feat: updated neural networks hero lab

jasperan · jasperan · commit cbce2efee1a8 · 2023-06-01T00:12:40.000+02:00
diff --git a/neural_networks_hero/end/end.md b/neural_networks_hero/end/end.md
@@ -0,0 +1,101 @@
+# The End
+
+Follow us if you want content like this, AI news, coding tips... in your social media timelines.
+
+![victor](./images/victor_1.PNG)
+
+![nacho](./images/me_1.PNG)
+
+Hey.
+
+<img src='https://user-images.githubusercontent.com/20752424/214715922-edb3ba30-f89e-4ab9-992c-afcdb44c3e86.jpg' width='500"'>
+
+If you're interested in my content, check out the following links. I'm a Data Scientist Advocate with 4 years of experience, and I love teaching people about Machine Learning (ML) in unique ways that make people learn better.
+
+Follow me if you're interested in ML content. I promise, everything I do goes opensource.
+
+## 🏆 My Stats
+
+![My Public contributions](https://github-readme-stats.vercel.app/api?username=jasperan&show_icons=true&hide_border=false&theme=tokyonight&count_private=true&hide=prs,stars,contribs)
+
+![Streak](https://github-readme-streak-stats.herokuapp.com/?user=jasperan&theme=tokyonight)
+
+![Top Languages](https://github-readme-stats.vercel.app/api/top-langs/?username=jasperan&layout=compact)
+
+[![Trophies](https://github-profile-trophy.vercel.app/?username=jasperan&theme=onedark)](https://github.com/jasperan)
+
+## ☕ Get In Touch
+
+- [StackOverflow](https://stackoverflow.com/users/9151930/jasper?tab=profile)
+- [My Website](https://jasperan.com)
+- [LinkedIn](https://www.linkedin.com/in/jasperan/)
+
+## 👀 My Best Repositories
+
+[![WhatsApp Spy](https://github-readme-stats.vercel.app/api/pin/?username=jasperan&repo=whatsapp-osint)](https://github.com/jasperan/whatsapp-osint)
+
+[![League of Legends Optimizer](https://github-readme-stats.vercel.app/api/pin/?username=oracle-devrel&repo=leagueoflegends-optimizer)](https://github.com/oracle-devrel/leagueoflegends-optimizer)
+
+[![RedBull Analytics Hands-On Lab](https://github-readme-stats.vercel.app/api/pin/?username=oracle-devrel&repo=redbull-analytics-hol)](https://github.com/oracle-devrel/redbull-analytics-hol)
+
+[![F1'21 Telemetry](https://github-readme-stats.vercel.app/api/pin/?username=jasperan&repo=f1-telemetry-oracle)](https://github.com/jasperan/f1-telemetry-oracle)
+
+### My Gaming ML Content
+
+- [League of Legends Machine Learning with OCI - Data Extraction](https://oracle-devrel.github.io/leagueoflegends-optimizer/hols/workshops/dataextraction/index.html) - About Data Extraction & Engineering!
+- [League of Legends Machine Learning with OCI - Model Building with scikit-learn and AutoGluon](https://oracle-devrel.github.io/leagueoflegends-optimizer/hols/workshops/mlwithoci/index.html) - Illustrates the whole AI process once we have data available
+- [League of Legends Machine Learning with OCI - Introduction to Neural Networks](https://oracle-devrel.github.io/leagueoflegends-optimizer/hols/workshops/nn/index.html) - A very basic introduction to all Neural Network concepts, like learning rate, backpropagation... etc.
+
+#### Topic-specific Articles
+
+- [League of Legends Optimizer using Oracle Cloud Infrastructure: Data Extraction & Processing](https://github.com/oracle-devrel/leagueoflegends-optimizer/blob/main/articles/article1.md)
+- [League of Legends Optimizer using Oracle Cloud Infrastructure: Data Extraction & Processing 2](https://github.com/oracle-devrel/leagueoflegends-optimizer/blob/main/articles/article2.md)
+- [League of Legends Optimizer using Oracle Cloud Infrastructure: Building an Adversarial League of Legends AI Model](https://github.com/oracle-devrel/leagueoflegends-optimizer/blob/main/articles/article3.md)
+- [League of Legends Optimizer using Oracle Cloud Infrastructure: Real-Time predictions](https://github.com/oracle-devrel/leagueoflegends-optimizer/blob/main/articles/article4.md)
+- [League of Legends Optimizer using Oracle Cloud Infrastructure: Real-Time predictions 2](https://github.com/oracle-devrel/leagueoflegends-optimizer/blob/main/articles/article5.md)
+
+### My RedBull Content
+
+- [Oracle RedBull Pit Strategy Hands-on Lab](https://oracle-devrel.github.io/redbull-pit-strategy/hols/workshops/pitstrategy/index.html)
+- [Oracle x RedBull AI conference](https://github.com/oracle-devrel/redbull-analytics-hol)
+- [Connecting F1 2021 Telemetry with Oracle JET](https://medium.com/oracledevs/connecting-f1-2021-telemetry-with-oracle-jet-a73714768c34)
+
+### My Computer Vision (Health ML) Content
+
+- [Creating a CMask Detection Model on OCI with YOLOv5: Data Labeling with RoboFlow](https://medium.com/oracledevs/creating-a-cmask-detection-model-on-oci-with-yolov5-data-labeling-with-roboflow-5cff89cf9b0b)
+
+- [Creating a Mask Model on OCI with YOLOv5: Training and Real-Time Inference](https://medium.com/oracledevs/creating-a-mask-model-on-oci-with-yolov5-training-and-real-time-inference-3534c7f9eb21)
+
+- [YOLOv5 and OCI: Implementing Custom PyTorch Code From Scratch](https://medium.com/oracledevs/yolov5-and-oci-implementing-custom-pytorch-code-from-scratch-7c6b82b0b6b1)
+
+### My General ML Content
+
+- [Benchmarking TensorFlow on OCI](https://medium.com/oracledevs/benchmarking-tensorflow-on-oci-70c781287b7d)
+- [Benchmarking PyTorch on OCI and EfficientNet Models](https://medium.com/oracledevs/benchmarking-pytorch-on-oci-and-efficientnet-models-1d729b45d503)
+- [Working with Data in TensorFlow](https://medium.com/oracledevs/working-with-data-in-tensorflow-a0656f616f4f)
+- [Working with Data in PyTorch](https://medium.com/oracledevs/working-with-data-in-pytorch-fa2641e37d17)
+- [Getting Started with PyTorch on OCI](https://medium.com/oracledevs/getting-started-with-pytorch-on-oci-dbaa5e7a40ef)
+
+### Articles in which I'm Featured
+
+- [Team up with Red Bull Racing Honda and Oracle for Hands-on Lab teaching machine learning with racing data](https://medium.com/oracledevs/team-up-with-red-bull-racing-honda-and-oracle-for-hands-on-lab-teaching-machine-learning-with-70eafcf78383)
+
+### My Public Appearances
+
+![Oracle CloudWorld 2022](https://user-images.githubusercontent.com/20752424/214705966-dd90d511-713b-4322-b620-bd2946857f02.jpg)
+    > Me @ Oracle CloudWorld 2022, Las Vegas
+
+### Current public repositories I am working on
+
+- [League of Legends Optimizer](https://github.com/oracle-devrel/leagueoflegends-optimizer)
+- [WhatsApp OSINT Tool](https://github.com/jasperan/whatsapp-osint)
+
+## YouTube Videos
+
+- [League of Legends Content Series Interview: Using ML with League of Legends (LoL)](https://youtu.be/zz3xaLI0uq8)
+- [AlmaLinux Pi Day 2021](https://youtu.be/kGfwYqXxBfY)
+- [Machine Learning on OCI for League of Legends- Data Extraction](https://youtu.be/ad0RkqB07vI)
+- [Machine Learning on OCI for League of Legends - Model Building with scikit-learn and AutoGluon](https://youtu.be/5iIvkgcMvhM)
+- [Machine Learning on OCI for League of Legends: Introduction to Neural Networks](https://youtu.be/Uuo3ZSexNU8)
+
+⭐️ From [jasperan](https://github.com/jasperan)
diff --git a/neural_networks_hero/end/images/me_1.PNG b/neural_networks_hero/end/images/me_1.PNG
diff --git a/neural_networks_hero/end/images/victor_1.PNG b/neural_networks_hero/end/images/victor_1.PNG
diff --git a/neural_networks_hero/infra/infra.md b/neural_networks_hero/infra/infra.md
@@ -73,10 +73,11 @@ Estimated Time: 15 minutes
     ![Paste Tenancy OCID](images/paste-tenancy-ocid.png)
 
 6. Copy the output of the same command as the compartment OCID:
-    ```
+
+    ```bash
     <copy>echo $OCI_TENANCY</copy>
     ```
-    
+
     > Note: we can paste the same OCID here in both tenancy and compartment because the root compartment in a tenancy is equal to the tenancy's OCID.
 
     ![Paste Compartment OCID](images/paste-compartment-ocid.png)
@@ -92,7 +93,7 @@ Estimated Time: 15 minutes
 7. Generate a SSH key pair, by default it will create a private key on _`~/.ssh/id_rsa`_ and a public key _`~/.ssh/id_rsa.pub`_.
     It will ask to enter the path, a passphrase and confirm again the passphrase; type _[ENTER]_ to continue all three times.
 
-    ```
+    ```bash
     <copy>ssh-keygen -t rsa</copy>
     ```
 
@@ -104,7 +105,7 @@ Estimated Time: 15 minutes
 
 8. We need the public key in our notes, so keep the result of the content of the following command in your notes.
 
-    ```
+    ```bash
     <copy>cat ~/.ssh/id_rsa.pub</copy>
     ```
 
@@ -115,11 +116,11 @@ Estimated Time: 15 minutes
 10. Save the file in the Code Editor.
     ![Code Editor Save](images/code-editor-save.png)
 
-
 ## Task 3: Start Deployment
 
 1. Run the `start.sh` script
-    ```
+
+    ```bash
     <copy>./start.sh</copy>
     ```
 
@@ -137,18 +138,17 @@ Estimated Time: 15 minutes
 
     > Note: login credentials for the Data Science notebook are the same as the ones used to access Oracle Cloud Infrastructure.
 
-
 ## Task 4: Accessing Notebook
 
 Having just created our OCI Data Science environment, we need to install the necessary Python dependencies to execute our code. For that, we'll access our environment.
 
-- The easiest way is to access into the notebook **through the URL** that we previously copied from Terraform's output.
+* The easiest way is to access into the notebook **through the URL** that we previously copied from Terraform's output.
 
     ![terraform output](images/start-sh-ssh.png)
 
     If you have done it this way, make sure to **skip through to the next task**.
 
-- (Optionally) We can also access to the notebook via the OCI console, on the top left burger menu:
+* (Optionally) We can also access to the notebook via the OCI console, on the top left burger menu:
 
     ![select data science](./images/select_data_science.png)
 
@@ -168,51 +168,49 @@ Having just created our OCI Data Science environment, we need to install the nec
 
     ![jupyter environment](./images/notebook.png)
 
-
 ## Task 5: Setting up Data Science Environment
 
 We now need to load our notebook into our environment.
+
 1. Opening a **Terminal** inside the _'Other'_ section the console and re-downloading the repository again:
 
     ![open terminal](./images/open_terminal.png)
 
 2. Then, we re-clone the repository:
 
-    ```
+    ```bash
     <copy>
     git clone --branch livelabs https://github.com/oracle-devrel/leagueoflegends-optimizer.git
     </copy>
     ```
 
-
-
 3. Install the conda environment
 
-    ```
+    ```bash
     <copy>odsc conda create -n myconda</copy>
     ```
 
     ![proceed](./images/proceed.png)
 
 4. Activate the newly-created conda environment:
 
-    ```
+    ```bash
     <copy>
     conda activate /home/datascience/conda/myconda_v1_0
     </copy>
     ```
 
 5. Install Python 3.8 within the conda environment:
 
-    ```
+    ```bash
     <copy>
     conda install -y python=3.8
     </copy>
     ```
 
 6. Install Python dependencies:
 
-    ```
+    ```bash
     <copy>
     pip install -r leagueoflegends-optimizer/deps/requirements_nn.txt
     </copy>
@@ -222,8 +220,6 @@ We now need to load our notebook into our environment.
 
 After these commands, all requirements will be fulfilled and we're ready to execute our notebooks with our newly created conda environment.
 
-
-
 ## Task 6: Downloading DataSets
 
 We now need to load our datasets into our environment. For that, we reuse the terminal we created in the previous step:
@@ -232,8 +228,7 @@ We now need to load our datasets into our environment. For that, we reuse the te
 
 Then, we execute the following command, which will download all necessary datasets:
 
-
-```
+```bash
 <copy>
 wget https://objectstorage.eu-frankfurt-1.oraclecloud.com/p/wPITlcIlqhE3VuDjIxeXyqwWTUa8o96q5jy-6gBXSNhO4OKha1A1JetWozNFZIAZ/n/axywji1aljc2/b/league-hol-ocw-datasets/o/ocw_datasets.zip && unzip ocw_datasets.zip -d /home/datascience/.
 
@@ -242,7 +237,6 @@ wget https://objectstorage.eu-frankfurt-1.oraclecloud.com/p/wPITlcIlqhE3VuDjIxeX
 
 ![unzip result](./images/unzip_result.png)
 
-
 ## Task 7: Accessing our Notebooks
 
 We should now see the repository / files in our file explorer:
@@ -253,7 +247,7 @@ We should now see the repository / files in our file explorer:
 
 We navigate to the _`leagueoflegends-optimizer/notebooks/`_ directory and the notebook [_`neural_networks_lol.ipynb`_](https://github.com/oracle-devrel/leagueoflegends-optimizer/blob/livelabs/notebooks/neural_networks_lol.ipynb) is the one we will review during this workshop.
 
-Let's open both of them and get to work. 
+Let's open both of them and get to work.
 
 You may now [proceed to the next lab](#next).
 
@@ -262,4 +256,4 @@ You may now [proceed to the next lab](#next).
 
 * **Author** - Nacho Martinez, Data Science Advocate @ DevRel
 * **Contributors** - Victor Martin - Product Strategy Director
-* **Last Updated By/Date** - April 20th, 2023
+* **Last Updated By/Date** - May 31st, 2023
diff --git a/neural_networks_hero/the_problem/problem.md b/neural_networks_hero/the_problem/problem.md
@@ -125,6 +125,8 @@ This generated a lot of visualizations for me, that gave me an idea of what's ne
 
 We can see that the loss of our ML model is low enough for our model to have taken the correct approach to predict the target variable. We can confirm that the model is telling us the most important variables by checking other models' predictions as well.
 
+> **Note**: beware of **overfitting** if the validation metrics are too good!
+
 As we can see, the model is able to deduce whether we're going to win or not by just looking at four or five weighted variables. By comparing these stats to what we already have in the Live Client API, we'll determine which variables we can use from that data structure to arrive at the conclusion.
 
 Considering that we're working with time-dependent data, from the variables mentioned above, we can extract the same statistics (deaths, kills)... per minute. This will introduce the time dimension into our dataset:
diff --git a/neural_networks_hero/understand_nn/understand_nns.md b/neural_networks_hero/understand_nn/understand_nns.md
@@ -26,11 +26,6 @@ This is a huge renovation that happens in your brain.
 - By the time we're teens it's gone a long way, but it hasn't finished.
 - And the part of the brain that's considered to develop last (up until 25 years old) is the very front, whith happens to be the part of the brain that sort of makes us human.
 
-The frontal cortex is a part of your brain that controls rational thought and reasoning
-
-So, when a teenager is confronted with a decision, this rational area is slower at communicating with other parts of their brain.
- So those other parts end up taking the lead. Especially the amygdala.
-
 ## Task 1: What is a Neural Network
 
 A neural network is a method in Machine Learning that *simulates* the human brain's way of thinking, and teaches computers how to process data in a way that *looks* human.
@@ -68,7 +63,7 @@ We'll also take a look at [another open-source tool](https://poloclub.github.io/
 
 ![recurrent](images/neural_network_visualization_1.gif)
 
-The example that we can see in this Neural Network is very simple and only has one input layer, one hidden layer, and one output layer. 
+The example that we can see in this Neural Network is very simple and only has one input layer, one hidden layer, and one output layer.
 
 In cases like text or image analysis, things get a bit more complicated, and usually, several pre-built layers (groups of layers that work very well together) are used to analyze this kind of data (image, text):
 
