FreeCourseWeb com Udemy Finally GET Deep Learning | 3C738248A024EDD68569307EBBDC69A33348DC2F

FreeCourseWeb com Udemy Finally GET Deep Learning

Name	[ FreeCourseWeb.com ] Udemy - Finally GET Deep Learning	DOWNLOAD Copy Link
Total Size	2.5 GB
Total Files	152
Last Seen	2024-07-08 23:51
Hash	3C738248A024EDD68569307EBBDC69A33348DC2F

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Get Bonus Downloads Here.url	0.2 KB
/.../01 Deep learning - the big picture/
001 Introduction.en.srt	7.2 KB
001 Introduction.mp4	75.5 MB
002 What is Machine Learning exactly_.en.srt	8.5 KB
002 What is Machine Learning exactly_.mp4	13.0 MB
002 lecture1.pdf	359.4 KB
003 Different types of machine learning_ supervised, unsupervised, and reinforcement.en.srt	18.2 KB
003 Different types of machine learning_ supervised, unsupervised, and reinforcement.mp4	27.1 MB
003 lecture2.pdf	1.3 MB
004 The big picture.en.srt	7.5 KB
004 The big picture.mp4	25.5 MB
004 lecture2_2.pdf	91.0 KB
005 Deep neural network as features and weights.en.srt	12.3 KB
005 Deep neural network as features and weights.mp4	34.3 MB
005 lecture2_3.pdf	498.0 KB
006 Loss functions and training vs inference.en.srt	12.6 KB
006 Loss functions and training vs inference.mp4	37.5 MB
006 lecture2_4.pdf	1.0 MB
007 Why deep learning is unintuitive and how to get good at it.en.srt	10.8 KB
007 Why deep learning is unintuitive and how to get good at it.mp4	14.8 MB
007 lecture2_5.pdf	778.4 KB
008 How to make neural networks feel intuitive.en.srt	8.8 KB
008 How to make neural networks feel intuitive.mp4	19.2 MB
008 lecture2_6.pdf	1.5 MB
009 Course overview.en.srt	10.2 KB
009 Course overview.mp4	14.4 MB
009 lecture2_7.pdf	954.1 KB
/.../02 Reinventing deep neural network from scratch/
001 Linear regression and MSE loss.en.srt	11.7 KB
001 Linear regression and MSE loss.mp4	18.8 MB
002 Numerical analysis - a.k.a. “trial-and-error”.en.srt	11.1 KB
002 Numerical analysis - a.k.a. “trial-and-error”.mp4	19.7 MB
003 Network view.en.srt	18.2 KB
003 Network view.mp4	47.6 MB
004 Perceptrons.en.srt	10.5 KB
004 Perceptrons.mp4	16.3 MB
005 The “Deep” in deep learning.en.srt	12.3 KB
005 The “Deep” in deep learning.mp4	26.3 MB
006 Activation Function.en.srt	12.3 KB
006 Activation Function.mp4	18.4 MB
007 Overparameterization and overfitting.en.srt	11.2 KB
007 Overparameterization and overfitting.mp4	21.0 MB
008 Linear Algebra detour.en.srt	20.1 KB
008 Linear Algebra detour.mp4	34.7 MB
009 Vectorization (= parallelization).en.srt	14.6 KB
009 Vectorization (= parallelization).mp4	30.7 MB
010 Scalability and emergent properties.en.srt	13.6 KB
010 Scalability and emergent properties.mp4	26.7 MB
010 lecture3.pdf	1.4 MB
011 Recap of the forward pass and brief introduction to backward pass.en.srt	6.9 KB
011 Recap of the forward pass and brief introduction to backward pass.mp4	11.8 MB
011 lecture4.pdf	770.0 KB
012 lecture5.pdf	884.6 KB
013 lecture6.pdf	957.1 KB
014 lecture7.pdf	1.3 MB
015 lecture8.pdf	921.4 KB
016 lecture9.pdf	1.0 MB
017 lecture10.pdf	858.5 KB
018 lecture11.pdf	1.0 MB
019 lecture12.pdf	866.7 KB
020 lecture13.pdf	538.0 KB
/.../03 How the model learns on its own - Back Propagation algorithm deep-div/
001 The back propagation algorithm.en.srt	8.6 KB
001 The back propagation algorithm.mp4	15.1 MB
002 Calculus detour.en.srt	18.8 KB
002 Calculus detour.mp4	38.9 MB
003 Calculus detour II.en.srt	10.8 KB
003 Calculus detour II.mp4	16.6 MB
004 Gradient descent.en.srt	25.1 KB
004 Gradient descent.mp4	105.9 MB
005 Calculus detour - partial derivatives and gradient descent.en.srt	12.0 KB
005 Calculus detour - partial derivatives and gradient descent.mp4	44.2 MB
006 Calculus detour - the Chain Rule.en.srt	21.5 KB
006 Calculus detour - the Chain Rule.mp4	40.1 MB
007 Calculus detour - the Chain Rule II.en.srt	22.5 KB
007 Calculus detour - the Chain Rule II.mp4	38.2 MB
008 Computational graph I - forward pass.en.srt	9.2 KB
008 Computational graph I - forward pass.mp4	15.8 MB
009 Computational graph II - backward pass.en.srt	14.4 KB
009 Computational graph II - backward pass.mp4	50.4 MB
010 Computational graph III - backward pass II.en.srt	15.4 KB
010 Computational graph III - backward pass II.mp4	66.6 MB
011 Computational graph IV - backward pass III.en.srt	25.0 KB
011 Computational graph IV - backward pass III.mp4	86.8 MB
012 Forward and backward pass recap and wrap up.en.srt	13.9 KB
012 Forward and backward pass recap and wrap up.mp4	48.2 MB
022 lecture15.pdf	1.4 MB
023 lecture15_2.pdf	864.3 KB
024 lecture16.pdf	951.8 KB
025 lecture17.pdf	1.3 MB
026 lecture18.pdf	1.5 MB
027 lecture18_2.pdf	1.2 MB
028 lecture19.pdf	515.7 KB
029 lecture20.pdf	606.8 KB
030 lecture20_2.pdf	591.9 KB
031 lecture21.pdf	1.2 MB
032 lecture22.pdf	1.2 MB
/.../04 How to make neural networks work in reality/
001 Vanishing gradient problem.en.srt	22.2 KB
001 Vanishing gradient problem.mp4	40.2 MB
002 Vanishing gradient solutions I.en.srt	18.4 KB
002 Vanishing gradient solutions I.mp4	23.5 MB
003 Vanishing gradient solutions II.en.srt	10.5 KB
003 Vanishing gradient solutions II.mp4	18.4 MB
004 Stochastic and mini-batch gradient descent.en.srt	23.3 KB
004 Stochastic and mini-batch gradient descent.mp4	41.6 MB
005 Other optimizers I.en.srt	14.1 KB
005 Other optimizers I.mp4	34.9 MB
006 Other optimizers II.en.srt	8.0 KB
006 Other optimizers II.mp4	12.2 MB
007 Hyperparameter tuning strategies.en.srt	12.8 KB
007 Hyperparameter tuning strategies.mp4	29.1 MB
008 Batch normalization.en.srt	14.3 KB
008 Batch normalization.mp4	46.0 MB
009 Overfitting I - problem and solution overview.en.srt	18.3 KB
009 Overfitting I - problem and solution overview.mp4	32.7 MB
010 Overfitting II - regularization and drop out.en.srt	15.3 KB
010 Overfitting II - regularization and drop out.mp4	26.6 MB
011 Softmax activation.en.srt	13.8 KB
011 Softmax activation.mp4	30.2 MB
012 Loss functions.en.srt	8.9 KB
012 Loss functions.mp4	12.2 MB
013 Cross entropy loss.en.srt	16.2 KB
013 Cross entropy loss.mp4	27.2 MB
033 lecture23.pdf	1.7 MB
034 lecture24.pdf	1.1 MB
035 lecture24_2.pdf	782.5 KB
036 lecture25.pdf	1.2 MB
037 lecture26.pdf	550.3 KB
038 lecture26_2.pdf	312.6 KB
039 lecture27.pdf	738.1 KB
040 lecture28.pdf	975.2 KB
041 lecture29.pdf	1.6 MB
042 lecture30.pdf	1.5 MB
/.../05 Coding deep neural networks in PyTorch and PyTorch Lightning/
001 Setting up a coding environment using Anaconda and Jupyter Notebook in Vscode.en.srt	8.0 KB
001 Setting up a coding environment using Anaconda and Jupyter Notebook in Vscode.mp4	35.2 MB
002 Train an MNIST model from scratch in plain PyTorch I.en.srt	20.9 KB
002 Train an MNIST model from scratch in plain PyTorch I.mp4	100.9 MB
003 Train an MNIST model from scratch in plain PyTorch II.en.srt	17.8 KB
003 Train an MNIST model from scratch in plain PyTorch II.mp4	101.0 MB
004 Train an MNIST model from scratch in plain PyTorch III.en.srt	24.0 KB
004 Train an MNIST model from scratch in plain PyTorch III.mp4	107.0 MB
005 Train an MNIST model from scratch in plain PyTorch IV.en.srt	23.6 KB
005 Train an MNIST model from scratch in plain PyTorch IV.mp4	79.3 MB
006 Train an MNIST model using PyTorch's nn module I.en.srt	22.6 KB
006 Train an MNIST model using PyTorch's nn module I.mp4	89.1 MB
007 Train an MNIST model using PyTorch's nn module II.en.srt	24.2 KB
007 Train an MNIST model using PyTorch's nn module II.mp4	107.1 MB
008 Train an MNIST model using PyTorch Lightning I.en.srt	17.3 KB
008 Train an MNIST model using PyTorch Lightning I.mp4	87.0 MB
009 Train an MNIST model using PyTorch Lightning II.en.srt	24.1 KB
009 Train an MNIST model using PyTorch Lightning II.mp4	124.2 MB
010 Next steps.en.srt	30.2 KB
010 Next steps.mp4	115.9 MB
/~Get Your Files Here !/
Bonus Resources.txt	0.4 KB
Total files 152