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1. Introduction/1. Welcome to Machine Learning.mp4 | 37.95 MB |
1. Introduction/2. What does the course cover.mp4 | 49.21 MB |
10. Optimizers/1. SGD_Batching.mp4 | 34.48 MB |
10. Optimizers/2. Local minima pitfalls.mp4 | 14.35 MB |
10. Optimizers/3. Momentum.mp4 | 18.96 MB |
10. Optimizers/4. Learning rate schedules.mp4 | 37.08 MB |
10. Optimizers/5. Learning rate schedules. A picture.mp4 | 10.93 MB |
10. Optimizers/6. Adaptive learning schedules.mp4 | 29.83 MB |
10. Optimizers/7. Adaptive moment estimation.mp4 | 29.08 MB |
11. Preprocessing/1. Preprocessing.mp4 | 25.55 MB |
11. Preprocessing/2. Basic preprocessing.mp4 | 11.11 MB |
11. Preprocessing/3. Standardization.mp4 | 40.37 MB |
11. Preprocessing/4. Dealing with categorical data.mp4 | 18.22 MB |
11. Preprocessing/5. One-hot vs binary.mp4 | 32.26 MB |
12. Deeper example/1. MNIST dataset.mp4 | 8.53 MB |
12. Deeper example/2. How to tackle the MNIST dataset.mp4 | 30.94 MB |
12. Deeper example/3. MNIST - Importing libraries and data.mp4 | 12.71 MB |
12. Deeper example/4. MNIST - Outlining the model.mp4 | 44.99 MB |
12. Deeper example/5. MNIST - Declaring the loss.mp4 | 18.71 MB |
12. Deeper example/6. Accuracy of a model.mp4 | 40.20 MB |
12. Deeper example/7. Early stopping and batching preparation.mp4 | 9.87 MB |
12. Deeper example/8. Optimization.mp4 | 34.04 MB |
12. Deeper example/9. Commenting on the results.mp4 | 52.76 MB |
13. Business case/1. The dataset.mp4 | 52.62 MB |
13. Business case/10. Homework.mp4 | 57.49 MB |
13. Business case/2. Outlining the solution.mp4 | 12.11 MB |
13. Business case/3. Balancing a dataset.mp4 | 44.95 MB |
13. Business case/4. Preprocessing the data.mp4 | 78.73 MB |
13. Business case/5. Creating the batching class.mp4 | 56.65 MB |
13. Business case/6. Outlining the model.mp4 | 46.28 MB |
13. Business case/7. Optimizing the algorithm.mp4 | 27.49 MB |
13. Business case/8. Running the code.mp4 | 19.90 MB |
13. Business case/9. Test.mp4 | 8.84 MB |
14. Conclusion/1. Summary.mp4 | 48.54 MB |
14. Conclusion/2. Whats more out there.mp4 | 17.51 MB |
14. Conclusion/3. An overview of CNNs.mp4 | 71.95 MB |
14. Conclusion/4. An overview of RNNs.mp4 | 27.42 MB |
14. Conclusion/5. Non-NN approaches.mp4 | 41.90 MB |
2. Neural networks Intro/1. Introduction to neural networks.mp4 | 42.58 MB |
2. Neural networks Intro/10. Cross-entropy loss.mp4 | 33.40 MB |
2. Neural networks Intro/11. One-parameter gradient descent.mp4 | 56.41 MB |
2. Neural networks Intro/12. N-parameter gradient descent.mp4 | 50.04 MB |
2. Neural networks Intro/2. Training the model.mp4 | 26.82 MB |
2. Neural networks Intro/3. Types of machine learning.mp4 | 40.85 MB |
2. Neural networks Intro/4. The linear model.mp4 | 26.04 MB |
2. Neural networks Intro/5. The linear model. Multiple inputs..mp4 | 23.69 MB |
2. Neural networks Intro/6. The linear model. Multiple inputs and multiple outputs.mp4 | 42.21 MB |
2. Neural networks Intro/7. Graphical representation.mp4 | 21.96 MB |
2. Neural networks Intro/8. The objective function.mp4 | 17.70 MB |
2. Neural networks Intro/9. L2-norm loss.mp4 | 21.40 MB |
3. Setting up the environment/1. Setting up the environment - Do not skip, please!.mp4 | 7.92 MB |
3. Setting up the environment/2. Why Python and why Jupyter.mp4 | 34.66 MB |
3. Setting up the environment/3. Installing Anaconda.mp4 | 37.10 MB |
3. Setting up the environment/4. Jupyter Dashboard - Part 1.mp4 | 10.32 MB |
3. Setting up the environment/5. Jupyter Dashboard - Part 2.mp4 | 21.00 MB |
3. Setting up the environment/6. Installing the TensorFlow package.mp4 | 14.13 MB |
4. Minimal example/1. Outline.mp4 | 13.94 MB |
4. Minimal example/2. Generating the data (optional).mp4 | 23.74 MB |
4. Minimal example/3. Initializing the variables.mp4 | 20.43 MB |
4. Minimal example/4. Training the model.mp4 | 46.57 MB |
5. Introduction to TensorFlow/1. TensorFlow outline.mp4 | 44.62 MB |
5. Introduction to TensorFlow/2. TensorFlow introduction.mp4 | 19.31 MB |
5. Introduction to TensorFlow/3. Types of file formats used in TensorFlow.mp4 | 12.90 MB |
5. Introduction to TensorFlow/4. Laying down the model.mp4 | 27.71 MB |
5. Introduction to TensorFlow/5. Laying down the optimizers.mp4 | 21.45 MB |
5. Introduction to TensorFlow/6. Output.mp4 | 30.23 MB |
6. Deep nets overview/1. The layer.mp4 | 16.36 MB |
6. Deep nets overview/2. What is a deep net.mp4 | 32.60 MB |
6. Deep nets overview/3. Really understand deep nets.mp4 | 58.18 MB |
6. Deep nets overview/4. Why do we need non-linearities.mp4 | 37.97 MB |
6. Deep nets overview/5. Activation functions.mp4 | 29.18 MB |
6. Deep nets overview/6. Softmax activation.mp4 | 24.98 MB |
6. Deep nets overview/7. Backpropagation.mp4 | 52.73 MB |
6. Deep nets overview/8. Backpropagation - intuition.mp4 | 24.39 MB |
8. Overfitting/1. Underfitting and overfitting.mp4 | 34.06 MB |
8. Overfitting/2. Underfitting and overfitting. A classification example.mp4 | 32.48 MB |
8. Overfitting/3. Train vs validation.mp4 | 37.52 MB |
8. Overfitting/4. Train vs validation vs test.mp4 | 31.32 MB |
8. Overfitting/5. N-fold cross validation.mp4 | 25.57 MB |
8. Overfitting/6. Early stopping - motivation and types.mp4 | 28.33 MB |
9. Initialization/1. Initializaiton.mp4 | 26.17 MB |
9. Initialization/2. Types of simple initializations.mp4 | 12.29 MB |
9. Initialization/3. Xavier_s initialization.mp4 | 19.12 MB |
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