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Fix a few minor typos/grammar errors in welcome page and lesson 1 #16

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Fix a few minor typos/grammar errors in welcome page and lesson 1 #16

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d8c7f0c
Fix a few minor typos/grammar errors in lesson 1
yoavf Dec 19, 2023
6bb40f7
A few additional fixes in the course homepage
yoavf Dec 19, 2023
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8 changes: 4 additions & 4 deletions Lessons/lesson1.qmd
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Expand Up @@ -20,13 +20,13 @@ This lesson is based partly on [chapter 1](https://github.com/fastai/fastbook/bl

## How to complete lesson 1

Every lesson includes lots of hands-on exercises for you to try. Most of these are run in interactive notebooks, all of which are available on [Kaggle](../Resources/kaggle.html). If you don't work through the notebooks yourself, you're not going to get nearly as much out of this course---so that means you need to get set up on Kaggle. We have a page to help you get going with Kaggle: [click here](../Resources/kaggle.html) to go there now. Instead of using Kaggle, another great option is [Paperspace Gradient](https://gradient.run/notebooks) If you don't have a Paperspace account yet, sign up with [this link](https://console.paperspace.com/signup?R=lg6rnx) to get $10 credit (and we get a credit too).
Every lesson includes lots of hands-on exercises for you to try. Most of these are run in interactive notebooks, all of which are available on [Kaggle](../Resources/kaggle.html). If you don't work through the notebooks yourself, you're not going to get nearly as much out of this course---so that means you need to get set up on Kaggle. We have a page to help you get going with Kaggle: [click here](../Resources/kaggle.html) to go there now. Instead of using Kaggle, another great option is [Paperspace Gradient](https://gradient.run/notebooks). If you don't have a Paperspace account yet, sign up with [this link](https://console.paperspace.com/signup?R=lg6rnx) to get $10 credit (and we get a credit too).

Once you've got your Kaggle account set up, you'll need to get familiar with [Jupyter Notebook](https://jupyter.org/), which is the platform we use for most of this course (and which most deep learning researchers and engineers use for their work). Jupyter is the most popular tool for doing data science in Python, for good reason. It is powerful, flexible, and easy to use. We think you will love it! Since the most important thing for learning deep learning is writing code and experimenting, it's important that you have a great platform for experimenting with code. If you haven't used it before, we've provided this to help you get started: [Jupyter Notebook 101](https://www.kaggle.com/code/jhoward/jupyter-notebook-101).

OK, now that you have your Kaggle account and know how to use Jupyter, you're ready to open the notebook for this lesson: [here it is](https://www.kaggle.com/code/jhoward/is-it-a-bird-creating-a-model-from-your-own-data). For every lesson, you can find links to all notebooks used in the *Resources* section of the lesson web page. For instance, for lesson 1, you'll see that section immediately below this one.

As well as watching the video and working through the notebooks, you should also read the relevent chapter(s) of the fast.ai book, [Practical Deep Learning for Coders](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch-ebook-dp-B08C2KM7NR/dp/B08C2KM7NR). Each lesson will tell you what chapter you need to read, just below the video. For this lesson, it's chapter 1. There's a few ways to read the book -- you can buy it as a paper book or Kindle ebook, or you can read it for free as a Jupyter notebook. The whole book is written as Jupyter notebooks, so you can also execute all the code in the book yourself. To go to the interactive Jupyter version of any chapter, click [The book](../Resources/book.html) in the left sidebar, where you'll find a list of chapter links. You'll also find links to read-only versions of each chapter there.
As well as watching the video and working through the notebooks, you should also read the relevant chapter(s) of the fast.ai book, [Practical Deep Learning for Coders](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch-ebook-dp-B08C2KM7NR/dp/B08C2KM7NR). Each lesson will tell you what chapter you need to read, just below the video. For this lesson, it's chapter 1. There are a few ways to read the book -- you can buy it as a paper book or Kindle ebook, or you can read it for free as a Jupyter notebook. The whole book is written as Jupyter notebooks, so you can also execute all the code in the book yourself. To go to the interactive Jupyter version of any chapter, click [The book](../Resources/book.html) in the left sidebar, where you'll find a list of chapter links. You'll also find links to read-only versions of each chapter there.

## Resources

Expand All @@ -42,7 +42,7 @@ As well as watching the video and working through the notebooks, you should also

## Links

You'll see that fast.ai's way of teaching is very different to what you might be used to, if you did a technical degree at university. Nearly all technical subjects at university are taught "bottom up": start with basic foundations, and gradually work up to complete useful solutions to real world problems. But we go "top down": start with complete useful solutions to real world problems, and gradually work down to the basic foundations. Education experts recommend this approach for more effective learning. For more information, have a look at this article that discusses the fast.ai teaching philosophy: [Providing a Good Education in Deep Learning](https://www.fast.ai/2016/10/08/teaching-philosophy/).
You'll see that fast.ai's way of teaching is very different from what you might be used to, if you did a technical degree at university. Nearly all technical subjects at university are taught "bottom up": start with basic foundations, and gradually work up to complete useful solutions to real world problems. But we go "top down": start with complete useful solutions to real world problems, and gradually work down to the basic foundations. Education experts recommend this approach for more effective learning. For more information, have a look at this article that discusses the fast.ai teaching philosophy: [Providing a Good Education in Deep Learning](https://www.fast.ai/2016/10/08/teaching-philosophy/).

- How to learn - highly recommended books for fast.ai students
- [Meta Learning](https://radekosmulski.gumroad.com/l/learn_deep_learning)
Expand All @@ -59,7 +59,7 @@ You'll see that fast.ai's way of teaching is very different to what you might be

## If you need help

There's lot of helpful people, and helpful answers to past questions, on the [fast.ai forums](https://forums.fast.ai/c/p1v5/54). There are special help topics for beginner questions, to ensure that your questions aren’t missed:
There are a lot of helpful people, and helpful answers to past questions, on the [fast.ai forums](https://forums.fast.ai/c/p1v5/54). There are special help topics for beginner questions, to ensure that your questions aren’t missed:

- [Help: Setup](https://forums.fast.ai/t/help-setup/95289)
- [Help: Creating a dataset, and using Gradio / Spaces](https://forums.fast.ai/t/help-creating-a-dataset-and-using-gradio-spaces/96281)
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8 changes: 4 additions & 4 deletions index.qmd
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Expand Up @@ -94,7 +94,7 @@ Previous fast.ai courses have been studied by hundreds of thousands of students,

It doesn't matter if you don't come from a technical or a mathematical background (though it's okay if you do too!); we wrote this course to make deep learning accessible to as many people as possible. The only prerequisite is that you know how to code (a year of experience is enough), preferably in Python, and that you have at least followed a high school math course.

Deep learning is a computer technique to extract and transform data–-with use cases ranging from human speech recognition to animal imagery classification–-by using multiple layers of neural networks. A lot of people assume that you need all kinds of hard-to-find stuff to get great results with deep learning, but as you'll see in this course, those people are wrong. Here's a few things you *absolutely don't need* to do world-class deep learning:
Deep learning is a computer technique to extract and transform data–-with use cases ranging from human speech recognition to animal imagery classification–-by using multiple layers of neural networks. A lot of people assume that you need all kinds of hard-to-find stuff to get great results with deep learning, but as you'll see in this course, those people are wrong. Here are a few things you *absolutely don't need* to do world-class deep learning:

| Myth (don't need) | Truth
|---|---|
Expand All @@ -114,9 +114,9 @@ In this course, you'll be using [PyTorch](https://pytorch.org/), [fastai](https:

We've completed hundreds of machine learning projects using dozens of different packages, and many different programming languages. At fast.ai, we have written courses using most of the main deep learning and machine learning packages used today. We spent over a thousand hours testing PyTorch before deciding that we would use it for future courses, software development, and research. PyTorch is now the world's fastest-growing deep learning library and is already used for most research papers at top conferences.

PyTorch works best as a low-level foundation library, providing the basic operations for higher-level functionality. The fastai library one of the most popular libraries for adding this higher-level functionality on top of PyTorch. In this course, as we go deeper and deeper into the foundations of deep learning, we will also go deeper and deeper into the layers of fastai.
PyTorch works best as a low-level foundation library, providing the basic operations for higher-level functionality. The fastai library is one of the most popular libraries for adding this higher-level functionality on top of PyTorch. In this course, as we go deeper and deeper into the foundations of deep learning, we will also go deeper and deeper into the layers of fastai.

Transformers is a popular library focused on natural language processing (NLP) using *transformers models*. In the course you'll see how to create a cutting-edge transfomers model using this library to detect similar concepts in patent applications.
Transformers is a popular library focused on natural language processing (NLP) using *transformers models*. In the course you'll see how to create a cutting-edge transformers model using this library to detect similar concepts in patent applications.

:::{.callout-tip}
## Get started
Expand Down Expand Up @@ -185,7 +185,7 @@ Each video is designed to go with various chapters from the book. The entirety o

We'll mainly use [Kaggle Notebooks](https://www.kaggle.com/docs/notebooks) and [Paperspace Gradient](https://gradient.run/notebooks) because we've found they work really well for this course, and have good free options. We also will do some parts of the course on your own laptop. (If you don't have a Paperspace account yet, sign up with [this link](https://console.paperspace.com/signup?R=lg6rnx) to get $10 credit -- and we get a credit too.)

We strongly suggest *not* using your own computer for training models in this course, unless you're very experienced with Linux system adminstration and handling GPU drivers, CUDA, and so forth.
We strongly suggest *not* using your own computer for training models in this course, unless you're very experienced with Linux system administration and handling GPU drivers, CUDA, and so forth.

If you need help, there's a [wonderful online community](https://forums.fast.ai/c/p1v5/54) ready to help you at forums.fast.ai. Before asking a question on the forums, search carefully to see if your question has been answered before.

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