One-hot Encoding explained

In this video, we discuss what one-hot encoding is, how this encoding is used in machine learning and artificial neural networks, and what is meant by having one-hot encoded vectors as labels for our input data.

🕒🦎 VIDEO SECTIONS 🦎🕒

00:30 Help deeplizard add video timestamps - See example in the description
05:30 Collective Intelligence and the DEEPLIZARD HIVEMIND

💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥

👋 Hey, we're Chris and Mandy, the creators of deeplizard!

👉 Check out the website for more learning material:

💻 ENROLL TO GET DOWNLOAD ACCESS TO CODE FILES

🧠 Support collective intelligence, join the deeplizard hivemind:

🧠 Use code DEEPLIZARD at checkout to receive 15% off your first Neurohacker order
👉 Use your receipt from Neurohacker to get a discount on deeplizard courses

👀 CHECK OUT OUR VLOG:

❤️🦎 Special thanks to the following polymaths of the deeplizard hivemind:
Tammy
Mano Prime
Ling Li

🚀 Boost collective intelligence by sharing this video on social media!

👀 Follow deeplizard:

🎓 Deep Learning with deeplizard:

🎓 Other Courses:

🛒 Check out products deeplizard recommends on Amazon:

🎵 deeplizard uses music by Kevin MacLeod

❤️ Please use the knowledge gained from deeplizard content for good, not evil.

[harshtiwari]

Youtube recommendation system must be really broken since it is not recommending me this magic!

[Wopara]

Thank God I came across your channel, scientist these days just like to sound smart

[sarajgupta]

OMG. Such a brilliant explanation. I have been watching your videos for almost 3 hours and couldn't stop myself to watch the next one each time. Subscribed, liked and commented. Thank you so much.

[fazankabir]

OMG. Such a brilliant explanation. I have been watching your videos from past almost 1 hour and couldn't stop myself to watch the next one each time. Subscribed, liked and commented. Thank you so much. You are life saver!

[bhang]

- One Hot encoding is one way of converting categorical data into a (sparase) vector
- Each one hot code represent the category of the data. So that machine can interpret each word.
- As you add more category, the length of the vector increases.

[SaidElnaffar]

A very high-quality video -- I got the idea in no time. Thanks.

[NotBob]

Just went through the entire series to date and found it very helpful and easy to follow.  I'm sure you have.a plan all mapped out but further topics might be in why you would want/need to add layers, how many nodes/neurons you'd want in each layer and how to optimize those (I.e. find out you don't have enough or too many), finding out when you have hit local minimums, choosing different functions such as relu, tanh, etc.  You're really on a good roll here and I look forward to more!

[tymothylim]

Thank you so much for this video! Now I finally understand what this term means!

[synthstatic]

I'm in a coding bootcamp and this explained the concept far better than the lesson.

[odds]

Within 3 minutes my minds been completely opened

[deepcodes]

Good quality of both video and blogs, Really amazed by your work!!

[ashita]

Thank you so much :) Clear and to the point

[milenaramirez]

Easy to understand, even for foreigners. Thanks a lot! :)

[Shkencetari]

This was really well explained. Thank you very much.

[cryptorick]

Love the channel!!! Killin it! Subscribed!

[broncioaguilar]

😊perfect explanation! Loved the animals too 🐈‍⬛🐕🦎🦙 thanks!!

[cloudkungfu]

This explanation was super clear! Awesomeee

[muhammadtalhabaig]

Thank you! You explain things really well! 😃

[billywhite]

great video! really good teaching, simple and engaging. bless you for making this

[travel]

That was soo helpful! Thanks so much for the animations and detail in the explaination