This course will guide you on how to use Google's TensorFlow framework to build artificial neural networks for deep learning. This course aims to provide you with an easy-to-understand guide to the intricacies of Google's TensorFlow framework to make it easy to understand, Deep learning techniques.
This course is designed to balance theory and practical implementation with complete Jupyter Notebook code guides and easy reference slides and notes.
This course covers a variety of topics, including
There are many deep learning frameworks out there, so why use TensorFlow?
TensorFlow is an open-source software library for numerical computations using data flow graphs. The nodes of the graph represent mathematical operations, while the edges of the graph represent the multidimensional data arrays (tensors) communicated between them. Flexible architecture lets you deploy computations on one or more CPUs or GPUs on desktops, servers, or mobile devices 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 to conduct machine learning and deep neural network research, but the system is general enough to be applicable to a wide variety of other domains.
Expert: Machine Learning, Deep Learning, and Data Science
English Proficiency: IELTS Score: 6; Listening: 6.5, Reading: 6, Speaking: 5.5, Writing: 6.5