[GigaCourse.Com] Udemy - A deep understanding of deep learning (with Python intro)磁力链接_[GigaCourse.Com] Udemy - A deep understanding of deep learning (with Python intro)bt种子下载_[GigaCourse.Com] Udemy - A deep understanding of deep learning (with Python intro)迅雷下载

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

文件类型	收录时间	最后活跃	资源热度	文件大小	文件数量
视频	2022-8-26 21:50	2024-12-23 22:06	211	21.97 GB	262

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文件列表

01 - Introduction/001 How to learn from this course.mp454.97MB
01 - Introduction/002 Using Udemy like a pro.mp454.37MB
02 - Download all course materials/001 Downloading and using the code.mp445.65MB
02 - Download all course materials/002 My policy on code-sharing.mp410.24MB
03 - Concepts in deep learning/001 What is an artificial neural network.mp465.38MB
03 - Concepts in deep learning/002 How models learn.mp472.79MB
03 - Concepts in deep learning/003 The role of DL in science and knowledge.mp434.76MB
03 - Concepts in deep learning/004 Running experiments to understand DL.mp474.84MB
03 - Concepts in deep learning/005 Are artificial neurons like biological neurons.mp4114.65MB
04 - About the Python tutorial/001 Should you watch the Python tutorial.mp423.77MB
05 - Math, numpy, PyTorch/002 Introduction to this section.mp411.12MB
05 - Math, numpy, PyTorch/003 Spectral theories in mathematics.mp451.06MB
05 - Math, numpy, PyTorch/004 Terms and datatypes in math and computers.mp438.08MB
05 - Math, numpy, PyTorch/005 Converting reality to numbers.mp433.21MB
05 - Math, numpy, PyTorch/006 Vector and matrix transpose.mp437.66MB
05 - Math, numpy, PyTorch/007 OMG it's the dot product!.mp450.11MB
05 - Math, numpy, PyTorch/008 Matrix multiplication.mp485.67MB
05 - Math, numpy, PyTorch/009 Softmax.mp495.96MB
05 - Math, numpy, PyTorch/010 Logarithms.mp443.88MB
05 - Math, numpy, PyTorch/011 Entropy and cross-entropy.mp4106MB
05 - Math, numpy, PyTorch/012 Minmax and argminargmax.mp488.21MB
05 - Math, numpy, PyTorch/013 Mean and variance.mp481.42MB
05 - Math, numpy, PyTorch/014 Random sampling and sampling variability.mp485.42MB
05 - Math, numpy, PyTorch/015 Reproducible randomness via seeding.mp469.7MB
05 - Math, numpy, PyTorch/016 The t-test.mp481.36MB
05 - Math, numpy, PyTorch/017 Derivatives intuition and polynomials.mp480.3MB
05 - Math, numpy, PyTorch/018 Derivatives find minima.mp445.47MB
05 - Math, numpy, PyTorch/019 Derivatives product and chain rules.mp455.63MB
06 - Gradient descent/001 Overview of gradient descent.mp468.44MB
06 - Gradient descent/002 What about local minima.mp467.08MB
06 - Gradient descent/003 Gradient descent in 1D.mp4119.29MB
06 - Gradient descent/004 CodeChallenge unfortunate starting value.mp477.09MB
06 - Gradient descent/005 Gradient descent in 2D.mp496.38MB
06 - Gradient descent/006 CodeChallenge 2D gradient ascent.mp439.36MB
06 - Gradient descent/007 Parametric experiments on g.d.mp4135.61MB
06 - Gradient descent/008 CodeChallenge fixed vs. dynamic learning rate.mp4113.6MB
06 - Gradient descent/009 Vanishing and exploding gradients.mp430.24MB
06 - Gradient descent/010 Tangent Notebook revision history.mp49.88MB
07 - ANNs (Artificial Neural Networks)/001 The perceptron and ANN architecture.mp485.84MB
07 - ANNs (Artificial Neural Networks)/002 A geometric view of ANNs.mp470.88MB
07 - ANNs (Artificial Neural Networks)/003 ANN math part 1 (forward prop).mp473.12MB
07 - ANNs (Artificial Neural Networks)/004 ANN math part 2 (errors, loss, cost).mp448.47MB
07 - ANNs (Artificial Neural Networks)/005 ANN math part 3 (backprop).mp452.89MB
07 - ANNs (Artificial Neural Networks)/006 ANN for regression.mp4135.5MB
07 - ANNs (Artificial Neural Networks)/007 CodeChallenge manipulate regression slopes.mp4139.12MB
07 - ANNs (Artificial Neural Networks)/008 ANN for classifying qwerties.mp4151.12MB
07 - ANNs (Artificial Neural Networks)/009 Learning rates comparison.mp4168.64MB
07 - ANNs (Artificial Neural Networks)/010 Multilayer ANN.mp4144.7MB
07 - ANNs (Artificial Neural Networks)/011 Linear solutions to linear problems.mp450.37MB
07 - ANNs (Artificial Neural Networks)/012 Why multilayer linear models don't exist.mp426.46MB
07 - ANNs (Artificial Neural Networks)/013 Multi-output ANN (iris dataset).mp4186.77MB
07 - ANNs (Artificial Neural Networks)/014 CodeChallenge more qwerties!.mp495.1MB
07 - ANNs (Artificial Neural Networks)/015 Comparing the number of hidden units.mp471.15MB
07 - ANNs (Artificial Neural Networks)/016 Depth vs. breadth number of parameters.mp4132.07MB
07 - ANNs (Artificial Neural Networks)/017 Defining models using sequential vs. class.mp489.48MB
07 - ANNs (Artificial Neural Networks)/018 Model depth vs. breadth.mp4158.91MB
07 - ANNs (Artificial Neural Networks)/019 CodeChallenge convert sequential to class.mp451.44MB
07 - ANNs (Artificial Neural Networks)/021 Reflection Are DL models understandable yet.mp458.59MB
08 - Overfitting and cross-validation/001 What is overfitting and is it as bad as they say.mp473.13MB
08 - Overfitting and cross-validation/002 Cross-validation.mp488.19MB
08 - Overfitting and cross-validation/003 Generalization.mp432.44MB
08 - Overfitting and cross-validation/004 Cross-validation -- manual separation.mp498.3MB
08 - Overfitting and cross-validation/005 Cross-validation -- scikitlearn.mp4142.88MB
08 - Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4172.32MB
08 - Overfitting and cross-validation/007 Splitting data into train, devset, test.mp479.21MB
08 - Overfitting and cross-validation/008 Cross-validation on regression.mp460.35MB
09 - Regularization/001 Regularization Concept and methods.mp480.05MB
09 - Regularization/002 train() and eval() modes.mp438.34MB
09 - Regularization/003 Dropout regularization.mp4138.39MB
09 - Regularization/004 Dropout regularization in practice.mp4183.23MB
09 - Regularization/005 Dropout example 2.mp453.87MB
09 - Regularization/006 Weight regularization (L1L2) math.mp485.41MB
09 - Regularization/007 L2 regularization in practice.mp4110.47MB
09 - Regularization/008 L1 regularization in practice.mp499.44MB
09 - Regularization/009 Training in mini-batches.mp462.12MB
09 - Regularization/010 Batch training in action.mp489.1MB
09 - Regularization/011 The importance of equal batch sizes.mp460.11MB
09 - Regularization/012 CodeChallenge Effects of mini-batch size.mp495.42MB
10 - Metaparameters (activations, optimizers)/001 What are metaparameters.mp432.7MB
10 - Metaparameters (activations, optimizers)/002 The wine quality dataset.mp4143.5MB
10 - Metaparameters (activations, optimizers)/003 CodeChallenge Minibatch size in the wine dataset.mp4118.79MB
10 - Metaparameters (activations, optimizers)/004 Data normalization.mp459.81MB
10 - Metaparameters (activations, optimizers)/005 The importance of data normalization.mp464.65MB
10 - Metaparameters (activations, optimizers)/006 Batch normalization.mp476.81MB
10 - Metaparameters (activations, optimizers)/007 Batch normalization in practice.mp461.76MB
10 - Metaparameters (activations, optimizers)/008 CodeChallenge Batch-normalize the qwerties.mp441.43MB
10 - Metaparameters (activations, optimizers)/009 Activation functions.mp497.03MB
10 - Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.mp491.46MB
10 - Metaparameters (activations, optimizers)/011 Activation functions comparison.mp473.9MB
10 - Metaparameters (activations, optimizers)/012 CodeChallenge Compare relu variants.mp463.97MB
10 - Metaparameters (activations, optimizers)/013 CodeChallenge Predict sugar.mp4122.1MB
10 - Metaparameters (activations, optimizers)/014 Loss functions.mp490.3MB
10 - Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.mp4138.1MB
10 - Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs.mp499.8MB
10 - Metaparameters (activations, optimizers)/017 Optimizers (minibatch, momentum).mp498.07MB
10 - Metaparameters (activations, optimizers)/018 SGD with momentum.mp462.1MB
10 - Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam).mp476.73MB
10 - Metaparameters (activations, optimizers)/020 Optimizers comparison.mp486.88MB
10 - Metaparameters (activations, optimizers)/021 CodeChallenge Optimizers and... something.mp449.77MB
10 - Metaparameters (activations, optimizers)/022 CodeChallenge Adam with L2 regularization.mp453MB
10 - Metaparameters (activations, optimizers)/023 Learning rate decay.mp496.9MB
10 - Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.mp461.74MB
11 - FFNs (Feed-Forward Networks)/001 What are fully-connected and feedforward networks.mp425.53MB
11 - FFNs (Feed-Forward Networks)/002 The MNIST dataset.mp4101.38MB
11 - FFNs (Feed-Forward Networks)/003 FFN to classify digits.mp4161.85MB
11 - FFNs (Feed-Forward Networks)/004 CodeChallenge Binarized MNIST images.mp440.78MB
11 - FFNs (Feed-Forward Networks)/005 CodeChallenge Data normalization.mp496.25MB
11 - FFNs (Feed-Forward Networks)/006 Distributions of weights pre- and post-learning.mp4116.26MB
11 - FFNs (Feed-Forward Networks)/007 CodeChallenge MNIST and breadth vs. depth.mp495.21MB
11 - FFNs (Feed-Forward Networks)/008 CodeChallenge Optimizers and MNIST.mp446.26MB
11 - FFNs (Feed-Forward Networks)/009 Scrambled MNIST.mp460.17MB
11 - FFNs (Feed-Forward Networks)/010 Shifted MNIST.mp477.91MB
11 - FFNs (Feed-Forward Networks)/011 CodeChallenge The mystery of the missing 7.mp474.25MB
11 - FFNs (Feed-Forward Networks)/012 Universal approximation theorem.mp449.18MB
12 - More on data/001 Anatomy of a torch dataset and dataloader.mp4135.84MB
12 - More on data/002 Data size and network size.mp4135.67MB
12 - More on data/003 CodeChallenge unbalanced data.mp4166.26MB
12 - More on data/004 What to do about unbalanced designs.mp454.21MB
12 - More on data/005 Data oversampling in MNIST.mp4122.59MB
12 - More on data/006 Data noise augmentation (with devset+test).mp4106.09MB
12 - More on data/007 Data feature augmentation.mp4158.27MB
12 - More on data/008 Getting data into colab.mp443.75MB
12 - More on data/009 Save and load trained models.mp455.71MB
12 - More on data/010 Save the best-performing model.mp4126.5MB
12 - More on data/011 Where to find online datasets.mp441.7MB
13 - Measuring model performance/001 Two perspectives of the world.mp440.01MB
13 - Measuring model performance/002 Accuracy, precision, recall, F1.mp472.58MB
13 - Measuring model performance/003 APRF in code.mp451.79MB
13 - Measuring model performance/004 APRF example 1 wine quality.mp4107.35MB
13 - Measuring model performance/005 APRF example 2 MNIST.mp498.62MB
13 - Measuring model performance/006 CodeChallenge MNIST with unequal groups.mp425.07MB
13 - Measuring model performance/007 Computation time.mp481.73MB
13 - Measuring model performance/008 Better performance in test than train.mp444.83MB
14 - FFN milestone projects/001 Project 1 A gratuitously complex adding machine.mp448.55MB
14 - FFN milestone projects/002 Project 1 My solution.mp499.75MB
14 - FFN milestone projects/003 Project 2 Predicting heart disease.mp450.61MB
14 - FFN milestone projects/004 Project 2 My solution.mp4155.73MB
14 - FFN milestone projects/005 Project 3 FFN for missing data interpolation.mp445.39MB
14 - FFN milestone projects/006 Project 3 My solution.mp475.48MB
15 - Weight inits and investigations/001 Explanation of weight matrix sizes.mp468.98MB
15 - Weight inits and investigations/002 A surprising demo of weight initializations.mp4121.57MB
15 - Weight inits and investigations/003 Theory Why and how to initialize weights.mp479.41MB
15 - Weight inits and investigations/004 CodeChallenge Weight variance inits.mp4103.96MB
15 - Weight inits and investigations/005 Xavier and Kaiming initializations.mp4134.08MB
15 - Weight inits and investigations/006 CodeChallenge Xavier vs. Kaiming.mp4126.5MB
15 - Weight inits and investigations/007 CodeChallenge Identically random weights.mp488.17MB
15 - Weight inits and investigations/008 Freezing weights during learning.mp493.15MB
15 - Weight inits and investigations/009 Learning-related changes in weights.mp4146.78MB
15 - Weight inits and investigations/010 Use default inits or apply your own.mp428.05MB
16 - Autoencoders/001 What are autoencoders and what do they do.mp449.04MB
16 - Autoencoders/002 Denoising MNIST.mp4118.53MB
16 - Autoencoders/003 CodeChallenge How many units.mp4135.38MB
16 - Autoencoders/004 AEs for occlusion.mp4138.2MB
16 - Autoencoders/005 The latent code of MNIST.mp4161.81MB
16 - Autoencoders/006 Autoencoder with tied weights.mp4177.74MB
17 - Running models on a GPU/001 What is a GPU and why use it.mp488.73MB
17 - Running models on a GPU/002 Implementation.mp476.6MB
17 - Running models on a GPU/003 CodeChallenge Run an experiment on the GPU.mp452.99MB
18 - Convolution and transformations/001 Convolution concepts.mp497.99MB
18 - Convolution and transformations/002 Feature maps and convolution kernels.mp470.41MB
18 - Convolution and transformations/003 Convolution in code.mp4173.1MB
18 - Convolution and transformations/004 Convolution parameters (stride, padding).mp466.93MB
18 - Convolution and transformations/005 The Conv2 class in PyTorch.mp4100.19MB
18 - Convolution and transformations/006 CodeChallenge Choose the parameters.mp458.71MB
18 - Convolution and transformations/007 Transpose convolution.mp492.89MB
18 - Convolution and transformations/008 Maxmean pooling.mp489.07MB
18 - Convolution and transformations/009 Pooling in PyTorch.mp481.02MB
18 - Convolution and transformations/010 To pool or to stride.mp455.51MB
18 - Convolution and transformations/011 Image transforms.mp4129.9MB
18 - Convolution and transformations/012 Creating and using custom DataLoaders.mp4139.53MB
19 - Understand and design CNNs/001 The canonical CNN architecture.mp455.83MB
19 - Understand and design CNNs/002 CNN to classify MNIST digits.mp4200.33MB
19 - Understand and design CNNs/003 CNN on shifted MNIST.mp458.34MB
19 - Understand and design CNNs/004 Classify Gaussian blurs.mp4185.14MB
19 - Understand and design CNNs/005 Examine feature map activations.mp4260.56MB
19 - Understand and design CNNs/006 CodeChallenge Softcode internal parameters.mp4120.1MB
19 - Understand and design CNNs/007 CodeChallenge How wide the FC.mp494.08MB
19 - Understand and design CNNs/008 Do autoencoders clean Gaussians.mp4147.88MB
19 - Understand and design CNNs/009 CodeChallenge AEs and occluded Gaussians.mp428.58MB
19 - Understand and design CNNs/010 CodeChallenge Custom loss functions.mp4132.89MB
19 - Understand and design CNNs/011 Discover the Gaussian parameters.mp4136.65MB
19 - Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4201.31MB
19 - Understand and design CNNs/013 Dropout in CNNs.mp482.73MB
19 - Understand and design CNNs/014 CodeChallenge How low can you go.mp455.36MB
19 - Understand and design CNNs/015 CodeChallenge Varying number of channels.mp492.37MB
19 - Understand and design CNNs/016 So many possibilities! How to create a CNN.mp421.04MB
20 - CNN milestone projects/001 Project 1 Import and classify CIFAR10.mp448.36MB
20 - CNN milestone projects/002 Project 1 My solution.mp4118.6MB
20 - CNN milestone projects/003 Project 2 CIFAR-autoencoder.mp433.37MB
20 - CNN milestone projects/004 Project 3 FMNIST.mp426.45MB
20 - CNN milestone projects/005 Project 4 Psychometric functions in CNNs.mp476.27MB
21 - Transfer learning/001 Transfer learning What, why, and when.mp496.61MB
21 - Transfer learning/002 Transfer learning MNIST - FMNIST.mp490.35MB
21 - Transfer learning/003 CodeChallenge letters to numbers.mp4118.74MB
21 - Transfer learning/004 Famous CNN architectures.mp441.28MB
21 - Transfer learning/005 Transfer learning with ResNet-18.mp4148.46MB
21 - Transfer learning/006 CodeChallenge VGG-16.mp420.28MB
21 - Transfer learning/007 Pretraining with autoencoders.mp4156.58MB
21 - Transfer learning/008 CIFAR10 with autoencoder-pretrained model.mp4153.34MB
22 - Style transfer/001 What is style transfer and how does it work.mp440.57MB
22 - Style transfer/002 The Gram matrix (feature activation covariance).mp466.49MB
22 - Style transfer/003 The style transfer algorithm.mp467.31MB
22 - Style transfer/004 Transferring the screaming bathtub.mp4216.82MB
22 - Style transfer/005 CodeChallenge Style transfer with AlexNet.mp453.47MB
23 - Generative adversarial networks/001 GAN What, why, and how.mp489.74MB
23 - Generative adversarial networks/002 Linear GAN with MNIST.mp4169.9MB
23 - Generative adversarial networks/003 CodeChallenge Linear GAN with FMNIST.mp462.73MB
23 - Generative adversarial networks/004 CNN GAN with Gaussians.mp4135.7MB
23 - Generative adversarial networks/005 CodeChallenge Gaussians with fewer layers.mp453.06MB
23 - Generative adversarial networks/006 CNN GAN with FMNIST.mp454.58MB
23 - Generative adversarial networks/007 CodeChallenge CNN GAN with CIFAR.mp460.77MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/001 Leveraging sequences in deep learning.mp472.79MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/002 How RNNs work.mp474.85MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/003 The RNN class in PyTorch.mp4122.98MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/004 Predicting alternating sequences.mp4160.16MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/005 CodeChallenge sine wave extrapolation.mp4195.67MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/006 More on RNNs Hidden states, embeddings.mp429.04MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/007 GRU and LSTM.mp4129.66MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/008 The LSTM and GRU classes.mp4120.14MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/009 Lorem ipsum.mp4192.53MB
25 - Ethics of deep learning/001 Will AI save us or destroy us.mp465.92MB
25 - Ethics of deep learning/002 Example case studies.mp452.92MB
25 - Ethics of deep learning/003 Some other possible ethical scenarios.mp466.25MB
25 - Ethics of deep learning/004 Will deep learning take our jobs.mp475.14MB
25 - Ethics of deep learning/005 Accountability and making ethical AI.mp470.06MB
26 - Where to go from here/001 How to learn topic _X_ in deep learning.mp442.03MB
26 - Where to go from here/002 How to read academic DL papers.mp4141.85MB
27 - Python intro Data types/001 How to learn from the Python tutorial.mp421.97MB
27 - Python intro Data types/002 Variables.mp477.58MB
27 - Python intro Data types/003 Math and printing.mp478.5MB
27 - Python intro Data types/004 Lists (1 of 2).mp455.04MB
27 - Python intro Data types/005 Lists (2 of 2).mp446.69MB
27 - Python intro Data types/006 Tuples.mp435.75MB
27 - Python intro Data types/007 Booleans.mp476.83MB
27 - Python intro Data types/008 Dictionaries.mp450.67MB
28 - Python intro Indexing, slicing/001 Indexing.mp451.07MB
28 - Python intro Indexing, slicing/002 Slicing.mp448.45MB
29 - Python intro Functions/001 Inputs and outputs.mp429.49MB
29 - Python intro Functions/002 Python libraries (numpy).mp463.39MB
29 - Python intro Functions/003 Python libraries (pandas).mp481.19MB
29 - Python intro Functions/004 Getting help on functions.mp448.6MB
29 - Python intro Functions/005 Creating functions.mp488.43MB
29 - Python intro Functions/006 Global and local variable scopes.mp465.96MB
29 - Python intro Functions/007 Copies and referents of variables.mp423.78MB
29 - Python intro Functions/008 Classes and object-oriented programming.mp4108.18MB
30 - Python intro Flow control/001 If-else statements.mp466.8MB
30 - Python intro Flow control/002 If-else statements, part 2.mp491.12MB
30 - Python intro Flow control/003 For loops.mp487.13MB
30 - Python intro Flow control/004 Enumerate and zip.mp458.59MB
30 - Python intro Flow control/005 Continue.mp433.03MB
30 - Python intro Flow control/006 Initializing variables.mp491.05MB
30 - Python intro Flow control/007 Single-line loops (list comprehension).mp475.14MB
30 - Python intro Flow control/008 while loops.mp491.1MB
30 - Python intro Flow control/009 Broadcasting in numpy.mp471.05MB
30 - Python intro Flow control/010 Function error checking and handling.mp499.87MB
31 - Python intro Text and plots/001 Printing and string interpolation.mp494.83MB
31 - Python intro Text and plots/002 Plotting dots and lines.mp453.87MB
31 - Python intro Text and plots/003 Subplot geometry.mp486.78MB
31 - Python intro Text and plots/004 Making the graphs look nicer.mp4107.66MB
31 - Python intro Text and plots/005 Seaborn.mp459.72MB
31 - Python intro Text and plots/006 Images.mp493.56MB
31 - Python intro Text and plots/007 Export plots in low and high resolution.mp417.17MB

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