1 Introduction.mp4 (72.01 MB)
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2 What is Machine Learning exactly.mp4 (12.43 MB)
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3 Different types of machine learning supervised, unsupervised, and reinforcement.mp4 (25.88 MB)
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4 The big picture.mp4 (24.31 MB)
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5 Deep neural network as features and weights.mp4 (32.69 MB)
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6 Loss functions & training vs inference.mp4 (38.47 MB)
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7 Why deep learning is unintuitive and how to get good at it.mp4 (14.09 MB)
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8 How to make neural networks feel intuitive.mp4 (18.27 MB)
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9 Course overview.mp4 (13.76 MB)
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1 Linear regression and MSE loss.mp4 (17.96 MB)
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10 Scalability and emergent properties.mp4 (25.43 MB)
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11 Recap of the forward pass and brief introduction to backward pass.mp4 (11.29 MB)
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2 Numerical analysis - a k a "trial-and-error".mp4 (18.83 MB)
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3 Network view.mp4 (45.35 MB)
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4 Perceptrons.mp4 (15.53 MB)
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5 The "Deep" in deep learning.mp4 (25.08 MB)
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6 Activation Function.mp4 (17.55 MB)
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7 Overparameterization and overfitting.mp4 (20.02 MB)
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8 Linear Algebra detour.mp4 (33.06 MB)
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9 Vectorization (= parallelization).mp4 (29.26 MB)
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1 The back propagation algorithm.mp4 (14.38 MB)
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10 Computational graph III - backward pass II.mp4 (63.55 MB)
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11 Computational graph IV - backward pass III.mp4 (82.75 MB)
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12 Forward and backward pass recap and wrap up.mp4 (46 MB)
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2 Calculus detour.mp4 (37.1 MB)
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3 Calculus detour II.mp4 (15.88 MB)
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4 Gradient descent.mp4 (100.98 MB)
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5 Calculus detour - partial derivatives and gradient descent.mp4 (42.14 MB)
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6 Calculus detour - the Chain Rule.mp4 (38.25 MB)
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7 Calculus detour - the Chain Rule II.mp4 (36.43 MB)
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8 Computational graph I - forward pass.mp4 (15.06 MB)
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9 Computational graph II - backward pass.mp4 (48.1 MB)
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1 Vanishing gradient problem.mp4 (38.35 MB)
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10 Overfitting II - regularization and drop out.mp4 (25.36 MB)
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11 Softmax activation.mp4 (28.77 MB)
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12 Loss functions.mp4 (11.61 MB)
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13 Cross entropy loss.mp4 (25.98 MB)
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2 Vanishing gradient solutions I.mp4 (22.45 MB)
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3 Vanishing gradient solutions II.mp4 (17.55 MB)
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4 Stochastic and mini-batch gradient descent.mp4 (39.64 MB)
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5 Other optimizers I.mp4 (33.3 MB)
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6 Other optimizers II.mp4 (11.59 MB)
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7 Hyperparameter tuning strategies.mp4 (27.78 MB)
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8 Batch normalization.mp4 (43.88 MB)
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9 Overfitting I - problem and solution overview.mp4 (31.15 MB)
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1 Setting up a coding environment using Anaconda and Jupyter Notebook in Vscode.mp4 (33.57 MB)
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10 Next steps.mp4 (110.56 MB)
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2 Train an MNIST model from scratch in plain PyTorch I.mp4 (96.24 MB)
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3 Train an MNIST model from scratch in plain PyTorch II.mp4 (96.28 MB)
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4 Train an MNIST model from scratch in plain PyTorch III.mp4 (102.03 MB)
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5 Train an MNIST model from scratch in plain PyTorch IV.mp4 (75.63 MB)
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6 Train an MNIST model using PyTorch's nn module I.mp4 (84.94 MB)
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7 Train an MNIST model using PyTorch's nn module II.mp4 (102.12 MB)
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8 Train an MNIST model using PyTorch Lightning I.mp4 (83.01 MB)
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9 Train an MNIST model using PyTorch Lightning II.mp4 (118.43 MB)
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