TensorFlow Binary & Multi-class Classification

I'll teach a ton about how to use TensorFlow for Binary & Multi-class Classification and I'll answer all your questions live. I'll cover all of the following and much more :

❇️ Working with Tensors
❇️ Downloading / Cleaning Data
❇️ Binary Classification
❇️ Multi-class Classification
❇️ Finding Ideal Learning Rates
❇️ Correlation Matrix
❇️ Neural Network Regressions
❇️ Normalizing Data
❇️ Separate Features & Labels
❇️ Separate Training & Testing Data
❇️ Building Models
❇️ Compiling, Optimizing, Evaluating
❇️ Activation (What data is important)
❇️ Fitting, Epochs & More

📆 Next Video: June 1st at Noon EST / 4PM UTC

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#DeepLearning #Tensorflow #MachineLearning

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[derekbanas]

MY UDEMY COURSES ARE 87.5% OFF TIL May 23rd ($9.99)

[ChessFlix]

Thanks for continuing to make videos Derek! I'm not even particularly interested in Tensorflow right now, but I just enjoy your material.

If you're taking requests for the future, I'd love to see you cover some js/ts stuff with a frontend framework. React, Vue, Svelte, or Solid in TypeScript would be super entertaining.

[ianhaylock]

Hi, it would be nice if you could explain the differences between the different types of neural networks, and which type would be best for which type of data set.

[hakanbozcuk]

Thanks for your valuable instruction. In the binary classification part, how can you calculate the outcome(good or bad wine) for an unknown wine for which you have the features, buy using model_1?

[martinndungu]

This was awesome💪 looking forward to you doing NLP with tensorflow

[canozturk]

Hey Derek, can you please make a video about how to boost our confidence and social courage ? Thanks in advance.

[jordansocha]

1. In the binary classification dataset the target variable (quality) was distributed as 1, 271 rated as "good wine" and 5, 192 rated as "not good wine" for a total of 6, 463 samples. Since the output is heavily distributed toward "not good wine" will the model pickup on this bias and lean toward predicting "not good wine?" Does the dataset need to be "evened out" so the number of "good wine" and "not good wine" samples are the same (or at least roughly the same)?

2. I understand you set the random state to 66 for reproducibility; if you did not do this and you use tf.random.shuffle and ran the model many times would this be similar to cross-validation?

[broooth]

Hey Derek, you evaluated your model on the training data, not testing (59:33)

[morthim]

1:04:16
'what does correlation mean'?

[bhavinmoriya]

Thank yo very much for awesome videos as always. Don't we have to set sigmoid as activation in output layer, while we do binary classification?