Update logo path.

README.md

@@ -1,4 +1,4 @@
-<p align="center"><img src="/assets/images/torchml-logo.png" height="120px" /></p>
+<p align="center"><img src="https://learnables.net/torchml/assets/images/torchml-logo.png" height="120px" /></p>
 
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docs/changelog.md

@@ -6,12 +6,14 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [Unreleased]
+## 0.1.0
 
 ### Added
 
-- Preliminary KNearestNeighbors implementation (@210057zzh).
-- Preliminary Ridge regression implementation (@sherrygao821).
+- Initial release with support for linear models (LinearRegression, Lasso, Ridge), naive Bayes (GaussianNaiveBayes), neighbor methods (NearestCentroid, NearestNeighbors, KNeighborsClassifier), discriminant analysis (LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis), decompositions (PCA), and kernel approximations (RBFSampler).
+- Most methods are differentiable (and gradient checked!).
+- Support for CPU/GPU.
+- Several tutorials.
 
 ### Changed
 

docs/index.md

@@ -5,7 +5,7 @@
 ![Test Status](https://github.com/learnables/torchml/workflows/Testing/badge.svg?branch=master)
 
 `torchml` implements the scikit-learn API on top of PyTorch.
-This we automatically get GPU support for scikit-learn and, when possible, differentiability.
+This means we automatically get GPU support for scikit-learn and, when possible, differentiability.
 
 ## Resources
 
@@ -26,7 +26,7 @@ import torchml as ml
 (X_train, y_train), (X_test, y_test) = generate_data()
 
 # API closely follows scikit-learn
-linreg = ml.linear_mode.LinearRegression()
+linreg = ml.linear_model.LinearRegression()
 linreg.fit(X_train, y_train)
 linreg.predict(X_test)
 ~~~