[ FreeCourseWeb ] Udemy - Finally GET Deep Learning

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 01 Deep learning - the big picture
• 001 Introduction.en.srt (7.0 KB)
• 001 Introduction.mp4 (72.0 MB)
• 002 What is Machine Learning exactly_.en.srt (8.3 KB)
• 002 What is Machine Learning exactly_.mp4 (12.4 MB)
• 002 lecture1.pdf (351.0 KB)
• 003 Different types of machine learning_ supervised, unsupervised, and reinforcement.en.srt (17.8 KB)
• 003 Different types of machine learning_ supervised, unsupervised, and reinforcement.mp4 (25.9 MB)
• 003 lecture2.pdf (1.3 MB)
• 004 The big picture.en.srt (7.4 KB)
• 004 The big picture.mp4 (24.3 MB)
• 004 lecture2_2.pdf (88.9 KB)
• 005 Deep neural network as features and weights.en.srt (12.0 KB)
• 005 Deep neural network as features and weights.mp4 (32.7 MB)
• 005 lecture2_3.pdf (486.3 KB)
• 006 Loss functions and training vs inference.en.srt (12.3 KB)
• 006 Loss functions and training vs inference.mp4 (35.8 MB)
• 006 lecture2_4.pdf (1,004.4 KB)
• 007 Why deep learning is unintuitive and how to get good at it.en.srt (10.6 KB)
• 007 Why deep learning is unintuitive and how to get good at it.mp4 (14.1 MB)
• 007 lecture2_5.pdf (760.1 KB)
• 008 How to make neural networks feel intuitive.en.srt (8.6 KB)
• 008 How to make neural networks feel intuitive.mp4 (18.3 MB)
• 008 lecture2_6.pdf (1.5 MB)
• 009 Course overview.en.srt (10.0 KB)
• 009 Course overview.mp4 (13.8 MB)
• 009 lecture2_7.pdf (931.7 KB)
02 Reinventing deep neural network from scratch
• 001 Linear regression and MSE loss.en.srt (11.4 KB)
• 001 Linear regression and MSE loss.mp4 (18.0 MB)
• 002 Numerical analysis - a.k.a. “trial-and-error”.en.srt (10.9 KB)
• 002 Numerical analysis - a.k.a. “trial-and-error”.mp4 (18.8 MB)
• 003 Network view.en.srt (17.8 KB)
• 003 Network view.mp4 (45.3 MB)
• 004 Perceptrons.en.srt (10.2 KB)
• 004 Perceptrons.mp4 (15.5 MB)
• 005 The “Deep” in deep learning.en.srt (12.0 KB)
• 005 The “Deep” in deep learning.mp4 (25.1 MB)
• 006 Activation Function.en.srt (12.0 KB)
• 006 Activation Function.mp4 (17.5 MB)
• 007 Overparameterization and overfitting.en.srt (10.9 KB)
• 007 Overparameterization and overfitting.mp4 (20.0 MB)
• 008 Linear Algebra detour.en.srt (19.7 KB)
• 008 Linear Algebra detour.mp4 (33.1 MB)
• 009 Vectorization (= parallelization).en.srt (14.3 KB)
• 009 Vectorization (= parallelization).mp4 (29.3 MB)
• 010 Scalability and emergent properties.en.srt (13.3 KB)
• 010 Scalability and emergent properties.mp4 (25.4 MB)
• 010 lecture3.pdf (1.3 MB)
• 011 Recap of the forward pass and brief introduction to backward pass.en.srt (6.7 KB)
• 011 Recap of the forward pass and brief introduction to backward pass.mp4 (11.3 MB)
• 011 lecture4.pdf (751.9 KB)
• 012 lecture5.pdf (863.8 KB)
• 013 lecture6.pdf (934.7 KB)
• 014 lecture7.pdf (1.2 MB)
• 015 lecture8.pdf (899.8 KB)
• 016 lecture9.pdf (988.9 KB)
• 017 lecture10.pdf (838.4 KB)
• 018 lecture11.pdf (1,002.8 KB)
• 019 lecture12.pdf (846.3 KB)
• 020 lecture13.pdf (525.4 KB)
03 How the model learns on its own - Back Propagation algorithm deep-div
• 001 The back propagation algorithm.en.srt (8.4 KB)
• 001 The back propagation algorithm.mp4 (14.4 MB)
• 002 Calculus detour.en.srt (18.4 KB)
• 002 Calculus detour.mp4 (37.1 MB)
• 003 Calculus detour II.en.srt (10.5 KB)
• 003 Calculus detour II.mp4 (15.9 MB)
• 004 Gradient descent.en.srt (24.5 KB)
• 004 Gradient descent.mp4 (101.0 MB)
• 005 Calculus detour - partial derivatives and gradient descent.en.srt (11.7 KB)
• 005 Calculus detour - partial derivatives and gradient descent.mp4 (42.1 MB)
• 006 Calculus detour - the Chain Rule.en.srt (21.0 KB)
• 006 Calculus detour - the Chain Rule.mp4 (38.2 MB)
• 007 Calculus detour - the Chain Rule II.en.srt (22.0 KB)
• 007 Calculus detour - the Chain Rule II.mp4 (36.4 MB)
• 008 Computational graph I - forward pass.en.srt (9.0 KB)
• 008 Computational graph I - forward pass.mp4 (15.1 MB)
• 009 Computational graph II - backward pass.en.srt (14.1 KB)
• 009 Computational graph II - backward pass.mp4 (48.1 MB)
• 010 Computational graph III - backward pass II.en.srt (15.1 KB)
• 010 Computational graph III - backward pass II.mp4 (63.5 MB)
• 011 Computational graph IV - backward pass III.en.srt (24.4 KB)
• 011 Computational graph IV - backward pass III.mp4 (82.7 MB)
• 012 Forward and backward pass recap and wrap up.en.srt (13.6 KB)
• 012 Forward and backward pass recap and wrap up.mp4 (46.0 MB)
• 022 lecture15.pdf (1.3 MB)
• 023 lecture15_2.pdf (844.1 KB)
• 024 lecture16.pdf (929.5 KB)
• 025 lecture17.pdf (1.3 MB)
• 026 lecture18.pdf (1.4 MB)
• 027 lecture18_2.pdf (1.2 MB)
• 028 lecture19.pdf (503.6 KB)
• 029 lecture20.pdf (592.6 KB)
• 030 lecture20_2.pdf (578.1 KB)
• 031 lecture21.pdf (1.1 MB)
• 032 lecture22.pdf (1.2 MB)
04 How to make neural networks work in reality
• 001 Vanishing gradient problem.en.srt (21.7 KB)
• 001 Vanishing gradient problem.mp4 (38.3 MB)
• 002 Vanishing gradient solutions I.en.srt (18.0 KB)
• 002 Vanishing gradient solutions I.mp4 (22.4 MB)
• 003 Vanishing gradient solutions II.en.srt (10.3 KB)
• 003 Vanishing gradient solutions II.mp4 (17.5 MB)
• 004 Stochastic and mini-batch gradient descent.en.srt (22.7 KB)
• 004 Stochastic and mini-batch gradient descent.mp4 (39.6 MB)
• 005 Other optimizers I.en.srt (13.7 KB)
• 005 Other optimizers I.mp4 (33.3 MB)
• 006 Other optimizers II.en.srt (7.8 KB)
• 006 Other optimizers II.mp4 (11.6 MB)
• 007 Hyperparameter tuning strategie