Introduction and outline
Software setup
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We will be using an IDE and not notebooks (0:59)
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Visual Studio Code (how to download and install it) (3:24)
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Miniconda - how to install it (2:33)
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Miniconda - why we need it (4:00)
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How are we going to use conda virtual environments in VS Code? (3:56)
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Installing Tensorflow 2 (CPU version) (8:36)
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Installing Tensorflow 2 (GPU version) (13:38)
Image classification : MNIST example
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What do we want to achieve? (1:51)
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Exploring MNIST dataset (20:28)
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Tensorflow layers (3:49)
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Building a neural network the sequential way (17:38)
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Compiling the model and fitting the data (33:29)
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Building a neural network the functional way (7:41)
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Building a neural network the Model Class way (5:57)
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Things we should add (3:57)
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Restructuring our code for better readability (4:42)
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Summary (1:00)
Image classification : German Traffic Signs
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What we want to achieve (1:11)
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Downloading and exploring the dataset (8:57)
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Preparing train and validation sets (19:16)
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Preparing the test set (16:40)
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Building a neural network the functional way (11:55)
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Creating data generators (9:26)
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Instantiating the generators (3:58)
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Compiling the model and fitting the data (4:57)
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Adding callbacks (11:33)
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Evaluating the model (5:56)
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Potential improvements (10:45)
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Running prediction on single images (14:15)
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Summary (0:51)
Where to go from here?
