[DOC] Update README.rst

Author: xuyxu

README.rst

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+Deep Forest
+===========
+
+**Deep Forest** is a general ensemble framework that uses tree-based ensemble algorithms such as Random Forest. It is designed to have the following advantages:
+
+- **Powerful**: Better accuracy than existing tree-based ensemble methods.
+- **Easy to Use**: Less efforts on tunning parameters.
+- **Efficient**: Fast training speed and high efficiency.
+- **Scalable**: Capable of handling large-scale data.
+
+For a quick start, please refer to `How to Get Started <http://www.lamda.nju.edu.cn/deep-forest/how_to_get_started.html>`__. For a detailed guidance on parameter tunning, please refer to `Parameters Tunning <http://www.lamda.nju.edu.cn/deep-forest/parameters_tunning.html>`__.
+
+Installation
+------------
+
+The package is available via PyPI using:
+
+.. code-block:: bash
+
+    pip install deep-forest
+
+Quickstart
+----------
+
+.. code-block:: python
+
+    from sklearn.datasets import load_digits
+    from sklearn.model_selection import train_test_split
+    from sklearn.metrics import accuracy_score
+
+    from deepforest import CascadeForestClassifier
+
+    X, y = load_digits(return_X_y=True)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
+    model = CascadeForestClassifier(random_state=1)
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+    acc = accuracy_score(y_test, y_pred) * 100
+    print("\nTesting Accuracy: {:.3f} %".format(acc))
+    >>> Testing Accuracy: 98.667 %
+
+Resources
+---------
+
+* `Documentation <http://www.lamda.nju.edu.cn/deep-forest>`__
+* Deep Forest: `[Paper] <https://arxiv.org/pdf/1702.08835.pdf>`__
+* Keynote at AISTATS 2019: `[Slides] <https://aistats.org/aistats2019/0-AISTATS2019-slides-zhi-hua_zhou.pdf>`__
+
+Reference
+---------
+
+.. code-block:: latex
+
+    @inproceedings{zhou2017deep,
+        Author = {Zhi-Hua Zhou and Ji Feng},
+        Booktitle = {IJCAI},
+        Pages = {3553-3559},
+        Title = {{Deep Forest:} Towards an alternative to deep neural networks},
+        Year = {2017}}