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[FreeCourseSite.com] Udemy - A deep understanding of deep learning (with Python intro)
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2022-4-23 23:04
2024-12-21 22:29
248
21.09 GB
253
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Udemy
-
A
deep
understanding
of
deep
learning
with
Python
intro
文件列表
01 Introduction/001 How to learn from this course.mp4
54.97MB
01 Introduction/002 Using Udemy like a pro.mp4
54.37MB
02 Download all course materials/001 Downloading and using the code.mp4
45.65MB
02 Download all course materials/002 My policy on code-sharing.mp4
10.24MB
03 Concepts in deep learning/001 What is an artificial neural network_.mp4
65.38MB
03 Concepts in deep learning/002 How models _learn_.mp4
72.79MB
03 Concepts in deep learning/003 The role of DL in science and knowledge.mp4
121.55MB
03 Concepts in deep learning/004 Running experiments to understand DL.mp4
74.84MB
03 Concepts in deep learning/005 Are artificial _neurons_ like biological neurons_.mp4
114.65MB
04 About the Python tutorial/001 Should you watch the Python tutorial_.mp4
23.77MB
05 Math, numpy, PyTorch/001 Introduction to this section.mp4
11.12MB
05 Math, numpy, PyTorch/002 Spectral theories in mathematics.mp4
51.06MB
05 Math, numpy, PyTorch/003 Terms and datatypes in math and computers.mp4
38.08MB
05 Math, numpy, PyTorch/004 Converting reality to numbers.mp4
33.21MB
05 Math, numpy, PyTorch/005 Vector and matrix transpose.mp4
37.66MB
05 Math, numpy, PyTorch/006 OMG it's the dot product!.mp4
50.11MB
05 Math, numpy, PyTorch/007 Matrix multiplication.mp4
85.67MB
05 Math, numpy, PyTorch/008 Softmax.mp4
95.96MB
05 Math, numpy, PyTorch/009 Logarithms.mp4
43.88MB
05 Math, numpy, PyTorch/010 Entropy and cross-entropy.mp4
106MB
05 Math, numpy, PyTorch/011 Min_max and argmin_argmax.mp4
88.21MB
05 Math, numpy, PyTorch/012 Mean and variance.mp4
80.57MB
05 Math, numpy, PyTorch/013 Random sampling and sampling variability.mp4
85.42MB
05 Math, numpy, PyTorch/014 Reproducible randomness via seeding.mp4
69.7MB
05 Math, numpy, PyTorch/015 The t-test.mp4
81.36MB
05 Math, numpy, PyTorch/016 Derivatives_ intuition and polynomials.mp4
80.3MB
05 Math, numpy, PyTorch/017 Derivatives find minima.mp4
45.47MB
05 Math, numpy, PyTorch/018 Derivatives_ product and chain rules.mp4
55.63MB
06 Gradient descent/001 Overview of gradient descent.mp4
68.44MB
06 Gradient descent/002 What about local minima_.mp4
67.08MB
06 Gradient descent/003 Gradient descent in 1D.mp4
119.29MB
06 Gradient descent/004 CodeChallenge_ unfortunate starting value.mp4
77.09MB
06 Gradient descent/005 Gradient descent in 2D.mp4
95.9MB
06 Gradient descent/006 CodeChallenge_ 2D gradient ascent.mp4
39.36MB
06 Gradient descent/007 Parametric experiments on g.d.mp4
135.61MB
06 Gradient descent/008 CodeChallenge_ fixed vs. dynamic learning rate.mp4
114.56MB
06 Gradient descent/009 Vanishing and exploding gradients.mp4
30.24MB
06 Gradient descent/010 Tangent_ Notebook revision history.mp4
22.18MB
07 ANNs/001 The perceptron and ANN architecture.mp4
83.64MB
07 ANNs/002 A geometric view of ANNs.mp4
70.88MB
07 ANNs/003 ANN math part 1 (forward prop).mp4
57.9MB
07 ANNs/004 ANN math part 2 (errors, loss, cost).mp4
48.47MB
07 ANNs/005 ANN math part 3 (backprop).mp4
52.89MB
07 ANNs/006 ANN for regression.mp4
135.5MB
07 ANNs/007 CodeChallenge_ manipulate regression slopes.mp4
139.12MB
07 ANNs/008 ANN for classifying qwerties.mp4
151.12MB
07 ANNs/009 Learning rates comparison.mp4
168.64MB
07 ANNs/010 Multilayer ANN.mp4
144.7MB
07 ANNs/011 Linear solutions to linear problems.mp4
50.37MB
07 ANNs/012 Why multilayer linear models don't exist.mp4
26.46MB
07 ANNs/013 Multi-output ANN (iris dataset).mp4
186.77MB
07 ANNs/014 CodeChallenge_ more qwerties!.mp4
95.1MB
07 ANNs/015 Comparing the number of hidden units.mp4
71.15MB
07 ANNs/016 Depth vs. breadth_ number of parameters.mp4
132.07MB
07 ANNs/017 Defining models using sequential vs. class.mp4
89.48MB
07 ANNs/018 Model depth vs. breadth.mp4
158.91MB
07 ANNs/019 CodeChallenge_ convert sequential to class.mp4
51.44MB
07 ANNs/021 Reflection_ Are DL models understandable yet_.mp4
58.59MB
08 Overfitting and cross-validation/001 What is overfitting and is it as bad as they say_.mp4
73.13MB
08 Overfitting and cross-validation/002 Cross-validation.mp4
88.19MB
08 Overfitting and cross-validation/003 Generalization.mp4
32.44MB
08 Overfitting and cross-validation/004 Cross-validation -- manual separation.mp4
98.3MB
08 Overfitting and cross-validation/005 Cross-validation -- scikitlearn.mp4
142.88MB
08 Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4
172.32MB
08 Overfitting and cross-validation/007 Splitting data into train, devset, test.mp4
79.21MB
08 Overfitting and cross-validation/008 Cross-validation on regression.mp4
60.35MB
09 Regularization/001 Regularization_ Concept and methods.mp4
80.05MB
09 Regularization/002 train() and eval() modes.mp4
38.34MB
09 Regularization/003 Dropout regularization.mp4
136.03MB
09 Regularization/004 Dropout regularization in practice.mp4
183.23MB
09 Regularization/005 Dropout example 2.mp4
53.87MB
09 Regularization/006 Weight regularization (L1_L2)_ math.mp4
85.41MB
09 Regularization/007 L2 regularization in practice.mp4
110.47MB
09 Regularization/008 L1 regularization in practice.mp4
99.44MB
09 Regularization/009 Training in mini-batches.mp4
62.12MB
09 Regularization/010 Batch training in action.mp4
89.1MB
09 Regularization/011 The importance of equal batch sizes.mp4
60.11MB
09 Regularization/012 CodeChallenge_ Effects of mini-batch size.mp4
95.42MB
10 Metaparameters (activations, optimizers)/001 What are _metaparameters__.mp4
32.7MB
10 Metaparameters (activations, optimizers)/002 The _wine quality_ dataset.mp4
143.5MB
10 Metaparameters (activations, optimizers)/003 CodeChallenge_ Minibatch size in the wine dataset.mp4
118.79MB
10 Metaparameters (activations, optimizers)/004 Data normalization.mp4
59.81MB
10 Metaparameters (activations, optimizers)/005 The importance of data normalization.mp4
64.65MB
10 Metaparameters (activations, optimizers)/006 Batch normalization.mp4
76.81MB
10 Metaparameters (activations, optimizers)/007 Batch normalization in practice.mp4
61.76MB
10 Metaparameters (activations, optimizers)/008 CodeChallenge_ Batch-normalize the qwerties.mp4
41.43MB
10 Metaparameters (activations, optimizers)/009 Activation functions.mp4
97.03MB
10 Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.mp4
91.46MB
10 Metaparameters (activations, optimizers)/011 Activation functions comparison.mp4
73.9MB
10 Metaparameters (activations, optimizers)/012 CodeChallenge_ Compare relu variants.mp4
63.97MB
10 Metaparameters (activations, optimizers)/013 CodeChallenge_ Predict sugar.mp4
122.1MB
10 Metaparameters (activations, optimizers)/014 Loss functions.mp4
90.3MB
10 Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.mp4
138.1MB
10 Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs.mp4
99.8MB
10 Metaparameters (activations, optimizers)/017 Optimizers (minibatch, momentum).mp4
98.07MB
10 Metaparameters (activations, optimizers)/018 SGD with momentum.mp4
62.1MB
10 Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam).mp4
76.73MB
10 Metaparameters (activations, optimizers)/020 Optimizers comparison.mp4
86.88MB
10 Metaparameters (activations, optimizers)/021 CodeChallenge_ Optimizers and... something.mp4
49.77MB
10 Metaparameters (activations, optimizers)/022 CodeChallenge_ Adam with L2 regularization.mp4
53MB
10 Metaparameters (activations, optimizers)/023 Learning rate decay.mp4
96.9MB
10 Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.mp4
61.74MB
11 FFNs/001 What are fully-connected and feedforward networks_.mp4
25.53MB
11 FFNs/002 The MNIST dataset.mp4
101.46MB
11 FFNs/003 FFN to classify digits.mp4
161.85MB
11 FFNs/004 CodeChallenge_ Binarized MNIST images.mp4
40.78MB
11 FFNs/005 CodeChallenge_ Data normalization.mp4
96.25MB
11 FFNs/006 Distributions of weights pre- and post-learning.mp4
116.26MB
11 FFNs/007 CodeChallenge_ MNIST and breadth vs. depth.mp4
95.21MB
11 FFNs/008 CodeChallenge_ Optimizers and MNIST.mp4
46.26MB
11 FFNs/009 Scrambled MNIST.mp4
60.17MB
11 FFNs/010 Shifted MNIST.mp4
77.91MB
11 FFNs/011 CodeChallenge_ The mystery of the missing 7.mp4
74.25MB
11 FFNs/012 Universal approximation theorem.mp4
49.18MB
12 More on data/001 Anatomy of a torch dataset and dataloader.mp4
135.84MB
12 More on data/002 Data size and network size.mp4
135.67MB
12 More on data/003 CodeChallenge_ unbalanced data.mp4
166.26MB
12 More on data/004 What to do about unbalanced designs_.mp4
54.21MB
12 More on data/005 Data oversampling in MNIST.mp4
122.59MB
12 More on data/006 Data noise augmentation (with devset+test).mp4
106.09MB
12 More on data/007 Data feature augmentation.mp4
158.27MB
12 More on data/008 Getting data into colab.mp4
43.75MB
12 More on data/009 Save and load trained models.mp4
55.71MB
12 More on data/010 Save the best-performing model.mp4
126.5MB
12 More on data/011 Where to find online datasets.mp4
41.7MB
13 Measuring model performance/001 Two perspectives of the world.mp4
40.01MB
13 Measuring model performance/002 Accuracy, precision, recall, F1.mp4
72.57MB
13 Measuring model performance/003 APRF in code.mp4
51.79MB
13 Measuring model performance/004 APRF example 1_ wine quality.mp4
107.35MB
13 Measuring model performance/005 APRF example 2_ MNIST.mp4
98.62MB
13 Measuring model performance/006 CodeChallenge_ MNIST with unequal groups.mp4
62.37MB
13 Measuring model performance/007 Computation time.mp4
81.73MB
13 Measuring model performance/008 Better performance in test than train_.mp4
44.83MB
14 FFN milestone projects/001 Project 1_ A gratuitously complex adding machine.mp4
48.55MB
14 FFN milestone projects/002 Project 1_ My solution.mp4
99.75MB
14 FFN milestone projects/003 Project 2_ Predicting heart disease.mp4
50.61MB
14 FFN milestone projects/004 Project 2_ My solution.mp4
155.73MB
14 FFN milestone projects/005 Project 3_ FFN for missing data interpolation.mp4
45.39MB
14 FFN milestone projects/006 Project 3_ My solution.mp4
75.48MB
15 Weight inits and investigations/001 Explanation of weight matrix sizes.mp4
68.98MB
15 Weight inits and investigations/002 A surprising demo of weight initializations.mp4
121.57MB
15 Weight inits and investigations/003 Theory_ Why and how to initialize weights.mp4
79.41MB
15 Weight inits and investigations/004 CodeChallenge_ Weight variance inits.mp4
103.96MB
15 Weight inits and investigations/005 Xavier and Kaiming initializations.mp4
134.08MB
15 Weight inits and investigations/006 CodeChallenge_ Xavier vs. Kaiming.mp4
126.5MB
15 Weight inits and investigations/007 CodeChallenge_ Identically random weights.mp4
88.17MB
15 Weight inits and investigations/008 Freezing weights during learning.mp4
93.15MB
15 Weight inits and investigations/009 Learning-related changes in weights.mp4
146.78MB
15 Weight inits and investigations/010 Use default inits or apply your own_.mp4
28.05MB
16 Autoencoders/001 What are autoencoders and what do they do_.mp4
49.04MB
16 Autoencoders/002 Denoising MNIST.mp4
118.53MB
16 Autoencoders/003 CodeChallenge_ How many units_.mp4
135.38MB
16 Autoencoders/004 AEs for occlusion.mp4
138.2MB
16 Autoencoders/005 The latent code of MNIST.mp4
161.81MB
16 Autoencoders/006 Autoencoder with tied weights.mp4
177.74MB
17 Running models on a GPU/001 What is a GPU and why use it_.mp4
88.73MB
17 Running models on a GPU/002 Implementation.mp4
76.6MB
17 Running models on a GPU/003 CodeChallenge_ Run an experiment on the GPU.mp4
52.99MB
18 Convolution and transformations/001 Convolution_ concepts.mp4
98.06MB
18 Convolution and transformations/002 Feature maps and convolution kernels.mp4
70.41MB
18 Convolution and transformations/003 Convolution in code.mp4
173.1MB
18 Convolution and transformations/004 Convolution parameters (stride, padding).mp4
66.93MB
18 Convolution and transformations/005 The Conv2 class in PyTorch.mp4
100.19MB
18 Convolution and transformations/006 CodeChallenge_ Choose the parameters.mp4
58.71MB
18 Convolution and transformations/007 Transpose convolution.mp4
92.89MB
18 Convolution and transformations/008 Max_mean pooling.mp4
89.07MB
18 Convolution and transformations/009 Pooling in PyTorch.mp4
81.02MB
18 Convolution and transformations/010 To pool or to stride_.mp4
55.51MB
18 Convolution and transformations/011 Image transforms.mp4
129.9MB
18 Convolution and transformations/012 Creating and using custom DataLoaders.mp4
139.53MB
19 Understand and design CNNs/001 The canonical CNN architecture.mp4
55.83MB
19 Understand and design CNNs/002 CNN to classify MNIST digits.mp4
200.33MB
19 Understand and design CNNs/003 CNN on shifted MNIST.mp4
58.34MB
19 Understand and design CNNs/004 Classify Gaussian blurs.mp4
185.14MB
19 Understand and design CNNs/005 Examine feature map activations.mp4
260.56MB
19 Understand and design CNNs/006 CodeChallenge_ Softcode internal parameters.mp4
120.1MB
19 Understand and design CNNs/007 CodeChallenge_ How wide the FC_.mp4
94.08MB
19 Understand and design CNNs/008 Do autoencoders clean Gaussians_.mp4
147.88MB
19 Understand and design CNNs/009 CodeChallenge_ AEs and occluded Gaussians.mp4
89.45MB
19 Understand and design CNNs/010 CodeChallenge_ Custom loss functions.mp4
132.89MB
19 Understand and design CNNs/011 Discover the Gaussian parameters.mp4
136.65MB
19 Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4
201.31MB
19 Understand and design CNNs/013 Dropout in CNNs.mp4
82.73MB
19 Understand and design CNNs/014 CodeChallenge_ How low can you go_.mp4
55.36MB
19 Understand and design CNNs/015 CodeChallenge_ Varying number of channels.mp4
92.37MB
19 Understand and design CNNs/016 So many possibilities! How to create a CNN_.mp4
21.04MB
20 CNN milestone projects/001 Project 1_ Import and classify CIFAR10.mp4
48.36MB
20 CNN milestone projects/002 Project 1_ My solution.mp4
118.6MB
20 CNN milestone projects/003 Project 2_ CIFAR-autoencoder.mp4
33.37MB
20 CNN milestone projects/004 Project 3_ FMNIST.mp4
26.45MB
20 CNN milestone projects/005 Project 4_ Psychometric functions in CNNs.mp4
76.27MB
21 Transfer learning/001 Transfer learning_ What, why, and when_.mp4
96.61MB
21 Transfer learning/002 Transfer learning_ MNIST -_ FMNIST.mp4
90.35MB
21 Transfer learning/003 CodeChallenge_ letters to numbers.mp4
118.74MB
21 Transfer learning/004 Famous CNN architectures.mp4
41.28MB
21 Transfer learning/005 Transfer learning with ResNet-18.mp4
148.46MB
21 Transfer learning/006 CodeChallenge_ VGG-16.mp4
20.28MB
21 Transfer learning/007 Pretraining with autoencoders.mp4
156.58MB
21 Transfer learning/008 CIFAR10 with autoencoder-pretrained model.mp4
153.34MB
22 Style transfer/001 What is style transfer and how does it work_.mp4
40.57MB
22 Style transfer/002 The Gram matrix (feature activation covariance).mp4
66.49MB
22 Style transfer/003 The style transfer algorithm.mp4
67.31MB
22 Style transfer/004 Transferring the screaming bathtub.mp4
216.82MB
22 Style transfer/005 CodeChallenge_ Style transfer with AlexNet.mp4
53.47MB
23 Generative adversarial networks/001 GAN_ What, why, and how.mp4
89.74MB
23 Generative adversarial networks/002 Linear GAN with MNIST.mp4
169.9MB
23 Generative adversarial networks/003 CodeChallenge_ Linear GAN with FMNIST.mp4
62.73MB
23 Generative adversarial networks/004 CNN GAN with Gaussians.mp4
135.7MB
23 Generative adversarial networks/005 CodeChallenge_ Gaussians with fewer layers.mp4
53.06MB
23 Generative adversarial networks/006 CNN GAN with FMNIST.mp4
54.58MB
23 Generative adversarial networks/007 CodeChallenge_ CNN GAN with CIFAR.mp4
60.77MB
24 Ethics of deep learning/001 Will AI save us or destroy us_.mp4
65.92MB
24 Ethics of deep learning/002 Example case studies.mp4
52.92MB
24 Ethics of deep learning/003 Some other possible ethical scenarios.mp4
66.25MB
24 Ethics of deep learning/004 Will deep learning take our jobs_.mp4
75.14MB
24 Ethics of deep learning/005 Accountability and making ethical AI.mp4
70.06MB
25 Where to go from here_/001 How to learn topic _X_ in deep learning_.mp4
42.03MB
25 Where to go from here_/002 How to read academic DL papers.mp4
141.85MB
27 Python intro_ Data types/001 How to learn from the Python tutorial.mp4
21.97MB
27 Python intro_ Data types/002 Variables.mp4
77.58MB
27 Python intro_ Data types/003 Math and printing.mp4
78.5MB
27 Python intro_ Data types/004 Lists (1 of 2).mp4
55.04MB
27 Python intro_ Data types/005 Lists (2 of 2).mp4
46.69MB
27 Python intro_ Data types/006 Tuples.mp4
35.75MB
27 Python intro_ Data types/007 Booleans.mp4
76.83MB
27 Python intro_ Data types/008 Dictionaries.mp4
50.67MB
28 Python intro_ Indexing, slicing/001 Indexing.mp4
51.07MB
28 Python intro_ Indexing, slicing/002 Slicing.mp4
48.45MB
29 Python intro_ Functions/001 Inputs and outputs.mp4
29.49MB
29 Python intro_ Functions/002 Python libraries (numpy).mp4
63.39MB
29 Python intro_ Functions/003 Python libraries (pandas).mp4
81.19MB
29 Python intro_ Functions/004 Getting help on functions.mp4
48.6MB
29 Python intro_ Functions/005 Creating functions.mp4
88.43MB
29 Python intro_ Functions/006 Global and local variable scopes.mp4
65.96MB
29 Python intro_ Functions/007 Copies and referents of variables.mp4
23.78MB
29 Python intro_ Functions/008 Classes and object-oriented programming.mp4
108.18MB
30 Python intro_ Flow control/001 If-else statements.mp4
66.8MB
30 Python intro_ Flow control/002 If-else statements, part 2.mp4
91.12MB
30 Python intro_ Flow control/003 For loops.mp4
87.13MB
30 Python intro_ Flow control/004 Enumerate and zip.mp4
58.59MB
30 Python intro_ Flow control/005 Continue.mp4
33.03MB
30 Python intro_ Flow control/006 Initializing variables.mp4
91.05MB
30 Python intro_ Flow control/007 Single-line loops (list comprehension).mp4
75.14MB
30 Python intro_ Flow control/008 while loops.mp4
91.1MB
30 Python intro_ Flow control/009 Broadcasting in numpy.mp4
71.05MB
30 Python intro_ Flow control/010 Function error checking and handling.mp4
99.87MB
31 Python intro_ Text and plots/001 Printing and string interpolation.mp4
94.83MB
31 Python intro_ Text and plots/002 Plotting dots and lines.mp4
53.87MB
31 Python intro_ Text and plots/003 Subplot geometry.mp4
86.78MB
31 Python intro_ Text and plots/004 Making the graphs look nicer.mp4
107.66MB
31 Python intro_ Text and plots/005 Seaborn.mp4
59.72MB
31 Python intro_ Text and plots/006 Images.mp4
93.56MB
31 Python intro_ Text and plots/007 Export plots in low and high resolution.mp4
43.57MB
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