Merge pull request mrdbourke#403 from acromondx/patch-1

fixed typos

Author: mrdbourke

README.md

@@ -33,7 +33,7 @@ This table is the ground truth for course materials. All the links you need for
 Key:
 * **Number:** The number of the target notebook (this may not match the video section of the course but it ties together all of the materials in the table)
 * **Notebook:** The notebook for a particular module with lots of code and text annotations (notebooks from the videos are based on these)
-* **Data/model:** Links to datasets/pre-trained models for the assosciated notebook
+* **Data/model:** Links to datasets/pre-trained models for the associated notebook
 * **Exercises & Extra-curriculum:** Each module comes with a set of exercises and extra-curriculum to help practice your skills and learn more, I suggest going through these **before** you move onto the next module
 * **Slides:** Although we focus on writing TensorFlow code, we sometimes use pretty slides to describe different concepts, you'll find them here
 
@@ -93,7 +93,7 @@ If no, go and do a beginner machine learning course and if you decide you want t
 
 ## 🛠 Exercises & 📖 Extra-curriculum
 
-To prevent the course from being 100+ hours (deep learning is a broad field), various external resources for different sections are recommended to puruse under your own discrestion.
+To prevent the course from being 100+ hours (deep learning is a broad field), various external resources for different sections are recommended to puruse under your own discretion.
 
 You can find solutions to the exercises in [`extras/solutions/`](https://github.com/mrdbourke/tensorflow-deep-learning/tree/main/extras/solutions), there's a notebook per set of exercises (one for 00, 01, 02... etc). Thank you to [Ashik Shafi](https://github.com/ashikshafi08) for all of the efforts creating these.
 
@@ -151,7 +151,7 @@ You can find solutions to the exercises in [`extras/solutions/`](https://github.
 3. Create a classification dataset using Scikit-Learn's [`make_moons()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html) function, visualize it and then build a model to fit it at over 85% accuracy.
 4. Train a model to get 88%+ accuracy on the fashion MNIST test set. Plot a confusion matrix to see the results after.
 5. Recreate [TensorFlow's](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax) [softmax activation function](https://en.wikipedia.org/wiki/Softmax_function) in your own code. Make sure it can accept a tensor and return that tensor after having the softmax function applied to it.
-6. Create a function (or write code) to visualize multiple image predictions for the fashion MNIST at the same time. Plot at least three different images and their prediciton labels at the same time. Hint: see the [classifcation tutorial in the TensorFlow documentation](https://www.tensorflow.org/tutorials/keras/classification) for ideas.
+6. Create a function (or write code) to visualize multiple image predictions for the fashion MNIST at the same time. Plot at least three different images and their prediction labels at the same time. Hint: see the [classification tutorial in the TensorFlow documentation](https://www.tensorflow.org/tutorials/keras/classification) for ideas.
 7. Make a function to show an image of a certain class of the fashion MNIST dataset and make a prediction on it. For example, plot 3 images of the `T-shirt` class with their predictions.
 
 ### 📖 02. Neural network classification with TensorFlow Extra-curriculum
@@ -228,7 +228,7 @@ You can find solutions to the exercises in [`extras/solutions/`](https://github.
 3. Recreate the first model (the feature extraction model) with [`mixed_precision`](https://www.tensorflow.org/guide/mixed_precision) turned on. 
   * Does it make the model train faster? 
   * Does it effect the accuracy or performance of our model? 
-  * What's the advatanges of using `mixed_precision` training?
+  * What's the advantages of using `mixed_precision` training?
 
 ### 📖 06. Transfer Learning in TensorFlow Part 3: Scaling-up Extra-curriculum
 * Spend 15-minutes reading up on the [EarlyStopping callback](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping). What does it do? How could we use it in our model training?
@@ -253,7 +253,7 @@ You can find solutions to the exercises in [`extras/solutions/`](https://github.
 ### 📖 07. Milestone Project 1: 🍔👁 Food Vision Big™ Extra-curriculum
 
 * Read up on learning rate scheduling and the [learning rate scheduler callback](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/LearningRateScheduler). What is it? And how might it be helpful to this project?
-* Read up on TensorFlow data loaders ([improving TensorFlow data loading performance](https://www.tensorflow.org/guide/data_performance)). Is there anything we've missed? What methods you keep in mind whenever loading data in TensorFlow? Hint: check the summary at the bottom of the page for a gret round up of ideas.
+* Read up on TensorFlow data loaders ([improving TensorFlow data loading performance](https://www.tensorflow.org/guide/data_performance)). Is there anything we've missed? What methods you keep in mind whenever loading data in TensorFlow? Hint: check the summary at the bottom of the page for a great round up of ideas.
 * Read up on the documentation for [TensorFlow mixed precision training](https://www.tensorflow.org/guide/mixed_precision). What are the important things to keep in mind when using mixed precision training?
 
 ---
@@ -286,14 +286,14 @@ To practice what you've learned, a good idea would be to spend an hour on 3 of t
 * Text data doesn't always come in a nice package like the data we've downloaded. So if you're after more on preparing different text sources for being with your TensorFlow deep learning models, it's worth checking out the following:
   * [TensorFlow text loading tutorial](https://www.tensorflow.org/tutorials/load_data/text).
   * [Reading text files with Python](https://realpython.com/read-write-files-python/) by Real Python.
-* This notebook has focused on writing NLP code. For a mathematically rich overview of how NLP with Deep Learning happens, read [Standford's Natural Language Processing with Deep Learning lecture notes Part 1](https://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes01-wordvecs1.pdf).  
+* This notebook has focused on writing NLP code. For a mathematically rich overview of how NLP with Deep Learning happens, read [Stanford's Natural Language Processing with Deep Learning lecture notes Part 1](https://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes01-wordvecs1.pdf).  
   * For an even deeper dive, you could even do the whole [CS224n](http://web.stanford.edu/class/cs224n/) (Natural Language Processing with Deep Learning) course. 
 * Great blog posts to read:
   * Andrei Karpathy's [The Unreasonable Effectiveness of RNNs](https://karpathy.github.io/2015/05/21/rnn-effectiveness/) dives into generating Shakespeare text with RNNs.
   * [Text Classification with NLP: Tf-Idf vs Word2Vec vs BERT](https://towardsdatascience.com/text-classification-with-nlp-tf-idf-vs-word2vec-vs-bert-41ff868d1794) by Mauro Di Pietro. An overview of different techniques for turning text into numbers and then classifying it.
   * [What are word embeddings?](https://machinelearningmastery.com/what-are-word-embeddings/) by Machine Learning Mastery.
 * Other topics worth looking into:
-  * [Attention mechanisms](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/). These are a foundational component of the transformer architecture and also often add improvments to deep NLP models.
+  * [Attention mechanisms](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/). These are a foundational component of the transformer architecture and also often add improvements to deep NLP models.
   * [Transformer architectures](http://jalammar.github.io/illustrated-transformer/). This model architecture has recently taken the NLP world by storm, achieving state of the art on many benchmarks. However, it does take a little more processing to get off the ground, the [HuggingFace Models (formerly HuggingFace Transformers) library](https://huggingface.co/models/) is probably your best quick start.
 
 ---
@@ -317,7 +317,7 @@ To practice what you've learned, a good idea would be to spend an hour on 3 of t
   * `PREDICTED_LABEL`: `SEQUENCE`
   * `PREDICTED_LABEL`: `SEQUENCE`
   * ...
-    * You can find your own unstrcutured RCT abstract from PubMed or try this one from: [*Baclofen promotes alcohol abstinence in alcohol dependent cirrhotic patients with hepatitis C virus (HCV) infection*](https://pubmed.ncbi.nlm.nih.gov/22244707/).
+    * You can find your own unstructured RCT abstract from PubMed or try this one from: [*Baclofen promotes alcohol abstinence in alcohol dependent cirrhotic patients with hepatitis C virus (HCV) infection*](https://pubmed.ncbi.nlm.nih.gov/22244707/).
 
 ### 📖 09. Milestone Project 2: SkimLit 📄🔥 Extra-curriculum
 
@@ -431,7 +431,7 @@ Any further updates/changes will be added below.
 * 02 Dec 2021 - add fix for TensorFlow 2.7 to notebook 02
 * 11 Nov 2021 - add fix for TensorFlow 2.7 to notebook 01
 * 14 Aug 2021 - added a discussion with TensorFlow 2.6 updates and EfficientNetV2 notes: https://github.com/mrdbourke/tensorflow-deep-learning/discussions/166
-* 16 Jul 2021 - added 35 videos to ZTM Academy + Udemy versions of the course for time series and how to pass TensorFlow Developer Certificaiton
+* 16 Jul 2021 - added 35 videos to ZTM Academy + Udemy versions of the course for time series and how to pass TensorFlow Developer Certification
 * 10 Jul 2021 - added 29 edited time series videos to ZTM Academy + Udemy versions of the course, more to come soon
 * 07 Jul 2021 - recorded 5 videos for passing TensorFlow Developer Certification exam section - ALL VIDEOS FOR COURSE DONE!!! time to edit/upload! 🎉
 * 06 Jul 2021 - added guide to TensorFlow Certification Exam: https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/11_passing_the_tensorflow_developer_certification_exam.md - going to record videos for it tomorrow