Filename | Size |
| 01 - Introduction/001 How to learn from this course.mp4 | 55 MB |
| 01 - Introduction/001 How to learn from this course_en.srt | 12.5 KB |
| 01 - Introduction/002 Using Udemy like a pro.mp4 | 54.4 MB |
| 01 - Introduction/002 Using Udemy like a pro_en.srt | 11.8 KB |
| 02 - Download all course materials/001 Downloading and using the code.mp4 | 45.6 MB |
| 02 - Download all course materials/001 Downloading and using the code_en.srt | 9.1 KB |
| 02 - Download all course materials/001 DUDL-PythonCode.zip | 660.8 KB |
| 02 - Download all course materials/002 My policy on code-sharing.mp4 | 10.2 MB |
| 02 - Download all course materials/002 My policy on code-sharing_en.srt | 2.4 KB |
| 03 - Concepts in deep learning/001 What is an artificial neural network.mp4 | 65.4 MB |
| 03 - Concepts in deep learning/001 What is an artificial neural network_en.srt | 20.5 KB |
| 03 - Concepts in deep learning/002 How models learn.mp4 | 72.8 MB |
| 03 - Concepts in deep learning/002 How models learn_en.srt | 18.1 KB |
| 03 - Concepts in deep learning/003 The role of DL in science and knowledge.mp4 | 34.8 MB |
| 03 - Concepts in deep learning/003 The role of DL in science and knowledge_en.srt | 22.5 KB |
| 03 - Concepts in deep learning/004 Running experiments to understand DL.mp4 | 74.8 MB |
| 03 - Concepts in deep learning/004 Running experiments to understand DL_en.srt | 18.5 KB |
| 03 - Concepts in deep learning/005 Are artificial neurons like biological neurons.mp4 | 114.7 MB |
| 03 - Concepts in deep learning/005 Are artificial neurons like biological neurons_en.srt | 23.3 KB |
| 04 - About the Python tutorial/001 Should you watch the Python tutorial.mp4 | 23.8 MB |
| 04 - About the Python tutorial/001 Should you watch the Python tutorial_en.srt | 5.9 KB |
| 05 - Math, numpy, PyTorch/001 PyTorch or TensorFlow.html | 1.1 KB |
| 05 - Math, numpy, PyTorch/002 Introduction to this section.mp4 | 11.1 MB |
| 05 - Math, numpy, PyTorch/002 Introduction to this section_en.srt | 2.8 KB |
| 05 - Math, numpy, PyTorch/003 Spectral theories in mathematics.mp4 | 51.1 MB |
| 05 - Math, numpy, PyTorch/003 Spectral theories in mathematics_en.srt | 13.1 KB |
| 05 - Math, numpy, PyTorch/004 Terms and datatypes in math and computers.mp4 | 38.1 MB |
| 05 - Math, numpy, PyTorch/004 Terms and datatypes in math and computers_en.srt | 10.3 KB |
| 05 - Math, numpy, PyTorch/005 Converting reality to numbers.mp4 | 33.2 MB |
| 05 - Math, numpy, PyTorch/005 Converting reality to numbers_en.srt | 9.2 KB |
| 05 - Math, numpy, PyTorch/006 Vector and matrix transpose.mp4 | 37.7 MB |
| 05 - Math, numpy, PyTorch/006 Vector and matrix transpose_en.srt | 9.6 KB |
| 05 - Math, numpy, PyTorch/007 OMG it's the dot product!.mp4 | 50.1 MB |
| 05 - Math, numpy, PyTorch/007 OMG it's the dot product!_en.srt | 13.4 KB |
| 05 - Math, numpy, PyTorch/008 Matrix multiplication.mp4 | 85.7 MB |
| 05 - Math, numpy, PyTorch/008 Matrix multiplication_en.srt | 19.8 KB |
| 05 - Math, numpy, PyTorch/009 Softmax.mp4 | 96 MB |
| 05 - Math, numpy, PyTorch/009 Softmax_en.srt | 26.7 KB |
| 05 - Math, numpy, PyTorch/010 Logarithms.mp4 | 43.9 MB |
| 05 - Math, numpy, PyTorch/010 Logarithms_en.srt | 11 KB |
| 05 - Math, numpy, PyTorch/011 Entropy and cross-entropy.mp4 | 106 MB |
| 05 - Math, numpy, PyTorch/011 Entropy and cross-entropy_en.srt | 24.5 KB |
| 05 - Math, numpy, PyTorch/012 Minmax and argminargmax.mp4 | 88.2 MB |
| 05 - Math, numpy, PyTorch/012 Minmax and argminargmax_en.srt | 17.5 KB |
| 05 - Math, numpy, PyTorch/013 Mean and variance.mp4 | 81.4 MB |
| 05 - Math, numpy, PyTorch/013 Mean and variance_en.srt | 21.7 KB |
| 05 - Math, numpy, PyTorch/014 Random sampling and sampling variability.mp4 | 85.4 MB |
| 05 - Math, numpy, PyTorch/014 Random sampling and sampling variability_en.srt | 15.8 KB |
| 05 - Math, numpy, PyTorch/015 Reproducible randomness via seeding.mp4 | 69.7 MB |
| 05 - Math, numpy, PyTorch/015 Reproducible randomness via seeding_en.srt | 11.3 KB |
| 05 - Math, numpy, PyTorch/016 The t-test.mp4 | 81.4 MB |
| 05 - Math, numpy, PyTorch/016 The t-test_en.srt | 18.7 KB |
| 05 - Math, numpy, PyTorch/017 Derivatives intuition and polynomials.mp4 | 80.3 MB |
| 05 - Math, numpy, PyTorch/017 Derivatives intuition and polynomials_en.srt | 23.5 KB |
| 05 - Math, numpy, PyTorch/018 Derivatives find minima.mp4 | 45.5 MB |
| 05 - Math, numpy, PyTorch/018 Derivatives find minima_en.srt | 11.7 KB |
| 05 - Math, numpy, PyTorch/019 Derivatives product and chain rules.mp4 | 55.6 MB |
| 05 - Math, numpy, PyTorch/019 Derivatives product and chain rules_en.srt | 13 KB |
| 06 - Gradient descent/001 Overview of gradient descent.mp4 | 68.4 MB |
| 06 - Gradient descent/001 Overview of gradient descent_en.srt | 20.1 KB |
| 06 - Gradient descent/002 What about local minima.mp4 | 67.1 MB |
| 06 - Gradient descent/002 What about local minima_en.srt | 16.5 KB |
| 06 - Gradient descent/003 Gradient descent in 1D.mp4 | 119.3 MB |
| 06 - Gradient descent/003 Gradient descent in 1D_en.srt | 23.8 KB |
| 06 - Gradient descent/004 CodeChallenge unfortunate starting value.mp4 | 77.1 MB |
| 06 - Gradient descent/004 CodeChallenge unfortunate starting value_en.srt | 15.4 KB |
| 06 - Gradient descent/005 Gradient descent in 2D.mp4 | 96.4 MB |
| 06 - Gradient descent/005 Gradient descent in 2D_en.srt | 20.7 KB |
| 06 - Gradient descent/006 CodeChallenge 2D gradient ascent.mp4 | 39.4 MB |
| 06 - Gradient descent/006 CodeChallenge 2D gradient ascent_en.srt | 7.2 KB |
| 06 - Gradient descent/007 Parametric experiments on g.d.mp4 | 135.6 MB |
| 06 - Gradient descent/007 Parametric experiments on g.d_en.srt | 26.2 KB |
| 06 - Gradient descent/008 CodeChallenge fixed vs. dynamic learning rate.mp4 | 113.6 MB |
| 06 - Gradient descent/008 CodeChallenge fixed vs. dynamic learning rate_en.srt | 22.5 KB |
| 06 - Gradient descent/009 Vanishing and exploding gradients.mp4 | 30.2 MB |
| 06 - Gradient descent/009 Vanishing and exploding gradients_en.srt | 8.7 KB |
| 06 - Gradient descent/010 Tangent Notebook revision history.mp4 | 9.9 MB |
| 06 - Gradient descent/010 Tangent Notebook revision history_en.srt | 2.7 KB |
| 07 - ANNs (Artificial Neural Networks)/001 The perceptron and ANN architecture.mp4 | 85.8 MB |
| 07 - ANNs (Artificial Neural Networks)/001 The perceptron and ANN architecture_en.srt | 26.9 KB |
| 07 - ANNs (Artificial Neural Networks)/002 A geometric view of ANNs.mp4 | 70.9 MB |
| 07 - ANNs (Artificial Neural Networks)/002 A geometric view of ANNs_en.srt | 18.7 KB |
| 07 - ANNs (Artificial Neural Networks)/003 ANN math part 1 (forward prop).mp4 | 73.1 MB |
| 07 - ANNs (Artificial Neural Networks)/003 ANN math part 1 (forward prop)_en.srt | 21.4 KB |
| 07 - ANNs (Artificial Neural Networks)/004 ANN math part 2 (errors, loss, cost).mp4 | 48.5 MB |
| 07 - ANNs (Artificial Neural Networks)/004 ANN math part 2 (errors, loss, cost)_en.srt | 13.4 KB |
| 07 - ANNs (Artificial Neural Networks)/005 ANN math part 3 (backprop).mp4 | 52.9 MB |
| 07 - ANNs (Artificial Neural Networks)/005 ANN math part 3 (backprop)_en.srt | 14.7 KB |
| 07 - ANNs (Artificial Neural Networks)/006 ANN for regression.mp4 | 135.5 MB |
| 07 - ANNs (Artificial Neural Networks)/006 ANN for regression_en.srt | 34.5 KB |
| 07 - ANNs (Artificial Neural Networks)/007 CodeChallenge manipulate regression slopes.mp4 | 139.1 MB |
| 07 - ANNs (Artificial Neural Networks)/007 CodeChallenge manipulate regression slopes_en.srt | 27.3 KB |
| 07 - ANNs (Artificial Neural Networks)/008 ANN for classifying qwerties.mp4 | 151.1 MB |