Packt / Haohan Wang, Christian Fanli Ramsey | Duration: 5 h 57 m | Video: H264 1920x1080 | Audio: AAC 48 kHz 2ch | 1,79 GB | Language: English | 2018 Deep Learning uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation on large volumes of data in order to make decisions about high dimensional data. If you're looking to scale out your Deep Learning models and deploy your model into production then look no further because this video course will help you get the most out of TensorFlow and Keras to accelerate the training of your Deep Learning models and deploy your model at scale on the Cloud. Tools and frameworks such as TensorFlow, Keras, and Google Cloud MLE are used to showcase the strengths of various approaches, trade-offs, and building blocks for creating, training and evaluating your distributed deep learning models with GPU(s) and deploying your model to the Cloud. You will learn how to design and train your deep learning models and scale them out for larger datasets and complex neural network architectures on multiple GPUs using Google Cloud ML Engine. You'll learn distributed techniques such as how parallelism and distribution work using low-level TensorFlow and high-level TensorFlow APIs and Keras. Towards the end of the course, you will develop, train, and deploy your models using TensorFlow and Google Cloud Machine Learning Engine.
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