Machine Learning and Deep Learning using Tensor Flow & Keras

This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning and also the basics of Machine learning. This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand and its application. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed.

That's just the average. And it's not just about money - it's interesting work too. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.

This is a comprehensive course with very crisp and straight forward intent. This course covers a variety of topics, including Neural Network BasicsTensorFlow detailed,Keras,Sonnet etcArtificial Neural NetworksTypes of Neural networkFeed forward networkRadial basis networkKohonen Self organizing mapsRecurrent neural NetworkModular Neural networksDensely Connected NetworksConvolutional Neural NetworksRecurrent Neural NetworksMachine Learning Deep Learning Framework comparisons There are many Deep Learning Frameworks out there, so why use TensorFlow. TensorFlow is an open source software library for numerical computation using data flow graphs.

Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.