TensorFlow for Computer Vision Course – Full Python Tutorial for Beginners

Ecommerce Empire Academy

Learn how to use TensorFlow 2 and Python for computer vision in this complete course. The course shows you how to create two computer vision projects. The first involves an image classification model with a prepared dataset. The second is a more real-world problem where you will have to clean and prepare a dataset before using it.

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✏️ Nour Islam Mokhtari created this course. Connect with him here:

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⭐️ Course Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:01:21) Course outline
⌨️ (0:05:11) Who’s this course for
⌨️ (0:05:35) Why learn TensorFlow
⌨️ (0:06:25) We will be using an IDE and not notebooks
⌨️ (0:07:25) Visual Studio Code (how to download and install it)
⌨️ (0:10:50) Miniconda – how to install it
⌨️ (0:13:23) Miniconda – why we need it
⌨️ (0:17:24) How are we going to use conda virtual environments in VS Code?
⌨️ (0:21:20) Installing Tensorflow 2 (CPU version)
⌨️ (0:29:56) Installing Tensorflow 2 (GPU version)
⌨️ (0:43:34) What do we want to achieve?
⌨️ (0:45:26) Exploring MNIST dataset
⌨️ (1:05:54) Tensorflow layers
⌨️ (1:09:44) Building a neural network the sequential way
⌨️ (1:27:22) Compiling the model and fitting the data
⌨️ (2:00:52) Building a neural network the functional way
⌨️ (2:08:33) Building a neural network the Model Class way
⌨️ (2:14:31) Things we should add
⌨️ (2:18:29) Restructuring our code for better readability
⌨️ (2:23:11) First part summary
⌨️ (2:24:12) What we want to achieve
⌨️ (2:25:23) Downloading and exploring the dataset
⌨️ (2:34:20) Preparing train and validation sets
⌨️ (2:53:37) Preparing the test set
⌨️ (3:10:17) Building a neural network the functional way
⌨️ (3:22:12) Creating data generators
⌨️ (3:31:39) Instantiating the generators
⌨️ (3:35:37) Compiling the model and fitting the data
⌨️ (3:40:34) Adding callbacks
⌨️ (3:52:08) Evaluating the model
⌨️ (3:58:04) Potential improvements
⌨️ (4:08:49) Running prediction on single images
⌨️ (4:23:05) Second part summary
⌨️ (4:23:56) Where you can find me if you have questions

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35 thoughts on “TensorFlow for Computer Vision Course – Full Python Tutorial for Beginners”

  1. Man I sometimes I go through this channel and there is just so much to learn that I kind of feel overwhelmed a lil bit haha. Thank you for this course !

    1. LoL, very true. Especially as an IT guy, this channel reminds me every time “you don’t know anything” 😂

  2. I am about to start my graduation project and I swear this is the course that I needed, you can’t imagine how happy I am 😃.

  3. Fantastic. I am losing count of how many times I have said thank you and I am running out of of superlatives. The courses on here are just incredible.

  4. Nour Islam MOKHTARI

    Thank you very much for posting my course! For those who asked about the ML checklist, I will be sending it very soon, I did not expect this number of emails tbh, you guys are awesome!
    If you don’t get the checklist by tomorrow then please contact me on LinkedIn or Twitter, the links are in the description above.

    1. Why my progress bars in the Terminal create newlines to notify each iteration in every epoch, meanwhile yours terminal only display 1 lines of progress bar run at a time (new iteration replacing the old one)? What must I do?

  5. 19:35 if you have trouble with this step, it’s probably because the terminal is using powershell as a default and not cmd when you install vs code.

    1. Arianne Mae Lessly

      @Fake Thank you I’ve been searching for this one literally saving my time!

  6. Mohamed Mohudoom

    A great video, literally saved me from a huge amount of time and money for learning tf

  7. Hello thanks for the great course, one thing I noticed at the end was when we create test, val and train generators it sorts the directories as strings instead of integers, that’s the reason why you got 12 when you checked an image from the directory number 2 at the end. I actually couldn’t find the exact spot where we could solve that problem wisely and I was so lazy to come up with a decent solution tbh so did smth like this.

    Created a new dict:
    prediction_dict = {
    0 :0,
    1 :1 ,
    2 :10,
    3 :11,
    4 :12,
    5 :13,
    6 :14,
    7 :15,
    8 :16,
    9 :17,
    12:2 ,
    23:3 ,
    34:4 ,
    38:5 ,
    39:6 ,
    40:7 ,
    41:8 ,
    42:9 ,

    return this one from predict_with_model function instead:

    predictions = prediction_dict[np.argmax(predictions)]

    I hope that helps to the ones also experience the same thing

    1. Nour Islam MOKHTARI

      Good catch Eren, thank you! I will paste your comment to my pinned comment so that others can see it as well!

    2. @Nour Islam MOKHTARI Thanks a lot for the course again Islam, no problem I guess the ones who face the same problem will end up looking for such comment eventually and therefore they’ll find my answer. 😀

  8. Oluwatobi Akinlade

    Since macOS doesn’t support tensorflow GPU, and for the miniconda available for macOS there is not script file in order to activate the miniconda on VS code terminal. I would appreciate if you can give some guild for those learning with macOS.

  9. can you make a video on how to use javascript ,python and c++ and c and what software needed to use them and or how to get started as to using them for windows 10? Also can you provide insight on what can they be applied to, such as IP cameras, remote controlled devices, or anything that is a combination of electronics and coding.

  10. Thanks for the training. Would love to see a condensed version of this with real world examples. I got a little lost in the code. Some more visuals would be helpful.

  11. Great crash course on TensorFlow, and even better instructor who’s able to articulate the essential concepts well. Thanks, Nour!

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