp4 65.27 MB
15 - Weight inits and investigations/008 Freezing weights during learning.mp4 88.26 MB
15 - Weight inits and investigations/009 Learning-related changes in weights.mp4 107.96 MB
15 - Weight inits and investigations/010 Use default inits or apply your own.mp4 10.94 MB
16 - Autoencoders/001 What are autoencoders and what do they do.mp4 21.2 MB
16 - Autoencoders/002 Denoising MNIST.mp4 86.5 MB
16 - Autoencoders/003 CodeChallenge How many units.mp4 100.02 MB
16 - Autoencoders/004 AEs for occlusion.mp4 138.2 MB
16 - Autoencoders/005 The latent code of MNIST.mp4 117.79 MB
16 - Autoencoders/006 Autoencoder with tied weights.mp4 131.51 MB
17 - Running models on a GPU/001 What is a GPU and why use it.mp4 50.35 MB
17 - Running models on a GPU/002 Implementation.mp4 39.7 MB
17 - Running models on a GPU/003 CodeChallenge Run an experiment on the GPU.mp4 36.94 MB
18 - Convolution and transformations/001 Convolution concepts.mp4 88.41 MB
18 - Convolution and transformations/002 Feature maps and convolution kernels.mp4 53.56 MB
18 - Convolution and transformations/003 Convolution in code.mp4 165.71 MB
18 - Convolution and transformations/004 Convolution parameters (stride, padding).mp4 27.36 MB
18 - Convolution and transformations/005 The Conv2 class in PyTorch.mp4 75.51 MB
18 - Convolution and transformations/006 CodeChallenge Choose the parameters.mp4 18.97 MB
18 - Convolution and transformations/007 Transpose convolution.mp4 69.38 MB
18 - Convolution and transformations/008 Maxmean pooling.mp4 51.24 MB
18 - Convolution and transformations/009 Pooling in PyTorch.mp4 44.24 MB
18 - Convolution and transformations/010 To pool or to stride.mp4 49.22 MB
18 - Convolution and transformations/011 Image transforms.mp4 124.68 MB
18 - Convolution and transformations/012 Creating and using custom DataLoaders.mp4 102.39 MB
19 - Understand and design CNNs/001 The canonical CNN architecture.mp4 23.82 MB
19 - Understand and design CNNs/002 CNN to classify MNIST digits.mp4 144.84 MB
19 - Understand and design CNNs/003 CNN on shifted MNIST.mp4 41.39 MB
19 - Understand and design CNNs/004 Classify Gaussian blurs.mp4 176.04 MB
19 - Understand and design CNNs/006 CodeChallenge Softcode internal parameters.mp4 113.72 MB
19 - Understand and design CNNs/007 CodeChallenge How wide the FC.mp4 90.56 MB
19 - Understand and design CNNs/008 Do autoencoders clean Gaussians.mp4 128.84 MB
19 - Understand and design CNNs/009 CodeChallenge AEs and occluded Gaussians.mp4 78.57 MB
19 - Understand and design CNNs/010 CodeChallenge Custom loss functions.mp4 98.69 MB
19 - Understand and design CNNs/011 Discover the Gaussian parameters.mp4 136.65 MB
19 - Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4 143.88 MB
19 - Understand and design CNNs/013 Dropout in CNNs.mp4 70.64 MB
19 - Understand and design CNNs/014 CodeChallenge How low can you go.mp4 39.15 MB
19 - Understand and design CNNs/015 CodeChallenge Varying number of channels.mp4 67.29 MB
19 - Understand and design CNNs/016 So many possibilities! How to create a CNN.mp4 9.25 MB
20 - CNN milestone projects/001 Project 1 Import and classify CIFAR10.mp4 36.58 MB
20 - CNN milestone projects/002 Project 1 My solution.mp4 81.26 MB
20 - CNN milestone projects/003 Project 2 CIFAR-autoencoder.mp4 29.25 MB
20 - CNN milestone projects/004 Project 3 FMNIST.mp4 19.42 MB
20 - CNN milestone projects/005 Project 4 Psychometric functions in CNNs.mp4 76.46 MB
21 - Transfer learning/001 Transfer learning What, why, and when.mp4 40.48 MB
21 - Transfer learning/002 Transfer learning MNIST - FMNIST.mp4 78.22 MB
21 - Transfer learning/003 CodeChallenge letters to numbers.mp4 84.89 MB
21 - Transfer learning/004 Famous CNN architectures.mp4 22.26 MB
21 - Transfer learning/005 Transfer learning with ResNet-18.mp4 128.32 MB
21 - Transfer learning/006 CodeChallenge VGG-16.mp4 20.28 MB
21 - Transfer learning/007 Pretraining with autoencoders.mp4 135.97 MB
21 - Transfer learning/008 CIFAR10 with autoencoder-pretrained model.mp4 108.86 MB
22 - Style transfer/001 What is style transfer and how does it work.mp4 16.83 MB
22 - Style transfer/002 The Gram matrix (feature activation covariance).mp4 66.49 MB
22 - Style transfer/003 The style transfer algorithm.mp4 26.71 MB
22 - Style transfer/004 Transferring the screaming bathtub.mp4 210.35 MB
22 - Style transfer/005 CodeChallenge Style transfer with AlexNet.mp4 50.92 MB
23 - Generative adversarial networks/001 GAN What, why, and how.mp4 38.68 MB
23 - Generative adversarial networks/002 Linear GAN with MNIST.mp4 121.55 MB
23 - Generative adversarial networks/003 CodeChallenge Linear GAN with FMNIST.mp4 58.54 MB
23 - Generative adversarial networks/004 CNN GAN with Gaussians.mp4 131.44 MB
23 - Generative adversarial networks/005 CodeChallenge Gaussians with fewer layers.mp4 51.28 MB
23 - Generative adversarial networks/006 CNN GAN with FMNIST.mp4 46.94 MB
23 - Generative adversarial networks/007 CodeChallenge CNN GAN with CIFAR.mp4 43.2 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/001 Leveraging sequences in deep learning.mp4 63.92 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/002 How RNNs work.mp4 32.64 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/003 The RNN class in PyTorch.mp4 89.64 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/004 Predicting alternating sequences.mp4 153.76 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/005 CodeChallenge sine wave extrapolation.mp4 166.64 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/006 More on RNNs Hidden states, embeddings.mp4 94.25 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/007 GRU and LSTM.mp4 100.32 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/008 The LSTM and GRU classes.mp4 84.32 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/009 Lorem ipsum.mp4 141.62 MB
25 - Ethics of deep learning/001 Will AI save us or destroy us.mp4 23.82 MB
25 - Ethics of deep learning/002 Example case studies.mp4 38.4 MB
25 - Ethics of deep learning/003 Some other possible ethical scenarios.mp4 58.3 MB
25 - Ethics of deep learning/004 Will deep learning take our jobs.mp4 33.82 MB
25 - Ethics of deep learning/005 Accountability and making ethical AI.mp4 61.2 MB
26 - Where to go from here/001 How to learn topic _X_ in deep learning.mp4 17.45 MB
26 - Where to go from here/002 How to read academic DL papers.mp4 137.31 MB
27 - Python intro Data types/001 How to learn from the Python tutorial.mp4 12.27 MB
27 - Python intro Data types/002 Variables.mp4 41.07 MB
27 - Python intro Data types/003 Math and printing.mp4 35.93 MB
27 - Python intro Data types/004 Lists (1 of 2).mp4 24.85 MB
27 - Python intro Data types/005 Lists (2 of 2).mp4 23.55 MB
27 - Python intro Data types/006 Tuples.mp4 15.4 MB
27 - Python intro Data types/007 Booleans.mp4 46.04 MB
27 - Python intro Data types/008 Dictionaries.mp4 23.24 MB
28 - Python intro Indexing, slicing/001 Indexing.mp4 23.41 MB
28 - Python intro Indexing, slicing/002 Slicing.mp4 29.01 MB
29 - Python intro Functions/001 Inputs and outputs.mp4 13.45 MB
29 - Python intro Functions/002 Python libraries (numpy).mp4 27.96 MB
29 - Python intro Functions/003 Python libraries (pandas).mp4 60.86 MB
29 - Python intro Functions/004 Getting help on functions.mp4 24.8 MB
29 - Python intro Functions/005 Creating functions.mp4 40.14 MB
29 - Python intro Functions/006 Global and local variable scopes.mp4 39.19 MB
29 - Python intro Functions/007 Copies and referents of variables.mp4 10.64 MB
29 - Python intro Functions/008 Classes and object-oriented programming.mp4 60.61 MB
30 - Python intro Flow control/001 If-else statements.mp4 30.16 MB
30 - Python intro Flow control/002 If-else statements, part 2.mp4 53.74 MB
30 - Python intro Flow control/003 For loops.mp4 44.7 MB
30 - Python intro Flow control/004 Enumerate and zip.mp4 58.59 MB
30 - Python intro Flow control/005 Continue.mp4 14.34 MB
30 - Python intro Flow control/006 Initializing variables.mp4 46.47 MB
30 - Python intro Flow control/007 Single-line loops (list comprehension).mp4 44.09 MB
30 - Python intro Flow control/008 while loops.mp4 48.16 MB
30 - Python intro Flow control/009 Broadcasting in numpy.mp4 37.14 MB
30 - Python intro Flow control/010 Function error checking and handling.mp4 76.98 MB
31 - Python intro Text and plots/001 Printing and string interpolation.mp4 47.18 MB
31 - Python intro Text and plots/002 Plotting dots and lines.mp4 28.9 MB
31 - Python intro Text and plots/003 Subplot geometry.mp4 48.72 MB
31 - Python intro Text and plots/004 Making the graphs look nicer.mp4 59.02 MB
31 - Python intro Text and plots/005 Seaborn.mp4 34.31 MB
31 - Python intro Text and plots/006 Images.mp4 71.03 MB
31 - Python intro Text and plots/007 Export plots in low and high resolution.mp4 37.38 MB
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