fastai course how much time

What is the goal of the fastai course?

The goal is to have something much more than a “fastai recipe book,” where instead authors can explore in-depth into topics involving the usage of the fastai library. You can find available courses and tutorials by topic below. Come learn how to use fastai from an application-based approach, diving into multiple case studies per lecture.

What is the fastai library?

The fastai library is the most popular library 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.

How do I start talking about fastai?

Of course, to discuss fastai, you can use our forums, and be sure to look through the fastai docs too. Don’t worry if you’re just starting out—little, if any, of those docs and forum threads will make any sense to you just now.

What is fastai and is it worth the hype?

When I first heard about this powerful AI library that everyone seemed to be talking about, I was intrigued. FastAI — as its name stands, boasts to help coders deep dive into the vast and complicated world of deep learning in just a few lines of code and an extremely minimal setup too.

Can we learn AI in 3 months?

Artificial Intelligence The real world projects from the industry experts would definitely give all the course takers to become a practical expert for the field of AI for Robotics. The course usually takes 2.5 to 3 months to complete and can be easily done along with a full-time job!

Is fastai good to learn?

as a free course to teach people with basic coding experience state-of-the-art deep learning techniques. Without much explanation of the underlying theories, with very few lines of code, student of fast.ai is capable of achieving astoundingly great results on its own domain quickly into the lessons.

How can I learn fastai?

Here are some things I would like to do:Follow people like Jeremy, Sebastian, Rachel, Radek on twitter.Look at papers they mention.Read the summary first.Go to paperswithcode.com and replicate the paper and code.Take a new paper and try to implement code from scratch.Do small projects.More items...•

How long does it take to learn AI from scratch?

about six monthsIf you're starting from scratch and learning the basics of AI, you should be able to do it in about six months. At that point, you can start looking for entry-level positions. If you're learning more complicated AI, such as data science, you may need an advanced degree that will take several years to earn.

What is the use of fastai?

fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches.

Is fast ai free?

Our online courses (all are free and have no ads): Practical Deep Learning for Coders.

What is FastBook?

Using the most powerful live retail market data available, FastBook calculates a simple vehicle grade based on consumer interest, market velocity, market supply, vehicle options, mileage and retail strategy. If a vehicle has a good grade, it's a good buy for your dealership.

What is deep network?

A deep neural network is a neural network with a certain level of complexity, a neural network with more than two layers. Deep neural networks use sophisticated mathematical modeling to process data in complex ways.

How do I load a fastai model?

0:484:12save and load fastai models - YouTubeYouTubeStart of suggested clipEnd of suggested clipFirst you use the export function to save the trained. Model next you use the load learner functionMoreFirst you use the export function to save the trained. Model next you use the load learner function to load the saved.

Is studying AI difficult?

Is AI hard to learn? Yes, it can be, and it's so hard that 93% of automation technologists themselves don't feel sufficiently prepared for upcoming challenges in the world of smart machine technologies. Companies face many challenges when implementing artificial intelligence.

Is AI coding hard?

While creating some artificial intelligence programs is easy, turning them into successful businesses can be challenging, according to experts at the Innovfest Unbound tech conference in Singapore. It can be difficult to make money if the AI program is not addressing a sufficiently large problem, the experts said.

Is AI a good career?

The field of artificial intelligence has a tremendous career outlook, with the Bureau of Labor Statistics predicting a 31.4 percent, by 2030, increase in jobs for data scientists and mathematical science professionals, which are crucial to AI.

Using a GPU

To do nearly everything in this course, you’ll need access to a computer with an NVIDIA GPU (unfortunately other brands of GPU are not fully supported by the main deep learning libraries).

Jupyter notebook

Once you’ve finished the steps in one of the guides above, you’ll be presented with a screen like this.

Our forums

Got stuck? Want to know more about some topic? Your first port of call should be forums.fast.ai. There are thousands of students and practitioners asking and answering questions there.

PyTorch and fastai

We teach how to train PyTorch models using the fastai library. These two pieces of software are deeply connected—you can’t become really proficient at using fastai if you don’t know PyTorch well, too. Therefore, you will often need to refer to the PyTorch docs.

Overview

There have been many major advances in NLP in the last year, and new state-of-the-art results are being achieved every month. NLP is still very much a field in flux, with best practices changing and new standards not yet settled on. This makes for an exciting time to learn NLP.

Traditional NLP Methods

For the first third of the course, we cover topic modeling with SVD, sentiment classification via naive bayes and logisitic regression, and regex. Along the way, we learn crucial processing techniques such as tokenization and numericalizaiton.

Deep Learning: Transfer learning for NLP

Jeremy shares jupyter notebooks stepping through ULMFit, his groundbreaking work with Sebastian Ruder last year to successfully apply transfer learning to NLP. The technique involves training a language model on a large corpus, fine-tuning it for a different and smaller corpus, and then adding a classifier to the end.

Deep Learning: Seq2Seq translation and the Transformer

We will dig into some underlying details of how simple RNNs work, and then consider a seq2seq model for translation. We build up our translation model, adding approaches such as teacher forcing, attention, and GRUs to improve performance. We are then ready to move on to the Transformer, exploring an implementation.

Ethical Issues in NLP

NLP raises important ethical issues, such as how stereotypes can be encoded in word embeddings and how the words of marginalized groups are often more likely to be classified as toxic. It was a special treat to have Stanford PhD student Nikhil Garg share his work which had been published in PNAS on this topic.

minGPT: a small and educational implementation of GPT by Andrej Karpathy

minGPT: a small and educational implementation of GPT in vanilla #PyTorch in ~300 lines of code by Andrej Karpathy: github.com/karpathy/minGPT

Free Intro to Pandas, Docker, Pyspark, ML and Pytorch courses

Hey gang -- I'm a longtime coding instructor, and used to put on a lot of in person workshops. I spent the last couple of weeks converting them into short youtube courses with colabs. With each of the topics I tried to give you the essentials to get moving with each. Enjoy!

Welcome to Walk with fastai!

This project was started by me (Zachary Mueller) as a way to collect interesting techniques dotted throughout the fast.ai forums, my own course materials, and the fantastic work of others into one centralized place.

Courses

Come learn how to use fastai from an application-based approach, diving into multiple case studies per lecture.