Deep Learning with TensorFlow (beginner to expert level)

About this course

A warm welcome to the Deep Learning with TensorFlow course by Uplatz.

TensorFlow is an end-to-end open-source machine learning / deep learning platform. It has a comprehensive ecosystem of libraries, tools, and community resources that lets AI/ML engineers, scientists, analysts build and deploy ML-powered deep learning applications. The name TensorFlow is derived from the operations which neural networks perform on multidimensional data arrays or tensors. Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain.

TensorFlow is a machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library do to numerical computations, which in itself doesn’t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.

In simple words, TensorFlow is an open-source and most popular deep learning library for research and production. TensorFlow in Python is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks. TensorFlow manages to combine a comprehensive and flexible set of technical features with great ease of use.

There have been some remarkable developments lately in the world of artificial intelligence, from much publicized progress with self-driving cars to machines now composing imitations or being really good at video games. Central to these advances are a number of tools around to help derive deep learning and other machine learning models, with Torch, Caffe, and Theano amongst those at the fore. However, since Google Brain went open source in November 20

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