| top |
| back |
| 01 - Why this course.mp4 |
| 02 - Why Machine Learning.mp4 |
| 03 - Our problem in the TensorFlow Playground.mp4 |
| 04 - How does a neuron work.mp4 |
| 05 - Drawing Decision Boundaries with a single neuron.mp4 |
| 06 - Activation Functions.mp4 |
| 07 - Fully Connected Feed Forward Networks.mp4 |
| 08 - How does a network learn.mp4 |
| 09 - Finding the sweet spot.mp4 |
| 10 - Summary.mp4 |
| 11 - Python Notebooks on Colab.mp4 |
| 12 - Getting to know our data.mp4 |
| 13 - Our first network with TensorFlow and the Keras API.mp4 |
| 14 - Evaluating our model.mp4 |
| 15 - Training a network with TensorFlow and the Keras API.mp4 |
| 16 - Making our model more general.mp4 |
| 17 - Summing up and saving our final model.mp4 |
| 18 - Converting the Keras model for tensorflow.js.mp4 |
| 19 - Gluing together our JavaScript application.mp4 |
| 20 - Alternative - Hosting your model on Google Cloud ML.mp4 |
| 21 - Alternative - Running on a dedicated Linux server.mp4 |
| 22 - Summary.mp4 |
| deep-learning-crash-course-notebooks-master.zip.XML |