unit 1 and 2 readmes for studios

unit01-ml-intro/README.ipynb

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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using this Studio"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can find the code covered in the Unit 1.6 videos in the `./1.6.perceptron-in-python` subfolder. I recommend you first watch the videos and then experiment with the code:\n",
+    "\n",
+    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ftzkrT82tlI?si=Ord318b85-JkYBa_\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/IC7iT2gVni4?si=3QbZXs9LZvDcUy2i\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/YF9p8TKzmRM?si=1Zzr4LV41P1dhTfL\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After exploring the code in the `./1.6.perceptron-in-python` subfolder, you may want to try the following exercises in the `./exercises` subfolder. (Solutions to the excercises can be found in the `./solutions` subfolder)."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

unit01-ml-intro/Unit1-README.md

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+# Deep Learning Fundamentals Unit 1
+
+## Welcome to Machine Learning and Deep Learning
+
+This Studio provides a reproducible environment with the supplementary code for Unit 1 of the [**Deep Learning Fundamentals**](https://lightning.ai/pages/courses/deep-learning-fundamentals/) class by Sebastian Raschka, which is freely available at Lightning AI.
+
+In this Studio, you will learn how to code a simple perceptron algorithm in Python, a linear classifier that can be used to separate data belong to two different classes.
+
+
+<br>
+
+**What's included?**
+
+Click the "Run Template" button at the top of this page to launch into a Studio environment that contains the following materials:
+
+- `./1.6-perceptron-in-python/`: The code materials used in *Unit 1.6 Implementing a Perceptron in Python. Parts 1-3*
+- `./exercises`: 
+  - `./1_early-stop`: Exercise 1,  adding early-stopping to make the Perceptron more efficient
+  - `./2_random-weights`: Exercise 2, initializing the model parameters with small random numbers instead of 0's
+  - `./3_learning-rate`: Exercise 3, using a learning rate for updating the weights and bias unit
+- `./solutions`: Solutions to the exercises above
+
+---
+
+<br>
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/ftzkrT82tlI?si=SppRYYX4oUNyw-eM" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+
+- Links to [Part 2](https://www.youtube.com/watch?v=IC7iT2gVni4&list=PLaMu-SDt_RB4Ly0xb0qsQVpLwRQcjtOb-&index=15) and [Part 3](https://www.youtube.com/watch?v=YF9p8TKzmRM&list=PLaMu-SDt_RB4Ly0xb0qsQVpLwRQcjtOb-&index=16) of the code video above
+- [The complete YouTube Playlist](https://www.youtube.com/watch?v=6Py-tIEiXKw&list=PLaMu-SDt_RB4Ly0xb0qsQVpLwRQcjtOb-) with all 18 videos in Unit 1
+- [Or access the Unit 1 videos on the Lightning website](https://lightning.ai/courses/deep-learning-fundamentals/unit-1/), which includes additional quizzes
+
+<br>
+
+## About Unit 1: Welcome to Machine Learning and Deep Learning
+
+
+
+Welcome to this exciting journey into the world of machine learning and artificial intelligence. In this first unit, you will learn about the big picture behind machine learning and how it’s related to deep learning and artificial intelligence.
+
+Moreover, we will introduce the concepts of a typical machine learning workflow and implement a classifier in Python. 
+
+Specifically, via the videos and code examples in Unit 1.6 included in the Studio, we implement one of the first machine learning algorithms — a perceptron classifier — in Python. This perceptron algorithm is a linear classifier, which can be trained to separate two different classes via a linear decision boundary, as shown below:
+
+![perceptron](readme-images/perceptron.png)
+
+As shown above, a perceptron classifier is a type of linear binary classifier that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. The algorithm allows for the classification of inputs into two distinct classes, making it an important building block for understanding machine learning. 
+
+The perceptron, beyond its historical significance as one of the earliest algorithms developed in the field of neural networks, is known for its simplicity and efficiency in teaching the basic principles of machine learning, such as the concept of weight adjustment based on errors, which is a precursor to more complex algorithms covered in later Units. 
+
+In short, the perceptron is particularly useful for problems that are linearly separable, and its study provides a solid groundwork for understanding more advanced neural network architectures.
+
+Watch the video above to follow the explanation, and click the "Run Tempate" button at the top of this page to get started.
+

unit02-pytorch-tensors/README.ipynb

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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using this Studio"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can find the code covered in the Unit 2 videos in the `./code-units` subfolder. I recommend you first watch the videos and then experiment with the code.\n",
+    "\n",
+    "- [The complete YouTube Playlist](https://www.youtube.com/watch?v=cwcaTNHgGuM&list=PLaMu-SDt_RB55zaDxbuX4DGLC3hIVOGv_) with all 12 videos in Unit 2\n",
+    "- [Or access the Unit 2 videos on the Lightning website](https://lightning.ai/courses/deep-learning-fundamentals/2-0-unit-2-overview/), which includes additional quizzes"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After exploring the code, you may want to try the following exercises in the `./exercises` subfolder. (Solutions to the excercises can be found in the `./solutions` subfolder)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

unit02-pytorch-tensors/Unit2-README.md

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+# Deep Learning Fundamentals Unit 2
+
+## Introduction to Working with Tensors in PyTorch
+
+What are tensors? We’re glad you asked. In Unit 2, you will learn more about tensors and familiarize ourselves with PyTorch, the most widely used deep learning library.
+
+This Studio provides a reproducible environment with the supplementary code for Unit 2 of the [**Deep Learning Fundamentals**](https://lightning.ai/pages/courses/deep-learning-fundamentals/) class by Sebastian Raschka, which is freely available at Lightning AI.
+
+
+<br>
+
+**What's included?**
+
+Click the "Run Template" button at the top of this page to launch into a Studio environment that contains the following materials:
+
+- `code-units/`
+
+  - `2.2-tensors/`: The code materials used in *Unit 2.2: What Are Tensors (Part 1 and 2)*
+
+  - `2.3-using-tensors/`: The code materials used in *Unit 2.3 How Do We Use Tensors in PyTorch?*
+
+  - `2.4-linalg/`: The code materials used in *Unit 2.4 Improving Code Efficiency with Linear Algebra (Parts 1-4)*
+
+  - `2.5-debugging/`: The code materials used in *Unit 2.5 Debugging Code*
+
+  - `2.6-revisiting-perceptron/`: The code materials used in *Unit 2.6 Revisiting the Perceptron Algorithm*
+
+
+- `exercises/`: 
+  - `1_torch-where`: Exercise 2,  learning how to use the `torch.where` function
+  - `2_random-weights`: Exercise 2, making the perceptron algorithm more efficient using matrix multiplication
+- `solutions/`: Solutions to the exercises above
+
+---
+
+<br>
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/cwcaTNHgGuM?si=MaUer4N6HxnCNAOR" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+
+
+
+- [The complete YouTube Playlist](https://www.youtube.com/watch?v=cwcaTNHgGuM&list=PLaMu-SDt_RB55zaDxbuX4DGLC3hIVOGv_) with all 12 videos in Unit 2
+- [Or access the Unit 2 videos on the Lightning website](https://lightning.ai/courses/deep-learning-fundamentals/2-0-unit-2-overview/), which includes additional quizzes
+
+<br>
+
+## About Unit 2: Introduction to Working with Tensors in PyTorch
+
+In Unit 2, we introduce PyTorch. PyTorch is an open-source library for deep learning library that is widely used in both academia and industry. PyTorch originated from the original Torch project, a deep learning framework based on the programming language Lua. In the upcoming videos, we will dive deeper into PyTorch’s tensor data structure before implementing and training deep neural networks.
+
+Then, we'll introduce tensors as a mathematical concept: a generalization of vectors, matrices etc. However, in a computational context, we usually think of tensors as data containers. If you are familiar with NumPy arrays, PyTorch tensors are essentially a similar concept.
+
+![tensors-slide](unit2-readme-images/tensors-slide.png)
+
+In short, we can think PyTorch tensors as fancy NumPy arrays that support additional functionality, such as automatic differentation and computations on a GPU.
+
+Learn more by watching the videos of Deep Learning Fundamentals linked above and follow along with the code and exercises in this Studio. You can launch it by clicking the "Run Tempate" button at the top of this page to get started.
+