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001 Introduction.en.srt
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7 KB
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001 Introduction.mp4
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72 MB
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001 Linear regression and MSE loss.en.srt
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11.4 KB
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001 Linear regression and MSE loss.mp4
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18 MB
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001 Setting up a coding environment using Anaconda and Jupyter Notebook in Vscode.en.srt
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7.8 KB
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001 Setting up a coding environment using Anaconda and Jupyter Notebook in Vscode.mp4
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33.6 MB
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001 The back propagation algorithm.en.srt
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8.4 KB
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001 The back propagation algorithm.mp4
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14.4 MB
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001 Vanishing gradient problem.en.srt
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21.7 KB
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001 Vanishing gradient problem.mp4
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38.3 MB
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002 Calculus detour.en.srt
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18.4 KB
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002 Calculus detour.mp4
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37.1 MB
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002 Numerical analysis - a.k.a. “trial-and-error”.en.srt
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10.9 KB
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002 Numerical analysis - a.k.a. “trial-and-error”.mp4
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18.8 MB
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002 Train an MNIST model from scratch in plain PyTorch I.en.srt
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20.4 KB
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002 Train an MNIST model from scratch in plain PyTorch I.mp4
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96.2 MB
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002 Vanishing gradient solutions I.en.srt
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18 KB
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002 Vanishing gradient solutions I.mp4
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22.4 MB
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002 What is Machine Learning exactly_.en.srt
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8.3 KB
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002 What is Machine Learning exactly_.mp4
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12.4 MB
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002 lecture1.pdf
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351 KB
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003 Calculus detour II.en.srt
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10.5 KB
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003 Calculus detour II.mp4
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15.9 MB
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003 Different types of machine learning_ supervised, unsupervised, and reinforcement.en.srt
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17.8 KB
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003 Different types of machine learning_ supervised, unsupervised, and reinforcement.mp4
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25.9 MB
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003 Network view.en.srt
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17.8 KB
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003 Network view.mp4
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45.3 MB
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003 Train an MNIST model from scratch in plain PyTorch II.en.srt
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17.4 KB
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003 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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003 Vanishing gradient solutions II.en.srt
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10.3 KB
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003 Vanishing gradient solutions II.mp4
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17.5 MB
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003 lecture2.pdf
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1.3 MB
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004 Gradient descent.en.srt
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24.5 KB
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004 Gradient descent.mp4
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101 MB
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004 Perceptrons.en.srt
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10.2 KB
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004 Perceptrons.mp4
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15.5 MB
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004 Stochastic and mini-batch gradient descent.en.srt
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22.7 KB
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004 Stochastic and mini-batch gradient descent.mp4
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39.6 MB
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004 The big picture.en.srt
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7.4 KB
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004 The big picture.mp4
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24.3 MB
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004 Train an MNIST model from scratch in plain PyTorch III.en.srt
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23.5 KB
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004 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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004 lecture2_2.pdf
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88.9 KB
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005 Calculus detour - partial derivatives and gradient descent.en.srt
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11.7 KB
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005 Calculus detour - partial derivatives and gradient descent.mp4
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42.1 MB
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005 Deep neural network as features and weights.en.srt
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12 KB
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005 Deep neural network as features and weights.mp4
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32.7 MB
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005 Other optimizers I.en.srt
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13.7 KB
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005 Other optimizers I.mp4
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33.3 MB
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005 The “Deep” in deep learning.en.srt
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12 KB
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005 The “Deep” in deep learning.mp4
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25.1 MB
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005 Train an MNIST model from scratch in plain PyTorch IV.en.srt
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23 KB
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005 Train an MNIST model from scratch in plain PyTorch IV.mp4
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75.6 MB
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005 lecture2_3.pdf
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486.3 KB
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006 Activation Function.en.srt
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12 KB
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006 Activation Function.mp4
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MP4
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17.5 MB
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006 Calculus detour - the Chain Rule.en.srt
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21 KB
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006 Calculus detour - the Chain Rule.mp4
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MP4
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38.2 MB
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006 Loss functions and training vs inference.en.srt
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12.3 KB
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006 Loss functions and training vs inference.mp4
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35.8 MB
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006 Other optimizers II.en.srt
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7.8 KB
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006 Other optimizers II.mp4
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11.6 MB
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006 Train an MNIST model using PyTorch's nn module I.en.srt
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22.1 KB
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006 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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007 Calculus detour - the Chain Rule II.en.srt
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22 KB
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007 Calculus detour - the Chain Rule II.mp4
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36.4 MB
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007 Hyperparameter tuning strategies.en.srt
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12.5 KB
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007 Hyperparameter tuning strategies.mp4
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27.8 MB
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007 Overparameterization and overfitting.en.srt
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10.9 KB
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007 Overparameterization and overfitting.mp4
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MP4
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20 MB
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007 Train an MNIST model using PyTorch's nn module II.en.srt
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23.6 KB
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007 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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007 Why deep learning is unintuitive and how to get good at it.en.srt
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10.6 KB
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007 Why deep learning is unintuitive and how to get good at it.mp4
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14.1 MB
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007 lecture2_5.pdf
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760.1 KB
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008 Batch normalization.en.srt
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14 KB
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008 Batch normalization.mp4
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MP4
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43.9 MB
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008 Computational graph I - forward pass.en.srt
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9 KB
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008 Computational graph I - forward pass.mp4
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15.1 MB
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008 How to make neural networks feel intuitive.en.srt
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8.6 KB
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008 How to make neural networks feel intuitive.mp4
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18.3 MB
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008 Linear Algebra detour.en.srt
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19.7 KB
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008 Linear Algebra detour.mp4
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MP4
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33.1 MB
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008 Train an MNIST model using PyTorch Lightning I.en.srt
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16.9 KB
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008 Train an MNIST model using PyTorch Lightning I.mp4
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MP4
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83 MB
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008 lecture2_6.pdf
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1.5 MB
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009 Computational graph II - backward pass.en.srt
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14.1 KB
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009 Computational graph II - backward pass.mp4
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MP4
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48.1 MB
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009 Course overview.en.srt
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10 KB
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009 Course overview.mp4
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MP4
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13.8 MB
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009 Overfitting I - problem and solution overview.en.srt
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17.9 KB
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009 Overfitting I - problem and solution overview.mp4
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MP4
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31.2 MB
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009 Train an MNIST model using PyTorch Lightning II.en.srt
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23.5 KB
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009 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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009 Vectorization (= parallelization).en.srt
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14.3 KB
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009 Vectorization (= parallelization).mp4
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MP4
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29.3 MB
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009 lecture2_7.pdf
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PDF
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931.7 KB
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010 Computational graph III - backward pass II.en.srt
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15.1 KB
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010 Computational graph III - backward pass II.mp4
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MP4
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63.5 MB
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010 Next steps.en.srt
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29.5 KB
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010 Next steps.mp4
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MP4
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110.6 MB
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010 Overfitting II - regularization and drop out.en.srt
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15 KB
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010 Overfitting II - regularization and drop out.mp4
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MP4
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25.4 MB
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010 Scalability and emergent properties.en.srt
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13.3 KB
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010 Scalability and emergent properties.mp4
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MP4
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25.4 MB
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010 lecture3.pdf
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PDF
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1.3 MB
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011 Computational graph IV - backward pass III.en.srt
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24.4 KB
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011 Computational graph IV - backward pass III.mp4
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MP4
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82.7 MB
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011 Recap of the forward pass and brief introduction to backward pass.en.srt
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6.7 KB
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011 Recap of the forward pass and brief introduction to backward pass.mp4
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11.3 MB
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011 Softmax activation.en.srt
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13.4 KB
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011 Softmax activation.mp4
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MP4
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28.8 MB
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011 lecture4.pdf
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751.9 KB
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012 Forward and backward pass recap and wrap up.en.srt
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13.6 KB
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012 Forward and backward pass recap and wrap up.mp4
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46 MB
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012 Loss functions.en.srt
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8.7 KB
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012 Loss functions.mp4
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MP4
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11.6 MB
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012 lecture5.pdf
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863.8 KB
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013 Cross entropy loss.en.srt
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15.8 KB
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013 Cross entropy loss.mp4
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MP4
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26 MB
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013 lecture6.pdf
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934.7 KB
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014 lecture7.pdf
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1.2 MB
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015 lecture8.pdf
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899.8 KB
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016 lecture9.pdf
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988.9 KB
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017 lecture10.pdf
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838.4 KB
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019 lecture12.pdf
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846.3 KB
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020 lecture13.pdf
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525.4 KB
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022 lecture15.pdf
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1.3 MB
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023 lecture15_2.pdf
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844.1 KB
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024 lecture16.pdf
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929.5 KB
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025 lecture17.pdf
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1.3 MB
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026 lecture18.pdf
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1.4 MB
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027 lecture18_2.pdf
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1.2 MB
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028 lecture19.pdf
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503.6 KB
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029 lecture20.pdf
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592.6 KB
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030 lecture20_2.pdf
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578.1 KB
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031 lecture21.pdf
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1.1 MB
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032 lecture22.pdf
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1.2 MB
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033 lecture23.pdf
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1.6 MB
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034 lecture24.pdf
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1.1 MB
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035 lecture24_2.pdf
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764.2 KB
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036 lecture25.pdf
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1.2 MB
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037 lecture26.pdf
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537.4 KB
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038 lecture26_2.pdf
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305.2 KB
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039 lecture27.pdf
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720.8 KB
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040 lecture28.pdf
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952.4 KB
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041 lecture29.pdf
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1.5 MB
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042 lecture30.pdf
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1.4 MB
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Bonus Resources.txt
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TXT
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307.2 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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![[ FreeCourseWeb ] Udemy - Finally GET Deep Learning](/static/img/no-torrent-image.png "[ FreeCourseWeb ] Udemy - Finally GET Deep Learning")
