Member-only story
I have recently taken a free course with Alison, which is Diploma in Machine Learning with Tensorflow. It was a pretty long course, as free courses go, and a bit dated because it covered Tensorflow version 2.0. In this blog post I will endeavour to cover the core concepts of the course as they relate to the current version of Google’s library.
What is Tensorflow?
Tensorflow is an end-to-end open source platform for machine learning. It is an open-source software library developed by the Google Brain team for numerical computation using data flow graphs. It was originally designed for large-scale machine learning and deep neural network models, but can also be used for a wide range of other numerical computations such as scientific simulations, data analysis, and other applications. TensorFlow allows developers to create and train machine learning models using a variety of high-level APIs, including Keras, Estimators, and Eager Execution. It also supports distributed computing across multiple CPUs and GPUs, making it possible to train and deploy large-scale machine learning models on a variety of platforms, including desktop computers, servers, and mobile devices.
Tensorflow will work on CPU’s, GPU’s, and TPU’s.
