TensorFlow: Advanced Techniques Specialization

What you will learn from this course

• Understand the underlying basis of the Functional API and build exotic non-sequential model types, custom loss functions, and layers.
• Learn optimization and how to use GradientTape & Autograph, optimize training in different environments with multiple processors and chip types.
• Practice object detection, image segmentation, and visual interpretation of convolutions.
• Explore generative deep learning, and how AIs can create new content, from Style Transfer through Auto Encoding and VAEs to GANs.
• Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network.
• Build custom loss functions (including the contrastive loss function used in a Siamese network) in order to measure how well a model is doing and help your neural network learn from training data.
• Build off of existing standard layers to create custom layers for your models, customize a network layer with a lambda layer, understand the differences between them, learn what makes up a custom layer, and explore activation functions.
• Learn how to define your own custom class instead of using the Functional or Sequential APIs, build models that can be inherited from the TensorFlow Model class, and build a residual network (ResNet) through defining a custom model class.
• Learn about Tensor objects, the fundamental building blocks of TensorFlow, understand the difference between the eager and graph modes in TensorFlow
• Learn how to use a TensorFlow tool to calculate gradients.
• Build your own custom training loops using GradientTape and TensorFlow Datasets to gain more flexibility and visibility with your model training.
• Learn about the benefits of generating code that runs in graph mode, take a peek at what graph code looks like, and practice generating this more efficient code automatically with TensorFlow’s tools.
• Harness the power of distributed training to process more data and train larger models, faster, get an overview of various distributed training strategies, and practice working with a strategy that trains on multiple GPU cores, and another that trains on multiple TPU cores.
• Explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection.
• Learn neural style transfer using transfer learning: extract the content of an image (eg. swan), and the style of a painting (eg. cubist or impressionist), and combine the content and style into a new image.
• Learn about GANs; their invention, properties, architecture, and how they vary from VAEs, understand the function of the generator and the discriminator within the model, the concept of 2 training phases and the role of introduced noise, and build your own GAN that can generate faces.

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