chapter 4:

- removing deprecated files
- the gcloud sessions are not using the same runtime (so we need to re-download data) so writing outputs to gdrive to make sure things are running and outputs are accessible for subsequent notebooks in colab
- renaming 4/1 notebook - since it had underscores instead of hyphens, the colab link in the notebook was not working
- xception added to imports for notebook 1, since it is part of the model_maker
- PracticalDL#163 - fixed
- PracticalDL#164 - fixed
- PracticalDL#169 - fixed
- metric='angular' added for annoy as default arg will be removed in subsequent releases
- removing a duplicate PCA + Annoy section
- PracticalDL#170 - fixed
- time is a negligible factor here, and we do not need it in the plots (since we are using optimised accuracy calculation using numpy from issue 170) - hence, modifying the plots
- removing matplotlib.style.use('seaborn') since it is deprecated
- the final fine-tuning notebook uses Caltech256 features (as per the book), which do not exist, since fine-tuning was done on Caltech101 - hence, renaming those files to caltech101. Can we retain caltech101 to test?
- PracticalDL#167 - fixed, if the above is okay
- formatted the code

chapter 5:
- write_grads and batch_size params have been removed from callback, or will be removed in subsequent releases
- PracticalDL#174 - not able to replicate this issue
- added a pointer to the notebook that suggests that for tensorboard to work without a 403 Forbidden error on Colab, cookies need to be allowed (I faced this issue)
- notebook 3 in chapter 5 is the exact same as notebook 2 in chapter 2 - replaced the file directly
- the autokeras notebook in Colab is named autokeras-error.ipynb - where can we change this to autokeras.ipynb?
- fixing accuracy score calculation in the autokeras notebook
- formatted the code

chapter 6:
- including the download_sample_image function
- formatted the code

Author: manavmoorthy

code/chapter-3/2-analyzing-the-results.ipynb

@@ -7,6 +7,8 @@ Like Google Reverse Image Search, we explore how one can use embeddings — a co
 Run the following command prior to running the scripts in this directory:
 
 `$ pip install annoy -U`
+`$ pip install nmslib -U`
+`$ pip install falconn -U`
 
 ## Code
 
@@ -23,4 +25,4 @@ In this portion of the chapter, we will find the least accurate (worst) performi
 
 ## Data
 
-We will be using the Caltech101 dataset. Please download the [Caltech101](http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz) dataset and place it in a `caltech101` directory.
+We will be using the [Caltech101](http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz) dataset. The commands for downloading the dataset can be found in the notebooks.