Keras with TensorFlow Course – Python Deep Learning and Neural Networks for Beginners Tutorial

20 thoughts on “Keras with TensorFlow Course – Python Deep Learning and Neural Networks for Beginners Tutorial”

1. Hi everyone! Hope you all learn and gain from this course! Come check out the other deep learning courses available on our channel! ❤️🦎

2. Amazing tutorial! I thought about improving the custom CNN model, and I got it up to 0.8993 val_accuracy. My model:

   model = Sequential([Conv2D(filters=8, kernel_size=(3,3), activation='relu', padding='same', input_shape=(224, 224, 3)),
   
   MaxPool2D(pool_size=(2,2), strides=2),
   
   Conv2D(filters=16, kernel_size=(3,3), activation='relu', padding='same'),
   
   MaxPool2D(pool_size=(2,2), strides=2),
   
   Conv2D(filters=32, kernel_size=(3,3), activation='relu', padding='same'),
   
   MaxPool2D(pool_size=(2,2), strides=2),
   
   Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'),
   
   MaxPool2D(pool_size=(2,2), strides=2),
   
   Conv2D(filters=128, kernel_size=(3,3), activation='relu', padding='same'),
   
   MaxPool2D(pool_size=(2,2), strides=2),
   
   Conv2D(filters=256, kernel_size=(3,3), activation='relu', padding='same'),
   
   MaxPool2D(pool_size=(2,2), strides=2),
   
   Flatten(),
   
   Dense(units=2048, activation='relu'),
   
   Dense(units=2, activation='softmax')
   
   ])

   I also changed the Adam learning rate from 0.0001 to 0.001(the default value) and the epochs to 30 and lastly I used all of the included 25000 pictures(9617/animal for training, 1922/animal for validation and 961/animal for testing)

   https://imgur.com/a/DprZDhl

3. what is "i in range" for?

4. Your very talented..thanks for your well explained instructions..

5. zzzz….. watch at 2x speed, thank me later

6. Abella Danger teaching us about ML is amazing

7. I always wanted to be trained by a lizard 🦎

8. This is the first long tutorial I watched from start to finish and it says a lot! Thank you very much!!!

9. You are so incredibly easy to listen to for hours on end, very well done I look forward to learning a bunch more from these videos

10. I can't understand your teaching is bcoz of your cute Smile..

11. Great work Mandy. I really enjoyed your video. I noticed though that " classes = cm_ plot_labels" wasn't defined in the video. Hence, my plot of the confusion matrix was somewhat different. I will be glad if you define the class. Thank you.

12. Who can share the link with the video of labeling data that she recommends in minute 1:18.00?

13. Thank you for this very good tutorial. You are a great teacher and very pleasant to listen to. I learned a ton.

14. The simulation generated an intelligent and beautiful women to help us all train our own deep learning concepts to become symbiotic; this is rad! We're rapidly reaching the singularity, art!

15. HELP

16. can we have acces to the code without paying the lesson on the website?

17. Brilliant illustarations and best DL material I have seen. Thank you, Andy.

18. In the chapter "Image Preparation for CNNs with TensorFlow's Keras API", when and how were the labels for the images defined in the code?

    My guess is that it was during the calls to ImageDataGenerator().flow_from_directory() via the machine matching the passed classes to the names of either the folders or the image files, but even if I'm right, I think that should have been addressed, even briefly, especially since if we want to follow these steps for our own data, we'll need to know how to tell the machine which images are in which class.

19. could anyone eexplain why i get bad accuracy with using the same code??

20. Hello, can you please tell me where can I find the information about how to process when a labelled image isn't given to us, while training the neural network. I am not able to find it out??
    Hope to hear from you soon
    Thank you

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