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Tensorflow Tutorial for Python in 10 Minutes
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- Опубліковано 15 сер 2024
- Want to build a deep learning model?
Struggling to get your head around Tensorflow?
Just want a clear walkthrough of which layer to use and why?
I got you!
Building neural networks with Tensorflow doesn’t need to be a nightmare. If you follow a couple of key steps you can be up and running and using Tensorflow to predict a whole bunch of stuff. In fact, you can learn how to do it with Python in just 10 minutes. By the end of this video you’ll have built your very own Tensorflow model to predict churn inside of a Jupyter Notebook.
What you'll learn:
1. Build a simple Tensorflow model to predict Churn
2. Training the model and make predictions on test data with Pandas
3. Save your model to disc and reload it to a Jupyter Notebook for reuse
Chapters
0:00 - Start
0:18 - Introduction
0:26 - What is Tensorflow
1:03 - Start of Coding
2:47 - Importing Tensorflow into a Notebook
3:48 - Building a Deep Neural Network with Fully Connected Layers
7:13 - Training/Fitting a Tensorflow Network
8:24 - Making Predictions with Tensorflow
9:15 - Calculating Accuracy from Tensorflow Predictions
9:50 - Saving Tensorflow Models
10:09 - Loading Tensorflow Models
GET THE CODE!
github.com/nic...
Links Mentioned
Tensorflow Documentation: www.tensorflow...
Pandas Crash Course: • Pandas for Data Scienc...
If you have any questions, please drop a comment below!
Oh, and don't forget to connect with me!
LinkedIn: / nicholasrenotte
Facebook: / nickrenotte
GitHub: github.com/nic...
Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand!
These videos are so good. A whole end-to-end project in 10 minutes. And a bit of humour and art tossed in there.
Thanks so much @Shivan! Glad you enjoyed it!
#TensorFlow-- python Library #Explanation with Example
ua-cam.com/video/ojevo88RVaE/v-deo.html
Yeah but you aren’t taught anything, you cant learn ml in 10 mins I’m sorry
@@Duhgy that is just to refresh some basics pertaining to Tensorflow. learning ML requires a hell lot of other steps from EDA to Feature Engineering to Feature Selection to HypterParameter Tuning.
@@NicholasRenotte ❤
I like how he is doing 10min tutorial but still included a humor intro
😆 gotta try to stay a little funny!
I normally never comment on tutorial videos but this was very excellently done! This was exceedingly concise and clear
This is the most amazing tutorial I have ever watched. I'm not ashamed to say I sometimes require extra explaining but this guy is just spot on with his explanations.
One thing I like about his videos is how basic he breaks down complex concepts for easy comprehension!
Having knowledge is one thing but passing that knowledge on is another. Nicholas is doing great at giving that knowledge!
Absolutely brilliant. End-to-end in just 10 minutes. Very explicit. Thanks for sharing
This tutorial is an absolute life-saver. Well done!
Didn't need to watch those 2 hours video. With your video, I was able to understand the base and the rest is just research and finding codes I need. This helped so much. Thank you! You are the best!
YESSS! Once you get the structuring it's all just a matter of building different architectures where needed!
Hey guys if your trying out this video in 2023 July like me you need this line changed for it to run X = pd.get_dummies(df.drop(['Churn', 'Customer ID'], axis=1), dtype=float)
the dtype=float is the most important was trying to figure why it wouldn't train all morning and just cracked it.
very helpful thanks!
Thank you so much !!
Went along with you and got .8 on the last epoch, but had .78 on the accuracy score. Loved this tutorial; it was so well explained. Thanks!
Awesome stuff @Tia, awesome work!
I've to say - great stuff, but you must be carefull with input dataset.
Because of some missing values in "Total Charges", it's treated as an object instead of series of numbers.
This leads to situation, when we feed layer with dimention over 6500 (which is close to cardinaltiy of training set - and this should be huge red flag - at least for example Random Forest prediction models are very bad in this circumstances).
After cleaning input dataset, we end up with dimention = 45, which is reasonable in this case.
#TensorFlow-- python Library #Explanation with Example
ua-cam.com/video/ojevo88RVaE/v-deo.html
Thanks for reaching the heart of the matter (4:07) so quickly and then explaining these '4 lines' so well.
Stoped you enjoyed it @Saptadeep!
Although this isn't an actual tutorial, it is cool to see you build a model so quickly!
Sir, as of all your other tutorials, it is so self-explanatory and clearly defined. Thank you so much.
#TensorFlow-- python Library #Explanation with Example
ua-cam.com/video/ojevo88RVaE/v-deo.html
epitome of greatness. Fashion Model looking guy having the best ML/AI lessons on youtube! xD
Excellent video, this was short, very clear, and easy follow. Great job, and thank you for this!
Top video, mate. Usually any Aussie who pronounces data as “day-ta” instead of the objectively superior “dah-ta” won’t win my respect. I’m willing to look past this for you xx
Cheers @Billy, I'll drop a "dah-ta" for you in one of the future videos 🤣my US colleagues have given up on trying to convert me!
This is a perfect introduction to sharing with people on any team that works with someone working with ML. :D
hey mate just watched your video and thought it was super useful to my learning. You explain everything very well (look good doing so) and left out the unimportant details. Thank you for this content!
Excellent presentation. Straight to the point, easy to follow and well explained.
I am about to choose a major at university as a high school student! you would be the one who has been inspiring me to learn AI! what an amazing channel bro!
YESSS, go getem!
Thank you! Up until now I've only done old skool supervised learning. With your tutorial I'm ready to enter the big leagues!!
Thankyou so much Nicholas, this is what I was looking for, whole story in 10 minutes, Tq so much,brilliant effort.
It's a bit of a crash course but it goes through the basics right?! 😃
Short and sweet! I'll add it to my memory palace. Thanks again.
A much needed video! Thank you for the great work!!
When i tried to run through this exercise i ran into an issue:
model.fit(X_train, y_train, epochs=200, batch_size=32)
gives errorValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type int).
So to get around this - I converted X and y train to float32
X_train = X_train.astype('float32')
y_train = y_train.astype('float32')
Later i ran into a similar issue with:
y_hat = model.predict(X_test)
y_hat = [0 if val < 0.5 else 1 for val in y_hat]
So again - converted X_test = X_test.astype('float32')
Everything seemed to complete as expected with 0.79 accuracy score.
Thoughts?
This was awesome man thanks. I got a good understanding of the flow of tensor flow and also the things I need to learn to become proficient. I def need to understand more about the different network types/shapes and their use cases, as well as the activation algorithms. Also is nice to know that I don't need to dive too deep into learning about the backpropagation and calculus because TF takes care of all of that!
I totally love your dedication on each videos bro !! Thanks for your hard work and keep it going !!!
#TensorFlow-- python Library #Explanation with Example
ua-cam.com/video/ojevo88RVaE/v-deo.html
Thanks a mil for checking it out @Nguyen Long!!
Can you please cover fall detection ?
I've been looking for tutorials on it for 6 hours on internet and i couldn't find a helpful resource..
you explain and makes things everything so easy to understand and no one does it like you !!
Thanks for sharing this! Can’t wait to watch some more of your content.
Thanks so much @Scarlett, plenty more to come!
I have been "tensored"! Hopefully this is the beginning of my AI career! Thank you
This is really useful. Give me a much clearer idea on how it works.
Understood one word out of 5, but this will for sure make me wants to work with it.
Exactly what I am looking for. Something straight forward
Add to it 1 month to start understanding what it is that you are doing and how to improve your models.
I remember my grad days for data science and this would still scare me for a test like that lol. Great video!
Hahahah, ikr, man I've been working with TimeDistributed layers right now and it's giving me the same nightmares!
How long and time consuming was grad school for data science? Could it be done with a full time data science job?
@@terristhompson9860 2.5 years and fairly time consuming given the various complex topics it covered (the college is nationally recognized). You'll need a heavy background in stats/computer sciences to obtain a data science job (a real one not just by name working in just excel or the like). One just can't go straight into becoming a lawyer without education just like data science.
@@protovici1476I’ll let you know if that last part is true or not after my interview next week.
Thanx for sharing your knowledge with us bro. U explain so easily and effectively
Watch it in 2x to learn Tensorflow in 5 minutes
This guy tensorflows
you didnt explain what "churn" means😓😓😓
Customer churn is the percentage of customers who stopped purchasing your business's products or services during a certain period of time. Your customer churn rate indicates how many of your existing customers are not likely to make another purchase from your business.
He's still learning 😅
@@amleth_prince_of_denmark thx!!
Perfect introduction, thanks a lot! Got me over that first big hurdle
You are the best, Nicholas. Just Brilliant!!
Finally I understood thank you for explicit expansion of those attributes.
This one video made me subscrib immediately to your channel and like all videos of you ! so perfect and time saving
This video is awesome, I have two questions because I'm new in Tensorflow,
1- Do we need to encode numeric data in the data sheet before we start building the model?, because I didn't see that in the video.
2- How we can map the prediction results 0, 1 to Yes, No as per the data sheet?
your videos are really helping man! appreciate it!
OK. you have the biggest eyes on the planet. YOU WIN!
so brilliantly!!!
Very unselfish 🙏💐
very helpful. made this seem "easy", which it def is not. Thanks!
Okay! This was much needed. Thenks!
Thanks so much @fukat!!
Dude, very nice compact video, thanks for making it!
So perfectly explained. Thank you
So glad you enjoyed it @Anirban!
EXCELLENT tutorial.
Thank you..
Awesome stuff @Mohamed, glad you enjoyed it!
0:23 _NICHOLAS RENOTTE - WORRIED ABOUT THE TIME LIMIT_ *Talking fast* LOL, that made me laugh really hard. I also enjoyed the video.
great! thanks dear Nicholas
Thank you for the awesome video.
great job and nice and concise work..
amazing video easy to understand
Lately I've been developing a large Tensorflow model, and I'm getting out of memory errors, from what I've learned it seems the best solution to this road block is gradient checkpointing, however there is little to no resources online about it. Could you make a video covering gradient checkpointing?
Great video, Nicholas.
wow great and fast ! thank you!
This was very helpful. Thank you very much.
How did you decide number of neurons to include in your sense layers? Do these relate to the number of feature columns in your data set at all? Or just a random/empiric choice?
This is brilliant! Well done Nicholas so helpful!
Thanks so much @Kevin! If you're interested, maybe check out Object Detection with Tensorflow next! ua-cam.com/video/yqkISICHH-U/v-deo.html
To the mark. Keep going!
awesome and simple explanation
Cheers mate u are doing brilliant things!
This was awesome
absolutly subscribed
I would like to resolve an error I came across when implementing the code:
Code to train the model for a certain amount of epochs:
model.fit(X_train, y_train, epochs=10, batch_size=32)
Error:
Failed to convert a NumPy array to a Tensor (Unsupported object type int).
same
good video. just wish you would've done the MNIST dataset
Great work...
Another great video 👏
Yeayyyahhh, thanks @Gustavo!!
What is Customer Churn?
Heya, it's to do with predicting customers that are likely to leave your business (e.g. go to another company or stop using your service altogether)!
Great video, veeery instructive and useful. ¡Gracias!
Yeh,It's Really helpful ,Thank you for the video
Subbed! The video is so good!
Great video..
Only thing that troubled me was the data selection using pandas but I will find out
Awesome! Want to share? Happy to help out!
😂loved the intro
🤣 not gonna lie, that was me when I first started learning it!
I love your videos! I have a small problem with this one though. This is rather keras and not tensorflow. With plain tensorflow you need lots more coding (which of course comes with greater flexibility)
Heya, ultimately using Keras with a Tensorflow backend. You still have a lot of flexibility running using the Sequential API, I'd agree though, there is a lot more flexibility using direct tensorflow layers. In my opionion however unless you're creating complex models or performing research it seems like overkill for most use cases.
Really great Churn Model explained in TensorFlow but,
why use pd.get_dummies() for the data preprocessing?
I had trouble understanding that as well, although in fairness to Nicholas, I think his real purpose was to show the process of TF neural network synthesis, as opposed to a real use case of one shot encoding of the columns. I dropped Monthly and Total Charges (and tenure as well) as I did not see any benefit of adding so many columns. Perhaps that was a vestige of an earlier video? Still pretty damn good for 10 minutes...
I could implement a churn model in rt thanks to Nicholas
Beautiful!!!!
What is the output of this ? Having a number like 0.8 is of no use when I want to see how many have churned. You could just have put a filter on the Excel sheet on the Churn column !
I had a little trouble getting the CSV file in place. It would have been great to point to the file upload capability in Colab. Other then that, awesome! THX.
Thank you for sharing. Your video has helped me learn a little more about TensorFlow with Python. 13may23
Great video!
Good video mate.
Very helpful
Nice, thanks.
Love This!
you are a rockstar.
Fantastic, after watching this video, making a couple of notes, I'm off to apply for an AI job at NASA.
Does it enable to train any kind of task required to achieve? can it learn from it for example how to do videos correctly? (Im a complete total noob in AI so I have no idea)
Please help. I ran the tutorial in google colab, got the model out to drive, then back into the colab notebook.
I dont understand what i am supposed to do with the model once it's ready.
This tutorial doesnt like, open it up and look at what it learned.
Can someone please offer guidance?
What is this second Dense layer for? You skip over it only saying it's a secondary layer. Why does it have a different number of units?
Thanks for this video Mick. I just think it would be better and more understandable for beginners if you go more into details and explain them more
Doing new vids on that as we speak!!
Why 32 and 64 units in the dense layers? How to know the no. of neurons to have in my NN layers?
You are amazing!
Compile - When computer turns code into machine language (the language understood by the computer)
How to fix AttributeError: module 'numpy' has no attribute 'object' while importing tensorflow?
these videos is very good how can i develop data set for deep learning model
I don't understand these 2 lines from section 0 Import data:
➡ X = pd.get_dummies (df.drop(['Churn', 'Customer ID'], axis=1))
➡ X_train, X_test, y_train, y_test = train_test_split (X, y, test_size=.2)
What's the difference between X_train and X_test?
Fantastic !!