| 14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.mp4 | 144.34 MB |
| 01. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.mp4 | 105.79 MB |
| 13. Business case/4. Preprocessing the data.mp4 | 92 MB |
| 13. Business case/1. Exploring the dataset and identifying predictors.mp4 | 78.16 MB |
| 13. Business case/9. Setting an early stopping mechanism.mp4 | 53.36 MB |
| 14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.mp4 | 49.79 MB |
| 14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.mp4 | 49.38 MB |
| 12. The MNIST example/6. Preprocess the data - shuffle and batch the data.mp4 | 45.93 MB |
| 12. The MNIST example/10. Learning.mp4 | 44.47 MB |
| 03. Setting up the working environment/9. Installing TensorFlow 2.mp4 | 42.94 MB |
| 03. Setting up the working environment/2. Why Python and why Jupyter.mp4 | 41.02 MB |
| 02. Introduction to neural networks/24. N-parameter gradient descent.mp4 | 39.45 MB |
| 05. TensorFlow - An introduction/1. TensorFlow outline.mp4 | 38.32 MB |
| 02. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.mp4 | 38.29 MB |
| 05. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 | 38.22 MB |
| 14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.mp4 | 38.09 MB |
| 13. Business case/3. Balancing the dataset.mp4 | 35.19 MB |
| 03. Setting up the working environment/4. Installing Anaconda.mp4 | 34.91 MB |
| 13. Business case/8. Learning and interpreting the result.mp4 | 34.6 MB |
| 14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.mp4 | 33.84 MB |
| 14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.mp4 | 33.59 MB |
| 05. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.mp4 | 32.82 MB |
| 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.mp4 | 32.61 MB |
| 12. The MNIST example/13. Testing the model.mp4 | 32.49 MB |
| 12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.mp4 | 31.94 MB |
| 12. The MNIST example/8. Outline the model.mp4 | 31.17 MB |
| 14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.mp4 | 26.67 MB |
| 05. TensorFlow - An introduction/2. TensorFlow 2 intro.mp4 | 25.07 MB |
| 05. TensorFlow - An introduction/7. Cutomizing your model.mp4 | 24.66 MB |
| 14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.mp4 | 23.99 MB |
| 14. Appendix Linear Algebra Fundamentals/5. Tensors.mp4 | 22.51 MB |
| 03. Setting up the working environment/6. The Jupyter dashboard - part 2.mp4 | 21.08 MB |
| 04. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp4 | 20.81 MB |
| 12. The MNIST example/2. How to tackle the MNIST.mp4 | 20.4 MB |
| 13. Business case/6. Load the preprocessed data.mp4 | 19.38 MB |
| 05. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.mp4 | 18.5 MB |
| 02. Introduction to neural networks/22. One parameter gradient descent.mp4 | 17.77 MB |
| 12. The MNIST example/3. Importing the relevant packages and load the data.mp4 | 17.77 MB |
| 01. Welcome! Course introduction/2. What does the course cover.mp4 | 16.36 MB |
| 12. The MNIST example/1. The dataset.mp4 | 15.67 MB |
| 12. The MNIST example/9. Select the loss and the optimizer.mp4 | 15.26 MB |
| 15. Conclusion/1. See how much you have learned.mp4 | 13.96 MB |
| 02. Introduction to neural networks/1. Introduction to neural networks.mp4 | 13.56 MB |
| 06. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.mp4 | 13.41 MB |
| 02. Introduction to neural networks/5. Types of machine learning.mp4 | 12.2 MB |
| 13. Business case/11. Testing the model.mp4 | 12.07 MB |
| 02. Introduction to neural networks/20. Cross-entropy loss.mp4 | 11.36 MB |
| 14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.mp4 | 11.17 MB |
| 06. Going deeper Introduction to deep neural networks/7. Backpropagation.mp4 | 11.06 MB |
| 08. Overfitting/1. Underfitting and overfitting.mp4 | 11.06 MB |