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@@ -12,7 +12,7 @@ There are also notebooks used as projects for the Nanodegree program. In the pro
 * [Intro to TensorFlow](https://github.com/udacity/deep-learning/tree/master/intro-to-tensorflow): Starting building neural networks with Tensorflow.
 * [Weight Intialization](https://github.com/udacity/deep-learning/tree/master/weight-initialization): Explore how initializing network weights affects performance.
 * [Autoencoders](https://github.com/udacity/deep-learning/tree/master/autoencoder): Build models for image compression and denoising, using feed-forward and convolution networks in TensorFlow.
-* [Transfer Learning (ConvNet)](https://github.com/udacity/deep-learning/tree/master/transfer-learning). In practice, most people don't train their own large networkd on huge datasets, but use pretrained networks such as VGGnet. Here you'll use VGGnet to classify images of flowers without training a network on the images themselves.
+* [Transfer Learning (ConvNet)](https://github.com/udacity/deep-learning/tree/master/transfer-learning). In practice, most people don't train their own large networks on huge datasets, but use pretrained networks such as VGGNet. Here, you'll use VGGNet to classify images of flowers without training a network on the images themselves.
 * [Intro to Recurrent Networks (Character-wise RNN)](https://github.com/udacity/deep-learning/tree/master/intro-to-rnns): Recurrent neural networks are able to use information about the sequence of data, such as the sequence of characters in text.
 * [Embeddings (Word2Vec)](https://github.com/udacity/deep-learning/tree/master/embeddings): Implement the Word2Vec model to find semantic representations of words for use in natural language processing.
 * [Sentiment Analysis RNN](https://github.com/udacity/deep-learning/tree/master/sentiment-rnn): Implement a recurrent neural network that can predict if a text sample is positive or negative.
@@ -43,4 +43,4 @@ To install these dependencies with pip, you can issue `pip3 install -r requireme
 
 ### Conda Environments
 
-You can find Conda environment files for the Deep Learning program in the `environments` folder. Note that environment files are platform dependent. Versions with `tensorflow-gpu` are labeled in the filename with "GPU".
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+You can find Conda environment files for the Deep Learning program in the `environments` folder. Note that environment files are platform dependent. Versions with `tensorflow-gpu` are labeled in the filename with "GPU".