Update gitbook documentation

docs/gitbook/from-zero-to-hero-tutorial/01_introduction.md

@@ -1,51 +1,21 @@
 ---
-description: Understand the Avalanche Structure
+description: Understand the Avalanche Package Structure
 ---
 
 # Introduction
 
-![](../.gitbook/assets/avalanche\_api.png)
+Welcome to the "_Introduction_" tutorial of the "_From Zero to Hero_" series. We will start our journey by taking a quick look at the _Avalanche_ main modules to understand its **general architecture**.
 
-Welcome to the "_Introduction_" tutorial of the "_From Zero to Hero_" series. We will start our journey by taking a quick look at _Avalanche_ main modules and its **general architecture**.
+As hinted in the getting started introduction _Avalanche_ is organized in **five main modules**:
 
-_Avalanche_ is organized in **five main modules**: Benchmarks, Training, Evaluation, Models, Logging.
-
-#### Benchmarks
-
-This module provides a uniform API for data handling: mostly generating a stream of data from one or more datasets. It contains all the major CL benchmarks (similar to what has been done for [torchvision](https://pytorch.org/docs/stable/torchvision/index.html)). Its main components are:
-
-* **Benchmarks**: these are complete benchmarks, providing access to train/test streams.
-* **Streams**: a sequential list of learning experiences.
-* **Experience**: a bunch of data available at a specific point in time.
-* **AvalancheDataset**: a dataset that provides support for train/eval transformations, concatenation and subsampling, and other operations needed to manipulate data in continual learning strategies.
-
-#### Training
-
-This module provides all the necessary utilities concerning model training. This includes simple and efficient ways of implement new _continual learning_ strategies as well as a set pre-implemented CL baselines and state-of-the-art algorithms you will be able to use for comparison!
-
-* **BaseStrategy** provides the default training and eval loops.
-* **Plugins** extend the basic loops with additional functionality.
-
-#### **`Evaluation`**
-
-This module provides all the utilities and metrics that can help evaluate a CL algorithm with respect to all the factors we believe to be important for a continually learning system.
-
-* **EvaluationPlugin**: the plugin that connects metrics and the `BaseStrategy.`
-* **MetricPlugins**: plugins that provide a bridge between metrics and the evaluation plugin. They emit `MetricValue`s that will be collected by the `EvaluationPlugin` and serialized by the loggers.
-* **Metric**: basic logic to compute a metric. Provides the `update`, `result` and `reset` operations used to compute and retrieve the metric value.
-
-#### Models
-
-In this module you'll find model architectures, pre-trained models, and utilities to implement continual learning models. We provide everything you need to implement architectural strategies, task-aware models, and dynamic model expansion.
-
-#### Logging
-
-Metrics are automatically logger using native _stdout_, _files_ and [Tensorboard](https://www.tensorflow.org/tensorboard) support (How cool it is to have a complete, interactive dashboard, tracking your experiment metrics in real-time with a single line of code?)
-
-## File Structure
+* **`Benchmarks`**: This module maintains a uniform API for data handling: mostly generating a stream of data from one or more datasets. It contains all the major CL benchmarks \(similar to what has been done for [torchvision](https://pytorch.org/docs/stable/torchvision/index.html)\).
+* **`Training`**: This module provides all the necessary utilities concerning model training. This includes simple and efficient ways of implement new _continual learning_ strategies as well as a set pre-implemented CL baselines and state-of-the-art algorithms you will be able to use for comparison!
+* **`Evaluation`**: This module provides all the utilities and metrics that can help evaluate a CL algorithm with respect to all the factors we believe to be important for a continually learning system. It also includes advanced logging and plotting features, including native [Tensorboard](https://www.tensorflow.org/tensorboard) support.
+* **`Models`**: In this module you'll find several model architectures and pre-trained models that can be used for your continual learning experiment \(similar to what has been done in [torchvision.models](https://pytorch.org/docs/stable/torchvision/index.html)\). Furthermore, we provide everything you need to implement architectural strategies, task-aware models, and dynamic model expansion.
+* **`Logging`**: It includes advanced logging and plotting features, including native _stdout_, _file_ and [Tensorboard](https://www.tensorflow.org/tensorboard) support \(How cool it is to have a complete, interactive dashboard, tracking your experiment metrics in real-time with a single line of code?\)
 
 {% code title="Avalanche Main Modules and Sub-Modules" %}
-```
+```text
 Avalanche
 ├── Benchmarks
 │   ├── Classic
@@ -63,6 +33,7 @@ Avalanche
 |   └── Utils
 ├── Models
 └── Loggers
+
 ```
 {% endcode %}
 
@@ -74,4 +45,4 @@ In the following tutorials we will assume you have already installed _Avalanche
 
 ## 🤝 Run it on Google Colab
 
-You can run _this chapter_ and play with it on Google Colaboratory: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ContinualAI/avalanche/blob/master/notebooks/from-zero-to-hero-tutorial/01\_introduction.ipynb)
+You can run _this chapter_ and play with it on Google Colaboratory: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ContinualAI/avalanche/blob/master/notebooks/from-zero-to-hero-tutorial/01_introduction.ipynb)

docs/gitbook/from-zero-to-hero-tutorial/03_benchmarks.md

@@ -138,7 +138,7 @@ print(split_mnist.test_stream)
 # stores a set of the (optionally remapped) IDs of classes of patterns
 # assigned to that experience.
 print('--- Classes in each experience:')
-split_mnist.classes_in_experience
+print(split_mnist.original_classes_in_exp)
 ```
 
 #### Train and Test Streams

docs/gitbook/how-tos/avalanchedataset/README.md

@@ -4,12 +4,12 @@ description: Dealing with AvalancheDatasets
 
 # AvalancheDataset
 
-The `AvalancheDataset` is an implementation of the PyTorch `Dataset` class that comes with many useful out-of-the-box functionalities. For most users, the _AvalancheDataset_ can be used as a plain PyTorch Dataset that will return `x, y, t` elements. However, the AvalancheDataset is much more powerful than a simple PyTorch Dataset.
+The `AvalancheDataset` is an implementation of the PyTorch `Dataset` class that comes with many useful out-of-the-box functionalities. For most users, the *AvalancheDataset* can be used as a plain PyTorch Dataset that will return `x, y, t` elements. However, the AvalancheDataset is much more powerful than a simple PyTorch Dataset. 
 
-**A serie of **_**Mini How-Tos**_ will guide you through the functionalities of the _AvalancheDataset_ and its subclasses:
+**A serie of _Mini How-Tos_** will guide you through the functionalities of the *AvalancheDataset* and its subclasses:
 
-* [Preamble: PyTorch Datasets](https://avalanche.continualai.org/how-tos/avalanchedataset/preamble-pytorch-datasets)
-* [Creating AvalancheDatasets](https://avalanche.continualai.org/how-tos/avalanchedataset/creating-avalanchedatasets)
-* [Advanced Transformations](https://avalanche.continualai.org/how-tos/avalanchedataset/advanced-transformations)
+- [Preamble: PyTorch Datasets](https://avalanche.continualai.org/how-tos/avalanchedataset/preamble-pytorch-datasets)
+- [Creating AvalancheDatasets](https://avalanche.continualai.org/how-tos/avalanchedataset/creating-avalanchedatasets)
+- [Advanced Transformations](https://avalanche.continualai.org/how-tos/avalanchedataset/advanced-transformations)
 
-Brefore jumping to the actual _Mini How-To_s, **we recommend having a look at the basic notions of Dataset and DataLoader by reading the** [**Preamble page**](https://avalanche.continualai.org/how-tos/avalanchedataset/preamble-pytorch-datasets).
+Brefore jumping to the actual *Mini How-To*s, **we recommend having a look at the basic notions of Dataset and DataLoader by reading the [Preamble page](https://avalanche.continualai.org/how-tos/avalanchedataset/preamble-pytorch-datasets)**.