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Practical Deep Learning for Coders
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Welcome to *Practical Deep Learning for Coders*. This web site covers
the book and the 2022 version of the course, which are designed to work
closely together. If you haven’t yet got the book, you can [buy it
here](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527).
It’s also [freely available](https://github.com/fastai/fastbook) as
interactive Jupyter Notebooks; read on to learn how to access them..

## How do I get started?

If you’re ready to dive in right now, here’s how to get started. If you
want to know more about this course, read the next sections, and then
come back here.

To watch the videos, click on the *Lessons* section in the navigation
sidebar. The lessons all have searchable transcripts; click “Transcript
Search” in the top right panel to search for a word or phrase, and then
click it to jump straight to video at the time that appears in the
transcript. The videos are all captioned; while watching the video click
the “CC” button to turn them on and off.

Each video to designed to go with various chapters from the book. The
entirety of every chapter of the book is available as an interactive
Jupyter Notebook. [Jupyter Notebook](https://jupyter.org/) 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.

In the course we 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.

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. (The forum system
won’t let you post until you’ve spent a few minutes on the site reading
existing topics.)

## Is this course for me?

Thank you for letting us join you on your deep learning journey, however
far along that you may be! Previous fast.ai courses have been studied by
hundreds of thousands of students, from all walks of life, from all
parts of the world. Many students have told us about how they’ve become
[multiple gold medal
winners](https://forums.fast.ai/t/my-first-gold-medal/54237) of
[international machine learning
competitions](https://towardsdatascience.com/my-3-year-journey-from-zero-python-to-deep-learning-competition-master-6605c188eec7),
[received
offers](https://forums.fast.ai/t/how-has-your-journey-been-so-far-learners/6480/2)
from top companies, and having
[research](https://ui.adsabs.harvard.edu/abs/2020EGUGA..2221465A/abstract)
[papers](http://www.ieomsociety.org/ieom2020/papers/37.pdf)
[published](https://pubs.rsna.org/doi/abs/10.1148/ryai.2019190113?journalCode=ai).
For instance, Isaac Dimitrovsky [told
us](https://forums.fast.ai/t/thanks-ra2-dream-challenge-win/76875) that
he had “*been playing around with ML for a couple of years without
really grokking it… \[then\] went through the fast.ai part 1 course late
last year, and it clicked for me*”. He went on to achieve first place in
the prestigious international [RA2-DREAM
Challenge](https://www.synapse.org/#!Synapse:syn20545111/wiki/594083)
competition! He developed a [multistage deep learning
method](https://www.synapse.org/#!Synapse:syn21478998/wiki/604432) for
scoring radiographic hand and foot joint damage in rheumatoid arthritis,
taking advantage of the fastai library.

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. The first three chapters have been explicitly
written in a way that will allow executives, product managers, etc. to
understand the most important things they’ll need to know about deep
learning – if that’s you, just skip over the code in those sections.

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:

| Myth (don’t need)           | Truth                                                        |
|-----------------------------|--------------------------------------------------------------|
| Lots of math                | Just high school math is sufficient                          |
| Lots of data                | We’ve seen record-breaking results with \<50 items of data   |
| Lots of expensive computers | You can get what you need for state of the art work for free |

Deep learning has power, flexibility, and simplicity. That’s why we
believe it should be applied across many disciplines. These include the
social and physical sciences, the arts, medicine, finance, scientific
research, and many more. Here’s a list of some of the thousands of tasks
in different areas at which deep learning, or methods heavily using deep
learning, is now the best in the world:

-   **Natural language processing (NLP)** Answering questions; speech
    recognition; summarizing documents; classifying documents; finding
    names, dates, etc. in documents; searching for articles mentioning a
    concept
-   **Computer vision** Satellite and drone imagery interpretation
    (e.g., for disaster resilience); face recognition; image captioning;
    reading traffic signs; locating pedestrians and vehicles in
    autonomous vehicles
-   **Medicine** Finding anomalies in radiology images, including CT,
    MRI, and X-ray images; counting features in pathology slides;
    measuring features in ultrasounds; diagnosing diabetic retinopathy
-   **Biology** Folding proteins; classifying proteins; many genomics
    tasks, such as tumor-normal sequencing and classifying clinically
    actionable genetic mutations; cell classification; analyzing
    protein/protein interactions
-   **Image generation** Colorizing images; increasing image resolution;
    removing noise from images; converting images to art in the style of
    famous artists
-   **Recommendation systems** Web search; product recommendations; home
    page layout
-   **Playing games** Chess, Go, most Atari video games, and many
    real-time strategy games
-   **Robotics** Handling objects that are challenging to locate (e.g.,
    transparent, shiny, lacking texture) or hard to pick up
-   **Other applications** Financial and logistical forecasting, text to
    speech, and much more…

## Your teacher

I am Jeremy Howard, your guide on this journey. I lead the development
of fastai, the software that you’ll be using throughout this course.

I have been using and teaching machine learning for around 30 years. I
started using neural networks 25 years ago. During this time, I have led
many companies and projects that have machine learning at their core,
including founding the first company to focus on deep learning and
medicine, Enlitic, and taking on the role of President and Chief
Scientist of the world’s largest machine learning community, Kaggle. I
am the co-founder, along with Dr. Rachel Thomas, of fast.ai, the
organization behind this course.

At fast.ai we care a lot about teaching. In this course, I start by
showing how to use a complete, working, very usable, state-of-the-art
deep learning network to solve real-world problems, using simple,
expressive tools. And then we gradually dig deeper and deeper into
understanding how those tools are made, and how the tools that make
those tools are made, and so on… We always teaching through examples. We
ensure that there is a context and a purpose that you can understand
intuitively, rather than starting with algebraic symbol manipulation.

## The software you will be using

In this course, you’ll be using [PyTorch](https://pytorch.org/),
[fastai](https://docs.fast.ai), Hugging Face
[Transformers](https://huggingface.co/docs/transformers/index), and
[Gradio](https://gradio.app/).

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.

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.

## What you will learn

After finishing this course you will know:

-   How to train models that achieve state-of-the-art results in:
    -   Computer vision, including image classification
        (e.g., classifying pet photos by breed), and image localization
        and detection (e.g., finding where the animals in an image are)
    -   Natural language processing (NLP), including document
        classification (e.g., movie review sentiment analysis) and
        phrase similarity
    -   Tabular data with categorical data, continuous data, and mixed
        data
    -   Collaborative filtering (e.g., movie recommendation)
-   How to turn your models into web applications, and deploy them
-   Why and how deep learning models work, and how to use that knowledge
    to improve the accuracy, speed, and reliability of your models
-   The latest deep learning techniques that really matter in practice
-   How to implement stochastic gradient descent and a complete training
    loop from scratch

Here are some of the techniques covered (don’t worry if none of these
words mean anything to you yet–you’ll learn them all soon):

-   Random forests and gradient boosting
-   Affine functions and nonlinearities
-   Parameters and activations
-   Transfer learning
-   Stochastic gradient descent (SGD)
-   Data augmentation
-   Weight decay
-   Image classification
-   Entity and word embeddings
-   And much more