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[Udemy] A deep understanding of deep learning (with Python intro) (08.2021)

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File Name
Size
01 Introduction/001 How to learn from this course.mp4
55 MB
01 Introduction/002 Using Udemy like a pro.en.srt
12 kB
01 Introduction/002 Using Udemy like a pro.mp4
54 MB
02 Download all course materials/001 Downloading and using the code.en.srt
9.4 kB
02 Download all course materials/001 Downloading and using the code.mp4
46 MB
02 Download all course materials/002 My policy on code-sharing.en.srt
2.5 kB
02 Download all course materials/002 My policy on code-sharing.mp4
10 MB
02 Download all course materials/003 DUDL_PythonCode.zip
701 kB
03 Concepts in deep learning/001 What is an artificial neural network_.en.srt
21 kB
03 Concepts in deep learning/001 What is an artificial neural network_.mp4
65 MB
03 Concepts in deep learning/002 How models _learn_.en.srt
19 kB
03 Concepts in deep learning/002 How models _learn_.mp4
73 MB
03 Concepts in deep learning/003 The role of DL in science and knowledge.en.srt
23 kB
03 Concepts in deep learning/003 The role of DL in science and knowledge.mp4
122 MB
03 Concepts in deep learning/004 Running experiments to understand DL.en.srt
19 kB
03 Concepts in deep learning/004 Running experiments to understand DL.mp4
75 MB
03 Concepts in deep learning/005 Are artificial _neurons_ like biological neurons_.en.srt
24 kB
03 Concepts in deep learning/005 Are artificial _neurons_ like biological neurons_.mp4
115 MB
04 About the Python tutorial/001 Should you watch the Python tutorial_.en.srt
6.1 kB
04 About the Python tutorial/001 Should you watch the Python tutorial_.mp4
24 MB
05 Math, numpy, PyTorch/001 Introduction to this section.en.srt
2.9 kB
05 Math, numpy, PyTorch/001 Introduction to this section.mp4
11 MB
05 Math, numpy, PyTorch/002 Spectral theories in mathematics.en.srt
14 kB
05 Math, numpy, PyTorch/002 Spectral theories in mathematics.mp4
51 MB
05 Math, numpy, PyTorch/003 Terms and datatypes in math and computers.en.srt
11 kB
05 Math, numpy, PyTorch/003 Terms and datatypes in math and computers.mp4
38 MB
05 Math, numpy, PyTorch/004 Converting reality to numbers.en.srt
9.6 kB
05 Math, numpy, PyTorch/004 Converting reality to numbers.mp4
33 MB
05 Math, numpy, PyTorch/005 Vector and matrix transpose.en.srt
10 kB
05 Math, numpy, PyTorch/005 Vector and matrix transpose.mp4
38 MB
05 Math, numpy, PyTorch/006 OMG it's the dot product!.en.srt
14 kB
05 Math, numpy, PyTorch/006 OMG it's the dot product!.mp4
50 MB
05 Math, numpy, PyTorch/007 Matrix multiplication.en.srt
21 kB
05 Math, numpy, PyTorch/007 Matrix multiplication.mp4
86 MB
05 Math, numpy, PyTorch/008 Softmax.en.srt
28 kB
05 Math, numpy, PyTorch/008 Softmax.mp4
96 MB
05 Math, numpy, PyTorch/009 Logarithms.en.srt
12 kB
05 Math, numpy, PyTorch/009 Logarithms.mp4
44 MB
05 Math, numpy, PyTorch/010 Entropy and cross-entropy.mp4
106 MB
05 Math, numpy, PyTorch/011 Min_max and argmin_argmax.en.srt
18 kB
05 Math, numpy, PyTorch/011 Min_max and argmin_argmax.mp4
88 MB
05 Math, numpy, PyTorch/012 Mean and variance.en.srt
22 kB
05 Math, numpy, PyTorch/012 Mean and variance.mp4
81 MB
05 Math, numpy, PyTorch/013 Random sampling and sampling variability.en.srt
16 kB
05 Math, numpy, PyTorch/013 Random sampling and sampling variability.mp4
85 MB
05 Math, numpy, PyTorch/014 Reproducible randomness via seeding.en.srt
12 kB
05 Math, numpy, PyTorch/014 Reproducible randomness via seeding.mp4
70 MB
05 Math, numpy, PyTorch/015 The t-test.en.srt
19 kB
05 Math, numpy, PyTorch/015 The t-test.mp4
81 MB
05 Math, numpy, PyTorch/016 Derivatives_ intuition and polynomials.en.srt
24 kB
05 Math, numpy, PyTorch/016 Derivatives_ intuition and polynomials.mp4
80 MB
05 Math, numpy, PyTorch/017 Derivatives find minima.en.srt
12 kB
05 Math, numpy, PyTorch/017 Derivatives find minima.mp4
46 MB
05 Math, numpy, PyTorch/018 Derivatives_ product and chain rules.en.srt
14 kB
05 Math, numpy, PyTorch/018 Derivatives_ product and chain rules.mp4
56 MB
06 Gradient descent/001 Overview of gradient descent.en.srt
21 kB
06 Gradient descent/001 Overview of gradient descent.mp4
68 MB
06 Gradient descent/002 What about local minima_.en.srt
17 kB
06 Gradient descent/002 What about local minima_.mp4
67 MB
06 Gradient descent/003 Gradient descent in 1D.en.srt
25 kB
06 Gradient descent/003 Gradient descent in 1D.mp4
119 MB
06 Gradient descent/004 CodeChallenge_ unfortunate starting value.en.srt
16 kB
06 Gradient descent/004 CodeChallenge_ unfortunate starting value.mp4
77 MB
06 Gradient descent/005 Gradient descent in 2D.en.srt
21 kB
06 Gradient descent/005 Gradient descent in 2D.mp4
96 MB
06 Gradient descent/006 CodeChallenge_ 2D gradient ascent.en.srt
7.5 kB
06 Gradient descent/006 CodeChallenge_ 2D gradient ascent.mp4
39 MB
06 Gradient descent/007 Parametric experiments on g.d.en.srt
27 kB
06 Gradient descent/007 Parametric experiments on g.d.mp4
136 MB
06 Gradient descent/008 CodeChallenge_ fixed vs. dynamic learning rate.en.srt
23 kB
06 Gradient descent/008 CodeChallenge_ fixed vs. dynamic learning rate.mp4
115 MB
06 Gradient descent/009 Vanishing and exploding gradients.en.srt
9.1 kB
06 Gradient descent/009 Vanishing and exploding gradients.mp4
30 MB
06 Gradient descent/010 Tangent_ Notebook revision history.en.srt
2.8 kB
06 Gradient descent/010 Tangent_ Notebook revision history.mp4
22 MB
07 ANNs/001 The perceptron and ANN architecture.en.srt
27 kB
07 ANNs/001 The perceptron and ANN architecture.mp4
84 MB
07 ANNs/002 A geometric view of ANNs.en.srt
19 kB
07 ANNs/002 A geometric view of ANNs.mp4
71 MB
07 ANNs/003 ANN math part 1 (forward prop).en.srt
17 kB
07 ANNs/003 ANN math part 1 (forward prop).mp4
58 MB
07 ANNs/004 ANN math part 2 (errors, loss, cost).en.srt
14 kB
07 ANNs/004 ANN math part 2 (errors, loss, cost).mp4
48 MB
07 ANNs/005 ANN math part 3 (backprop).en.srt
15 kB
07 ANNs/005 ANN math part 3 (backprop).mp4
53 MB
07 ANNs/006 ANN for regression.en.srt
36 kB
07 ANNs/006 ANN for regression.mp4
136 MB
07 ANNs/007 CodeChallenge_ manipulate regression slopes.en.srt
28 kB
07 ANNs/007 CodeChallenge_ manipulate regression slopes.mp4
139 MB
07 ANNs/008 ANN for classifying qwerties.en.srt
34 kB
07 ANNs/008 ANN for classifying qwerties.mp4
151 MB
07 ANNs/009 Learning rates comparison.en.srt
36 kB
07 ANNs/009 Learning rates comparison.mp4
169 MB
07 ANNs/010 Multilayer ANN.en.srt
29 kB
07 ANNs/010 Multilayer ANN.mp4
145 MB
07 ANNs/011 Linear solutions to linear problems.en.srt
12 kB
07 ANNs/011 Linear solutions to linear problems.mp4
50 MB
07 ANNs/012 Why multilayer linear models don't exist.en.srt
9.2 kB
07 ANNs/012 Why multilayer linear models don't exist.mp4
26 MB
07 ANNs/013 Multi-output ANN (iris dataset).en.srt
38 kB
07 ANNs/013 Multi-output ANN (iris dataset).mp4
187 MB
07 ANNs/014 CodeChallenge_ more qwerties!.en.srt
18 kB
07 ANNs/014 CodeChallenge_ more qwerties!.mp4
95 MB
07 ANNs/015 Comparing the number of hidden units.en.srt
15 kB
07 ANNs/015 Comparing the number of hidden units.mp4
71 MB
07 ANNs/016 Depth vs. breadth_ number of parameters.en.srt
26 kB
07 ANNs/016 Depth vs. breadth_ number of parameters.mp4
132 MB
07 ANNs/017 Defining models using sequential vs. class.en.srt
19 kB
07 ANNs/017 Defining models using sequential vs. class.mp4
90 MB
07 ANNs/018 Model depth vs. breadth.en.srt
31 kB
07 ANNs/018 Model depth vs. breadth.mp4
159 MB
07 ANNs/019 CodeChallenge_ convert sequential to class.en.srt
9.7 kB
07 ANNs/019 CodeChallenge_ convert sequential to class.mp4
51 MB
07 ANNs/020 Diversity of ANN visual representations.html
1.4 kB
07 ANNs/021 Reflection_ Are DL models understandable yet_.en.srt
12 kB
07 ANNs/021 Reflection_ Are DL models understandable yet_.mp4
59 MB
08 Overfitting and cross-validation/001 What is overfitting and is it as bad as they say_.en.srt
18 kB
08 Overfitting and cross-validation/001 What is overfitting and is it as bad as they say_.mp4
73 MB
08 Overfitting and cross-validation/002 Cross-validation.en.srt
25 kB
08 Overfitting and cross-validation/002 Cross-validation.mp4
88 MB
08 Overfitting and cross-validation/003 Generalization.en.srt
8.8 kB
08 Overfitting and cross-validation/003 Generalization.mp4
32 MB
08 Overfitting and cross-validation/004 Cross-validation -- manual separation.en.srt
19 kB
08 Overfitting and cross-validation/004 Cross-validation -- manual separation.mp4
98 MB
08 Overfitting and cross-validation/005 Cross-validation -- scikitlearn.en.srt
30 kB
08 Overfitting and cross-validation/005 Cross-validation -- scikitlearn.mp4
143 MB
08 Overfitting and cross-validation/006 Cross-validation -- DataLoader.en.srt
29 kB
08 Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4
172 MB
08 Overfitting and cross-validation/007 Splitting data into train, devset, test.en.srt
14 kB
08 Overfitting and cross-validation/007 Splitting data into train, devset, test.mp4
79 MB
08 Overfitting and cross-validation/008 Cross-validation on regression.en.srt
12 kB
08 Overfitting and cross-validation/008 Cross-validation on regression.mp4
60 MB
09 Regularization/001 Regularization_ Concept and methods.en.srt
19 kB
09 Regularization/001 Regularization_ Concept and methods.mp4
80 MB
09 Regularization/002 train() and eval() modes.en.srt
10 kB
09 Regularization/002 train() and eval() modes.mp4
38 MB
09 Regularization/003 Dropout regularization.en.srt
31 kB
09 Regularization/003 Dropout regularization.mp4
136 MB
09 Regularization/004 Dropout regularization in practice.en.srt
33 kB
09 Regularization/004 Dropout regularization in practice.mp4
183 MB
09 Regularization/005 Dropout example 2.en.srt
9.2 kB
09 Regularization/005 Dropout example 2.mp4
54 MB
09 Regularization/006 Weight regularization (L1_L2)_ math.en.srt
27 kB
09 Regularization/006 Weight regularization (L1_L2)_ math.mp4
85 MB
09 Regularization/007 L2 regularization in practice.en.srt
19 kB
09 Regularization/007 L2 regularization in practice.mp4
110 MB
09 Regularization/008 L1 regularization in practice.en.srt
17 kB
09 Regularization/008 L1 regularization in practice.mp4
99 MB
09 Regularization/009 Training in mini-batches.en.srt
17 kB
09 Regularization/009 Training in mini-batches.mp4
62 MB
09 Regularization/010 Batch training in action.en.srt
16 kB
09 Regularization/010 Batch training in action.mp4
89 MB
09 Regularization/011 The importance of equal batch sizes.en.srt
9.5 kB
09 Regularization/011 The importance of equal batch sizes.mp4
60 MB
09 Regularization/012 CodeChallenge_ Effects of mini-batch size.en.srt
18 kB
09 Regularization/012 CodeChallenge_ Effects of mini-batch size.mp4
95 MB
10 Metaparameters (activations, optimizers)/001 What are _metaparameters__.en.srt
7.3 kB
10 Metaparameters (activations, optimizers)/001 What are _metaparameters__.mp4
33 MB
10 Metaparameters (activations, optimizers)/002 The _wine quality_ dataset.en.srt
26 kB
10 Metaparameters (activations, optimizers)/002 The _wine quality_ dataset.mp4
144 MB
10 Metaparameters (activations, optimizers)/003 CodeChallenge_ Minibatch size in the wine dataset.en.srt
23 kB
10 Metaparameters (activations, optimizers)/003 CodeChallenge_ Minibatch size in the wine dataset.mp4
119 MB
10 Metaparameters (activations, optimizers)/004 Data normalization.en.srt
20 kB
10 Metaparameters (activations, optimizers)/004 Data normalization.mp4
60 MB
10 Metaparameters (activations, optimizers)/005 The importance of data normalization.en.srt
14 kB
10 Metaparameters (activations, optimizers)/005 The importance of data normalization.mp4
65 MB
10 Metaparameters (activations, optimizers)/006 Batch normalization.en.srt
19 kB
10 Metaparameters (activations, optimizers)/006 Batch normalization.mp4
77 MB
10 Metaparameters (activations, optimizers)/007 Batch normalization in practice.en.srt
11 kB
10 Metaparameters (activations, optimizers)/007 Batch normalization in practice.mp4
62 MB
10 Metaparameters (activations, optimizers)/008 CodeChallenge_ Batch-normalize the qwerties.en.srt
7.5 kB
10 Metaparameters (activations, optimizers)/008 CodeChallenge_ Batch-normalize the qwerties.mp4
41 MB
10 Metaparameters (activations, optimizers)/009 Activation functions.en.srt
26 kB
10 Metaparameters (activations, optimizers)/009 Activation functions.mp4
97 MB
10 Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.en.srt
17 kB
10 Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.mp4
92 MB
10 Metaparameters (activations, optimizers)/011 Activation functions comparison.en.srt
14 kB
10 Metaparameters (activations, optimizers)/011 Activation functions comparison.mp4
74 MB
10 Metaparameters (activations, optimizers)/012 CodeChallenge_ Compare relu variants.en.srt
11 kB
10 Metaparameters (activations, optimizers)/012 CodeChallenge_ Compare relu variants.mp4
64 MB
10 Metaparameters (activations, optimizers)/013 CodeChallenge_ Predict sugar.en.srt
25 kB
10 Metaparameters (activations, optimizers)/013 CodeChallenge_ Predict sugar.mp4
122 MB
10 Metaparameters (activations, optimizers)/014 Loss functions.en.srt
24 kB
10 Metaparameters (activations, optimizers)/014 Loss functions.mp4
90 MB
10 Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.en.srt
27 kB
10 Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.mp4
138 MB
10 Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs.en.srt
20 kB
10 Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs.mp4
100 MB
10 Metaparameters (activations, optimizers)/017 Optimizers (minibatch, momentum).mp4
98 MB
10 Metaparameters (activations, optimizers)/018 SGD with momentum.en.srt
12 kB
10 Metaparameters (activations, optimizers)/018 SGD with momentum.mp4
62 MB
10 Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam).en.srt
22 kB
10 Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam).mp4
77 MB
10 Metaparameters (activations, optimizers)/020 Optimizers comparison.en.srt
15 kB
10 Metaparameters (activations, optimizers)/020 Optimizers comparison.mp4
87 MB
10 Metaparameters (activations, optimizers)/021 CodeChallenge_ Optimizers and... something.en.srt
9.4 kB
10 Metaparameters (activations, optimizers)/021 CodeChallenge_ Optimizers and... something.mp4
50 MB
10 Metaparameters (activations, optimizers)/022 CodeChallenge_ Adam with L2 regularization.en.srt
10 kB
10 Metaparameters (activations, optimizers)/022 CodeChallenge_ Adam with L2 regularization.mp4
53 MB
10 Metaparameters (activations, optimizers)/023 Learning rate decay.en.srt
18 kB
10 Metaparameters (activations, optimizers)/023 Learning rate decay.mp4
97 MB
10 Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.en.srt
17 kB
10 Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.mp4
62 MB
11 FFNs/001 What are fully-connected and feedforward networks_.en.srt
6.9 kB
11 FFNs/001 What are fully-connected and feedforward networks_.mp4
26 MB
11 FFNs/002 The MNIST dataset.en.srt
19 kB
11 FFNs/002 The MNIST dataset.mp4
102 MB
11 FFNs/003 FFN to classify digits.en.srt
33 kB
11 FFNs/003 FFN to classify digits.mp4
162 MB
11 FFNs/004 CodeChallenge_ Binarized MNIST images.en.srt
7.4 kB
11 FFNs/004 CodeChallenge_ Binarized MNIST images.mp4
41 MB
11 FFNs/005 CodeChallenge_ Data normalization.en.srt
24 kB
11 FFNs/005 CodeChallenge_ Data normalization.mp4
96 MB
11 FFNs/006 Distributions of weights pre- and post-learning.en.srt
22 kB
11 FFNs/006 Distributions of weights pre- and post-learning.mp4
116 MB
11 FFNs/007 CodeChallenge_ MNIST and breadth vs. depth.en.srt
18 kB
11 FFNs/007 CodeChallenge_ MNIST and breadth vs. depth.mp4
95 MB
11 FFNs/008 CodeChallenge_ Optimizers and MNIST.en.srt
9.9 kB
11 FFNs/008 CodeChallenge_ Optimizers and MNIST.mp4
46 MB
11 FFNs/009 Scrambled MNIST.en.srt
11 kB
11 FFNs/009 Scrambled MNIST.mp4
60 MB
11 FFNs/010 Shifted MNIST.en.srt
16 kB
11 FFNs/010 Shifted MNIST.mp4
78 MB
11 FFNs/011 CodeChallenge_ The mystery of the missing 7.en.srt
16 kB
11 FFNs/011 CodeChallenge_ The mystery of the missing 7.mp4
74 MB
11 FFNs/012 Universal approximation theorem.en.srt
12 kB
11 FFNs/012 Universal approximation theorem.mp4
49 MB
12 More on data/001 Anatomy of a torch dataset and dataloader.en.srt
26 kB
12 More on data/001 Anatomy of a torch dataset and dataloader.mp4
136 MB
12 More on data/002 Data size and network size.en.srt
23 kB
12 More on data/002 Data size and network size.mp4
136 MB
12 More on data/003 CodeChallenge_ unbalanced data.en.srt
29 kB
12 More on data/003 CodeChallenge_ unbalanced data.mp4
166 MB
12 More on data/004 What to do about unbalanced designs_.mp4
54 MB
12 More on data/005 Data oversampling in MNIST.en.srt
24 kB
12 More on data/005 Data oversampling in MNIST.mp4
123 MB
12 More on data/006 Data noise augmentation (with devset+test).en.srt
19 kB
12 More on data/006 Data noise augmentation (with devset+test).mp4
106 MB
12 More on data/007 Data feature augmentation.en.srt
28 kB
12 More on data/007 Data feature augmentation.mp4
158 MB
12 More on data/008 Getting data into colab.en.srt
8.8 kB
12 More on data/008 Getting data into colab.mp4
44 MB
12 More on data/009 Save and load trained models.en.srt
8.9 kB
12 More on data/009 Save and load trained models.mp4
56 MB
12 More on data/010 Save the best-performing model.en.srt
22 kB
12 More on data/010 Save the best-performing model.mp4
126 MB
12 More on data/011 Where to find online datasets.en.srt
8.2 kB
12 More on data/011 Where to find online datasets.mp4
42 MB
13 Measuring model performance/001 Two perspectives of the world.en.srt
10 kB
13 Measuring model performance/001 Two perspectives of the world.mp4
40 MB
13 Measuring model performance/002 Accuracy, precision, recall, F1.en.srt
18 kB
13 Measuring model performance/002 Accuracy, precision, recall, F1.mp4
73 MB
13 Measuring model performance/003 APRF in code.en.srt
9.4 kB
13 Measuring model performance/003 APRF in code.mp4
52 MB
13 Measuring model performance/004 APRF example 1_ wine quality.en.srt
19 kB
13 Measuring model performance/004 APRF example 1_ wine quality.mp4
107 MB
13 Measuring model performance/005 APRF example 2_ MNIST.en.srt
17 kB
13 Measuring model performance/005 APRF example 2_ MNIST.mp4
99 MB
13 Measuring model performance/006 CodeChallenge_ MNIST with unequal groups.en.srt
13 kB
13 Measuring model performance/006 CodeChallenge_ MNIST with unequal groups.mp4
62 MB
13 Measuring model performance/007 Computation time.en.srt
14 kB
13 Measuring model performance/007 Computation time.mp4
82 MB
13 Measuring model performance/008 Better performance in test than train_.en.srt
12 kB
13 Measuring model performance/008 Better performance in test than train_.mp4
45 MB
14 FFN milestone projects/001 Project 1_ A gratuitously complex adding machine.en.srt
11 kB
14 FFN milestone projects/001 Project 1_ A gratuitously complex adding machine.mp4
49 MB
14 FFN milestone projects/002 Project 1_ My solution.en.srt
17 kB
14 FFN milestone projects/002 Project 1_ My solution.mp4
100 MB
14 FFN milestone projects/003 Project 2_ Predicting heart disease.en.srt
11 kB
14 FFN milestone projects/003 Project 2_ Predicting heart disease.mp4
51 MB
14 FFN milestone projects/004 Project 2_ My solution.en.srt
28 kB
14 FFN milestone projects/004 Project 2_ My solution.mp4
156 MB
14 FFN milestone projects/005 Project 3_ FFN for missing data interpolation.en.srt
14 kB
14 FFN milestone projects/005 Project 3_ FFN for missing data interpolation.mp4
45 MB
14 FFN milestone projects/006 Project 3_ My solution.en.srt
12 kB
14 FFN milestone projects/006 Project 3_ My solution.mp4
76 MB
15 Weight inits and investigations/001 Explanation of weight matrix sizes.en.srt
17 kB
15 Weight inits and investigations/001 Explanation of weight matrix sizes.mp4
69 MB
15 Weight inits and investigations/002 A surprising demo of weight initializations.en.srt
24 kB
15 Weight inits and investigations/002 A surprising demo of weight initializations.mp4
122 MB
15 Weight inits and investigations/003 Theory_ Why and how to initialize weights.en.srt
18 kB
15 Weight inits and investigations/003 Theory_ Why and how to initialize weights.mp4
79 MB
15 Weight inits and investigations/004 CodeChallenge_ Weight variance inits.en.srt
18 kB
15 Weight inits and investigations/004 CodeChallenge_ Weight variance inits.mp4
104 MB
15 Weight inits and investigations/005 Xavier and Kaiming initializations.en.srt
22 kB
15 Weight inits and investigations/005 Xavier and Kaiming initializations.mp4
134 MB
15 Weight inits and investigations/006 CodeChallenge_ Xavier vs. Kaiming.en.srt
25 kB
15 Weight inits and investigations/006 CodeChallenge_ Xavier vs. Kaiming.mp4
126 MB
15 Weight inits and investigations/007 CodeChallenge_ Identically random weights.en.srt
18 kB
15 Weight inits and investigations/007 CodeChallenge_ Identically random weights.mp4
88 MB
15 Weight inits and investigations/008 Freezing weights during learning.en.srt
19 kB
15 Weight inits and investigations/008 Freezing weights during learning.mp4
93 MB
15 Weight inits and investigations/009 Learning-related changes in weights.en.srt
33 kB
15 Weight inits and investigations/009 Learning-related changes in weights.mp4
147 MB
15 Weight inits and investigations/010 Use default inits or apply your own_.en.srt
6.3 kB
15 Weight inits and investigations/010 Use default inits or apply your own_.mp4
28 MB
16 Autoencoders/001 What are autoencoders and what do they do_.en.srt
17 kB
16 Autoencoders/001 What are autoencoders and what do they do_.mp4
49 MB
16 Autoencoders/002 Denoising MNIST.en.srt
23 kB
16 Autoencoders/002 Denoising MNIST.mp4
118 MB
16 Autoencoders/003 CodeChallenge_ How many units_.en.srt
29 kB
16 Autoencoders/003 CodeChallenge_ How many units_.mp4
135 MB
16 Autoencoders/004 AEs for occlusion.en.srt
25 kB
16 Autoencoders/004 AEs for occlusion.mp4
138 MB
16 Autoencoders/005 The latent code of MNIST.en.srt
32 kB
16 Autoencoders/005 The latent code of MNIST.mp4
162 MB
16 Autoencoders/006 Autoencoder with tied weights.en.srt
35 kB
16 Autoencoders/006 Autoencoder with tied weights.mp4
178 MB
17 Running models on a GPU/001 What is a GPU and why use it_.en.srt
22 kB
17 Running models on a GPU/001 What is a GPU and why use it_.mp4
89 MB
17 Running models on a GPU/002 Implementation.en.srt
15 kB
17 Running models on a GPU/002 Implementation.mp4
77 MB
17 Running models on a GPU/003 CodeChallenge_ Run an experiment on the GPU.en.srt
9.8 kB
17 Running models on a GPU/003 CodeChallenge_ Run an experiment on the GPU.mp4
53 MB
18 Convolution and transformations/001 Convolution_ concepts.en.srt
32 kB
18 Convolution and transformations/001 Convolution_ concepts.mp4
98 MB
18 Convolution and transformations/002 Feature maps and convolution kernels.en.srt
14 kB
18 Convolution and transformations/002 Feature maps and convolution kernels.mp4
70 MB
18 Convolution and transformations/003 Convolution in code.en.srt
30 kB
18 Convolution and transformations/003 Convolution in code.mp4
173 MB
18 Convolution and transformations/004 Convolution parameters (stride, padding).en.srt
18 kB
18 Convolution and transformations/004 Convolution parameters (stride, padding).mp4
67 MB
18 Convolution and transformations/005 The Conv2 class in PyTorch.en.srt
19 kB
18 Convolution and transformations/005 The Conv2 class in PyTorch.mp4
100 MB
18 Convolution and transformations/006 CodeChallenge_ Choose the parameters.en.srt
10 kB
18 Convolution and transformations/006 CodeChallenge_ Choose the parameters.mp4
59 MB
18 Convolution and transformations/007 Transpose convolution.en.srt
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18 Convolution and transformations/008 Max_mean pooling.en.srt
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18 Convolution and transformations/009 Pooling in PyTorch.en.srt
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18 Convolution and transformations/010 To pool or to stride_.en.srt
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18 Convolution and transformations/011 Image transforms.en.srt
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18 Convolution and transformations/012 Creating and using custom DataLoaders.en.srt
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19 Understand and design CNNs/001 The canonical CNN architecture.en.srt
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19 Understand and design CNNs/002 CNN to classify MNIST digits.en.srt
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19 Understand and design CNNs/003 CNN on shifted MNIST.en.srt
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19 Understand and design CNNs/004 Classify Gaussian blurs.en.srt
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19 Understand and design CNNs/005 Examine feature map activations.en.srt
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19 Understand and design CNNs/006 CodeChallenge_ Softcode internal parameters.en.srt
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19 Understand and design CNNs/007 CodeChallenge_ How wide the FC_.en.srt
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19 Understand and design CNNs/008 Do autoencoders clean Gaussians_.en.srt
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19 Understand and design CNNs/009 CodeChallenge_ AEs and occluded Gaussians.en.srt
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19 Understand and design CNNs/010 CodeChallenge_ Custom loss functions.en.srt
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19 Understand and design CNNs/011 Discover the Gaussian parameters.en.srt
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19 Understand and design CNNs/012 The EMNIST dataset (letter recognition).en.srt
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19 Understand and design CNNs/013 Dropout in CNNs.en.srt
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19 Understand and design CNNs/014 CodeChallenge_ How low can you go_.en.srt
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19 Understand and design CNNs/015 CodeChallenge_ Varying number of channels.en.srt
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19 Understand and design CNNs/016 So many possibilities! How to create a CNN_.en.srt
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20 CNN milestone projects/001 Project 1_ Import and classify CIFAR10.en.srt
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20 CNN milestone projects/002 Project 1_ My solution.en.srt
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20 CNN milestone projects/004 Project 3_ FMNIST.en.srt
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20 CNN milestone projects/005 Project 4_ Psychometric functions in CNNs.en.srt
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21 Transfer learning/001 Transfer learning_ What, why, and when_.en.srt
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21 Transfer learning/002 Transfer learning_ MNIST -_ FMNIST.en.srt
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21 Transfer learning/003 CodeChallenge_ letters to numbers.en.srt
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21 Transfer learning/004 Famous CNN architectures.en.srt
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21 Transfer learning/005 Transfer learning with ResNet-18.en.srt
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21 Transfer learning/007 Pretraining with autoencoders.en.srt
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21 Transfer learning/008 CIFAR10 with autoencoder-pretrained model.en.srt
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22 Style transfer/001 What is style transfer and how does it work_.en.srt
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22 Style transfer/002 The Gram matrix (feature activation covariance).en.srt
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22 Style transfer/003 The style transfer algorithm.en.srt
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22 Style transfer/004 Transferring the screaming bathtub.en.srt
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22 Style transfer/005 CodeChallenge_ Style transfer with AlexNet.en.srt
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23 Generative adversarial networks/001 GAN_ What, why, and how.en.srt
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23 Generative adversarial networks/002 Linear GAN with MNIST.en.srt
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23 Generative adversarial networks/003 CodeChallenge_ Linear GAN with FMNIST.en.srt
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23 Generative adversarial networks/004 CNN GAN with Gaussians.en.srt
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23 Generative adversarial networks/005 CodeChallenge_ Gaussians with fewer layers.en.srt
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23 Generative adversarial networks/006 CNN GAN with FMNIST.en.srt
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23 Generative adversarial networks/007 CodeChallenge_ CNN GAN with CIFAR.en.srt
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24 Ethics of deep learning/001 Will AI save us or destroy us_.en.srt
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24 Ethics of deep learning/002 Example case studies.en.srt
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24 Ethics of deep learning/003 Some other possible ethical scenarios.en.srt
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24 Ethics of deep learning/004 Will deep learning take our jobs_.en.srt
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24 Ethics of deep learning/005 Accountability and making ethical AI.en.srt
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25 Where to go from here_/001 How to learn topic _X_ in deep learning_.en.srt
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25 Where to go from here_/001 How to learn topic _X_ in deep learning_.mp4
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25 Where to go from here_/002 How to read academic DL papers.en.srt
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26 Bonus section/001 Bonus content.html
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27 Python intro_ Data types/001 How to learn from the Python tutorial.en.srt
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27 Python intro_ Data types/002 Variables.en.srt
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27 Python intro_ Data types/003 Math and printing.en.srt
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27 Python intro_ Data types/004 Lists (1 of 2).en.srt
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27 Python intro_ Data types/006 Tuples.en.srt
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27 Python intro_ Data types/007 Booleans.en.srt
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27 Python intro_ Data types/008 Dictionaries.en.srt
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28 Python intro_ Indexing, slicing/001 Indexing.en.srt
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28 Python intro_ Indexing, slicing/002 Slicing.en.srt
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29 Python intro_ Functions/001 Inputs and outputs.en.srt
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29 Python intro_ Functions/002 Python libraries (numpy).en.srt
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29 Python intro_ Functions/003 Python libraries (pandas).en.srt
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29 Python intro_ Functions/004 Getting help on functions.en.srt
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29 Python intro_ Functions/005 Creating functions.en.srt
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29 Python intro_ Functions/006 Global and local variable scopes.en.srt
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29 Python intro_ Functions/007 Copies and referents of variables.en.srt
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29 Python intro_ Functions/008 Classes and object-oriented programming.en.srt
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30 Python intro_ Flow control/001 If-else statements.en.srt
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30 Python intro_ Flow control/002 If-else statements, part 2.en.srt
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30 Python intro_ Flow control/003 For loops.en.srt
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30 Python intro_ Flow control/004 Enumerate and zip.en.srt
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30 Python intro_ Flow control/005 Continue.en.srt
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30 Python intro_ Flow control/006 Initializing variables.en.srt
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30 Python intro_ Flow control/007 Single-line loops (list comprehension).en.srt
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30 Python intro_ Flow control/009 Broadcasting in numpy.en.srt
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30 Python intro_ Flow control/010 Function error checking and handling.en.srt
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31 Python intro_ Text and plots/001 Printing and string interpolation.en.srt
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31 Python intro_ Text and plots/001 Printing and string interpolation.mp4
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31 Python intro_ Text and plots/002 Plotting dots and lines.en.srt
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31 Python intro_ Text and plots/003 Subplot geometry.en.srt
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31 Python intro_ Text and plots/004 Making the graphs look nicer.en.srt
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31 Python intro_ Text and plots/005 Seaborn.en.srt
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31 Python intro_ Text and plots/006 Images.en.srt
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31 Python intro_ Text and plots/007 Export plots in low and high resolution.en.srt
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