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!
[ Ссылка ]

Links Mentioned
Tensorflow Documentation: [ Ссылка ]
Pandas Crash Course: [ Ссылка ]

If you have any questions, please drop a comment below!

Oh, and don't forget to connect with me!
LinkedIn: [ Ссылка ]
Facebook: [ Ссылка ]
GitHub: [ Ссылка ]

Happy coding!
Nick

P.s. Let me know how you go and drop a comment if you need a hand!

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