Introduction to Tensor Flow using both Jupyter Noteooks and PyCharm. TimeLine included at the bottom so that you can skip to the part that interests you.
High-level APIs like tf.keras enable developers to train models easily and effectively. This session will introduce these APIs, and notebooks you can run live in the browser to get started using Colab.
We'll walk you through writing your first neural network in TensorFlow using just 10 lines of code with tf.keras, and then we’ll introduce you to Eager execution. We'll close with educational resources you can use to learn more about ML. By releasing easier and more intuitive APIs, we hope to make TensorFlow, an open-source machine learning framework more accessible for all. Rate this session by signing-in on the I/O website here → https://goo.gl/fZTwce Try TensorFlow with zero install → https://goo.gl/NrJAEz Train your first neural network with just 10 lines of code → https://goo.gl/6SRkzf Use the same Keras-compatible API with TensorFlow.js! → https://goo.gl/ZBbzJH Learn more about ML → https://goo.gl/36baeH
3:05 The Notebook URLs
3:50 Several Notebooks
5:44 Designing Experiments
6:50 Making TensorFlow Easy to Use.
8:05 Cell Bot (Biology Project)
9:15 Jupyter Notebooks.
10:55 Your First Neural Network (Keras)
13: Format of NMIST Data
14:10 Build the model
15:30 Memorizing Data Vs Pattern Recognition
16:35 Compile + Loss Functions
17:45 Explaining Gradient Descent
20:03 Like Tuning a guitar
21:23 Evaluate and Predict
22:38 Start Simple
24:39 Eager Execution
25:52 Second Speaker (TensorFlow with PyCharm)
28:27 Cats or Dogs
29:50 ML is very different
31:05 Debugging with PyCharm and Eager Mode
36:40 Recommended Resources
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