Fix typo in docs

Fix typos for some docs
- README.md and docs/configuration-ini.md: simple typo
- Applications/MNIST/README.md: typo and duplicate image

**Self evaluation:**
1. Build test:     [X]Passed [ ]Failed [ ]Skipped
2. Run test:     [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Hanbyeol Kim kimhan0515@snu.ac.kr
Signed-off-by: kimhan0515 <kimhan0515@gmail.com>

diff --git a/Applications/MNIST/README.md b/Applications/MNIST/README.md
index 81edee00373cde15dc8d77ddec4617793c09ca04..0b1753906a96c796371100078ef42597a9d6f783 100644 (file)
--- a/Applications/MNIST/README.md
+++ b/Applications/MNIST/README.md
@@ -140,13 +140,10 @@ You can run with
 $ python3 Training_Keras.py
 ```
 
-```mnist_trainingSet.data``` must be in the same directory with ```Training_Keras.py```.
+```mnist_trainingSet.dat``` must be in the same directory with ```Training_Keras.py```.
 
 
 ### Comparison with Tensorflow
 This is the comparison with tensorflow-1.14.0 for the two cases. One is with zero initialization of weight and bias and the other is random weight initialization data using the default intializers for each layer from tensorflow. As can be seen with the result below, the results are same within the margin of error.
 
 ![image](/docs/images/image2020-9-1_8-23-40.png?raw=true)
-
-
-![image](/docs/images/image2020-9-1_8-23-40.png?raw=true)

diff --git a/README.md b/README.md
index e7e3526778d679fb9dcc1eb6a66b9093ce3b9151..e4b859e52a90fff8e478cddd6687df816a96fd7e 100644 (file)
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ NNtrainer is a Software Framework for training Neural Network models on devices.
 
 NNtrainer is an Open Source Project. The aim of the NNtrainer is to develop a Software Framework to train neural network models on embedded devices which have relatively limited resources. Rather than training whole layers of a network from the scratch, NNtrainer finetunes the neural network model on device with user data for the personalization.
 
-Even if NNtariner runs on device, it provides full functionalities to train models and also utilizes limited device resources efficiently. NNTrainer is able to train various machine learning algorithms such as k-Nearest Neighbor (k-NN), Neural Networks, Logistic Regression, Reinforcement Learning algorithms, Recurrent network and more. We also provide examples for various tasks such as Few-shot learning, ResNet, VGG, Product Rating and more will be added. All of these were tested on Samsung Galaxy smart phone with Android and PC (Ubuntu 18.04/20.04).
+Even if NNtrainer runs on device, it provides full functionalities to train models and also utilizes limited device resources efficiently. NNTrainer is able to train various machine learning algorithms such as k-Nearest Neighbor (k-NN), Neural Networks, Logistic Regression, Reinforcement Learning algorithms, Recurrent network and more. We also provide examples for various tasks such as Few-shot learning, ResNet, VGG, Product Rating and more will be added. All of these were tested on Samsung Galaxy smart phone with Android and PC (Ubuntu 18.04/20.04).
 
 [ NNTrainer: Light-Weight On-Device Training Framework ](https://arxiv.org/pdf/2206.04688.pdf), arXiv, 2022 <br />
 [ NNTrainer: Towards the on-device learning for personalization ](https://www.youtube.com/watch?v=HWiV7WbIM3E), Samsung Software Developer Conference 2021 (Korean) <br />

diff --git a/docs/configuration-ini.md b/docs/configuration-ini.md
index c24e2f1245c36a966271264a3d68ea5cf541848c..1dd0fa044a55bf331a67072c4f854b0b5f86a178 100644 (file)
--- a/docs/configuration-ini.md
+++ b/docs/configuration-ini.md
@@ -12,7 +12,7 @@ If you want more about iniparser, please visit
 ## Sections
 ### Model Section
 
-Model section includes the hyper-parameters for the Network such type, epochs, loss, save path and batch size.
+Model section includes the hyper-parameters for the Network such as type, epochs, loss, save path and batch size.
 
 Start with "[Model]"