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1. Introduction.mp4
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MP4
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72 MB
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1. Introduction.srt
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SRT
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6.7 KB
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1. Linear regression and MSE loss.mp4
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MP4
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18 MB
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1. Linear regression and MSE loss.srt
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SRT
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11 KB
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1. Setting up a coding environment using Anaconda and Jupyter Notebook in Vscode.mp4
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MP4
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33.6 MB
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1. Setting up a coding environment using Anaconda and Jupyter Notebook in Vscode.srt
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SRT
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7.5 KB
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1. The back propagation algorithm.mp4
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MP4
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14.4 MB
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1. The back propagation algorithm.srt
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SRT
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8.1 KB
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1. Vanishing gradient problem.mp4
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MP4
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38.3 MB
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1. Vanishing gradient problem.srt
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SRT
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20.9 KB
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1.1 lecture23.pdf
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PDF
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1.6 MB
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1.1 lecture3.pdf
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PDF
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1.3 MB
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10. Computational graph III - backward pass II.mp4
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MP4
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63.5 MB
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10. Computational graph III - backward pass II.srt
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SRT
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14.5 KB
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10. Next steps.mp4
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MP4
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110.6 MB
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10. Next steps.srt
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SRT
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28.3 KB
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10. Overfitting II - regularization and drop out.mp4
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MP4
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25.4 MB
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10. Overfitting II - regularization and drop out.srt
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SRT
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14.4 KB
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10. Scalability and emergent properties.mp4
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MP4
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25.4 MB
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10. Scalability and emergent properties.srt
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SRT
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12.8 KB
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10.1 lecture12.pdf
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PDF
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846.3 KB
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10.1 lecture20_2.pdf
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PDF
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578.1 KB
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10.1 lecture30.pdf
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PDF
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1.4 MB
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11. Computational graph IV - backward pass III.mp4
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MP4
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82.7 MB
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11. Computational graph IV - backward pass III.srt
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SRT
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23.4 KB
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11. Recap of the forward pass and brief introduction to backward pass.mp4
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MP4
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11.3 MB
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11. Recap of the forward pass and brief introduction to backward pass.srt
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SRT
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6.5 KB
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11. Softmax activation.mp4
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MP4
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28.8 MB
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11. Softmax activation.srt
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SRT
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12.9 KB
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11.1 lecture13.pdf
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PDF
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525.4 KB
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11.1 lecture21.pdf
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PDF
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1.1 MB
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12. Forward and backward pass recap and wrap up.mp4
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MP4
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46 MB
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12. Forward and backward pass recap and wrap up.srt
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SRT
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13.1 KB
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12. Loss functions.mp4
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MP4
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11.6 MB
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12. Loss functions.srt
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SRT
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8.4 KB
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12.1 lecture22.pdf
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PDF
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1.2 MB
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13. Cross entropy loss.mp4
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MP4
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26 MB
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13. Cross entropy loss.srt
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SRT
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15.2 KB
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2. Calculus detour.mp4
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MP4
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37.1 MB
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2. Calculus detour.srt
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SRT
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17.6 KB
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2. Numerical analysis - a.k.a. “trial-and-error”.mp4
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MP4
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18.8 MB
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2. Numerical analysis - a.k.a. “trial-and-error”.srt
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SRT
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10.5 KB
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2. Train an MNIST model from scratch in plain PyTorch I.mp4
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MP4
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96.2 MB
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2. Train an MNIST model from scratch in plain PyTorch I.srt
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SRT
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19.5 KB
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2. Vanishing gradient solutions I.mp4
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MP4
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22.4 MB
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2. Vanishing gradient solutions I.srt
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SRT
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17.4 KB
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2. What is Machine Learning exactly.mp4
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MP4
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12.4 MB
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2. What is Machine Learning exactly.srt
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SRT
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8 KB
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2.1 lecture1.pdf
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PDF
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351 KB
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2.1 lecture15.pdf
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PDF
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1.3 MB
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2.1 lecture24.pdf
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PDF
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1.1 MB
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2.1 lecture4.pdf
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PDF
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751.9 KB
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3. Calculus detour II.mp4
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MP4
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15.9 MB
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3. Calculus detour II.srt
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SRT
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10.1 KB
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3. Different types of machine learning supervised, unsupervised, and reinforcement.mp4
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MP4
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25.9 MB
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3. Different types of machine learning supervised, unsupervised, and reinforcement.srt
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SRT
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17.1 KB
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3. Network view.mp4
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MP4
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45.3 MB
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3. Network view.srt
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SRT
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17.1 KB
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3. Train an MNIST model from scratch in plain PyTorch II.mp4
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MP4
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96.3 MB
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3. Train an MNIST model from scratch in plain PyTorch II.srt
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SRT
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16.7 KB
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3. Vanishing gradient solutions II.mp4
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MP4
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17.5 MB
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3. Vanishing gradient solutions II.srt
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SRT
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9.9 KB
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3.1 lecture15_2.pdf
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PDF
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844.1 KB
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3.1 lecture2.pdf
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PDF
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1.3 MB
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3.1 lecture24_2.pdf
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PDF
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764.2 KB
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3.1 lecture5.pdf
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PDF
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863.8 KB
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4. Gradient descent.mp4
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MP4
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101 MB
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4. Gradient descent.srt
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SRT
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23.6 KB
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4. Perceptrons.mp4
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MP4
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15.5 MB
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4. Perceptrons.srt
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SRT
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9.8 KB
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4. Stochastic and mini-batch gradient descent.mp4
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MP4
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39.6 MB
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4. Stochastic and mini-batch gradient descent.srt
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SRT
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21.9 KB
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4. The big picture.mp4
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MP4
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24.3 MB
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4. The big picture.srt
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SRT
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7.1 KB
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4. Train an MNIST model from scratch in plain PyTorch III.mp4
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MP4
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102 MB
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4. Train an MNIST model from scratch in plain PyTorch III.srt
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SRT
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22.4 KB
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4.1 lecture16.pdf
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PDF
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929.5 KB
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4.1 lecture25.pdf
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PDF
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1.2 MB
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4.1 lecture2_2.pdf
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PDF
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88.9 KB
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4.1 lecture6.pdf
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PDF
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934.7 KB
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5. Calculus detour - partial derivatives and gradient descent.mp4
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MP4
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42.2 MB
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5. Calculus detour - partial derivatives and gradient descent.srt
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SRT
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11.2 KB
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5. Deep neural network as features and weights.mp4
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MP4
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32.7 MB
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5. Deep neural network as features and weights.srt
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SRT
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11.5 KB
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5. Other optimizers I.mp4
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MP4
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33.3 MB
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5. Other optimizers I.srt
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SRT
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13.2 KB
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5. The “Deep” in deep learning.mp4
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MP4
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25.1 MB
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5. The “Deep” in deep learning.srt
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SRT
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11.6 KB
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5. Train an MNIST model from scratch in plain PyTorch IV.mp4
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MP4
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75.6 MB
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5. Train an MNIST model from scratch in plain PyTorch IV.srt
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SRT
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22 KB
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5.1 lecture17.pdf
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PDF
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1.3 MB
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5.1 lecture26.pdf
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PDF
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537.4 KB
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5.1 lecture2_3.pdf
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PDF
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486.3 KB
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5.1 lecture7.pdf
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PDF
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1.2 MB
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6. Activation Function.mp4
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MP4
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17.5 MB
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6. Activation Function.srt
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SRT
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11.6 KB
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6. Calculus detour - the Chain Rule.mp4
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MP4
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38.2 MB
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6. Calculus detour - the Chain Rule.srt
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SRT
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20.1 KB
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6. Loss functions and training vs inference.mp4
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MP4
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35.8 MB
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6. Loss functions and training vs inference.srt
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SRT
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11.9 KB
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6. Other optimizers II.mp4
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MP4
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11.6 MB
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6. Other optimizers II.srt
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SRT
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7.5 KB
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6. Train an MNIST model using PyTorch's nn module I.mp4
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MP4
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84.9 MB
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6. Train an MNIST model using PyTorch's nn module I.srt
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SRT
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21.2 KB
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6.1 lecture18.pdf
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PDF
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1.4 MB
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6.1 lecture26_2.pdf
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PDF
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305.2 KB
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6.1 lecture8.pdf
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PDF
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899.8 KB
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7. Calculus detour - the Chain Rule II.mp4
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MP4
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36.4 MB
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7. Calculus detour - the Chain Rule II.srt
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SRT
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21.1 KB
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7. Hyperparameter tuning strategies.mp4
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MP4
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27.8 MB
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7. Hyperparameter tuning strategies.srt
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SRT
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12 KB
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7. Overparameterization and overfitting.mp4
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MP4
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20 MB
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7. Overparameterization and overfitting.srt
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SRT
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10.5 KB
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7. Train an MNIST model using PyTorch's nn module II.mp4
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MP4
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102.1 MB
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7. Train an MNIST model using PyTorch's nn module II.srt
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SRT
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22.6 KB
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7. Why deep learning is unintuitive and how to get good at it.mp4
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MP4
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14.1 MB
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7. Why deep learning is unintuitive and how to get good at it.srt
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SRT
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10.2 KB
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7.1 lecture18_2.pdf
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PDF
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1.2 MB
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7.1 lecture27.pdf
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PDF
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720.8 KB
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7.1 lecture2_5.pdf
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PDF
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760.1 KB
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7.1 lecture9.pdf
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PDF
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988.9 KB
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8. Batch normalization.mp4
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MP4
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43.9 MB
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8. Batch normalization.srt
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SRT
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13.4 KB
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8. Computational graph I - forward pass.mp4
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MP4
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15.1 MB
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8. Computational graph I - forward pass.srt
|
SRT
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8.6 KB
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8. How to make neural networks feel intuitive.mp4
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MP4
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18.3 MB
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8. How to make neural networks feel intuitive.srt
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SRT
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8.3 KB
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8. Linear Algebra detour.mp4
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MP4
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33.1 MB
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8. Linear Algebra detour.srt
|
SRT
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18.9 KB
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8. Train an MNIST model using PyTorch Lightning I.mp4
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MP4
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83 MB
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8. Train an MNIST model using PyTorch Lightning I.srt
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SRT
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16.1 KB
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8.1 lecture10.pdf
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PDF
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838.4 KB
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8.1 lecture19.pdf
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PDF
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503.6 KB
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8.1 lecture28.pdf
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PDF
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952.4 KB
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8.1 lecture2_6.pdf
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PDF
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1.5 MB
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9. Computational graph II - backward pass.mp4
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MP4
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48.1 MB
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|
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9. Computational graph II - backward pass.srt
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SRT
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13.5 KB
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9. Course overview.mp4
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MP4
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13.8 MB
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9. Course overview.srt
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SRT
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9.7 KB
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9. Overfitting I - problem and solution overview.mp4
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MP4
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31.2 MB
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9. Overfitting I - problem and solution overview.srt
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SRT
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17.3 KB
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9. Train an MNIST model using PyTorch Lightning II.mp4
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MP4
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118.4 MB
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9. Train an MNIST model using PyTorch Lightning II.srt
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SRT
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22.5 KB
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9. Vectorization (= parallelization).mp4
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MP4
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29.3 MB
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9. Vectorization (= parallelization).srt
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SRT
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13.7 KB
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9.1 lecture20.pdf
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PDF
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592.6 KB
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9.1 lecture29.pdf
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PDF
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1.5 MB
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9.1 lecture2_7.pdf
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PDF
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931.7 KB
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Bonus Resources.txt
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TXT
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409.6 B
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Get Bonus Downloads Here.url
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URL
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204.8 B
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