Add notebooks for chapters 5 to 14

index.ipynb

@@ -16,19 +16,29 @@
     "\n",
     "### To run the examples\n",
     "* **Jupyter** – These notebooks are based on Jupyter. If you just plan to read without running any code, there's really nothing more to know, just keep reading! But if you want to experiment with the code examples you need to:\n",
-    "    * open these notebooks in Jupyter. If you clicked on the \"launch binder\" button in github or followed the Installation instructions, then you are good to go. If not you will need to go back to the project [home page](https://github.com/ageron/ml-notebooks/) and click on \"launch binder\" or follow the installation instructions.\n",
+    "    * open these notebooks in Jupyter. If you clicked on the \"launch binder\" button in github or followed the Installation instructions, then you are good to go. If not you will need to go back to the project [home page](https://github.com/ageron/handson-ml/) and click on \"launch binder\" or follow the installation instructions.\n",
     "    * learn how to use Jupyter. Start the User Interface Tour from the Help menu.\n",
     "\n",
     "### To activate extensions\n",
-    "* If this is an interactive session (see above), you may want to turn on a few Jupyter extensions by going to the [Extension Configuration](../nbextensions/) page. In particular the \"*table of contents (2)*\" extension is quite useful.\n",
+    "* If this is an interactive session (see above), you may want to turn on a few Jupyter extensions by going to the [Extension Configuration](../nbextensions/) page. In particular the \"*Table of Contents (2)*\" extension is quite useful.\n",
     "\n",
-    "## Chapters\n",
-    "1. [Fundamentals](fundamentals.ipynb)\n",
-    "2. [End-to-end project](end_to_end_project.ipynb)\n",
-    "3. [Classification](classification.ipynb)\n",
-    "4. [Training Linear Models](training_linear_models.ipynb)\n",
-    "\n",
-    "More explanations and chapters coming soon.\n",
+    "## Notebooks\n",
+    "1. [The Machine Learning landscape](01_the_machine_learning_landscape.ipynb)\n",
+    "2. [End-to-end Machine Learning project](02_end_to_end_machine_learning_project.ipynb)\n",
+    "3. [Classification](03_classification.ipynb)\n",
+    "4. [Training Linear Models](04_training_linear_models.ipynb)\n",
+    "5. [Support Vector Machines](05_support_vector_machines.ipynb)\n",
+    "6. [Decision Trees](06_decision_trees.ipynb)\n",
+    "7. [Ensemble Learning and Random Forests](07_ensemble_learning_and_random_forests.ipynb)\n",
+    "8. [Dimensionality Reduction](08_dimensionality_reduction.ipynb)\n",
+    "9. [Up and running with TensorFlow](09_up_and_running_with_tensorflow.ipynb)\n",
+    "10. [Introduction to Artificial Neural Networks](10_introduction_to_artificial_neural_networks.ipynb)\n",
+    "11. [Deep Learning](11_deep_learning.ipynb)\n",
+    "12. [Distributed TensorFlow](12_distributed_tensorflow.ipynb)\n",
+    "13. [Convolutional Neural Networks](13_convolutional_neural_networks.ipynb)\n",
+    "14. [Recurrent Neural Networks](14_recurrent_neural_networks.ipynb)\n",
+    "15. Autoencoders (coming soon)\n",
+    "16. Reinforcement Learning (coming soon)\n",
     "\n",
     "## Scientific Python tutorials\n",
     "* [NumPy](tools_numpy.ipynb)\n",
@@ -39,6 +49,15 @@
     "* [Linear Algebra](math_linear_algebra.ipynb)\n",
     "* Calculus (coming soon)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -59,10 +78,14 @@
    "pygments_lexer": "ipython2",
    "version": "2.7.11"
   },
+  "nav_menu": {},
   "toc": {
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 6,
    "toc_cell": false,
-   "toc_number_sections": true,
-   "toc_threshold": 6,
+   "toc_section_display": "block",
    "toc_window_display": false
   }
  },