Mckenzie  Osiki

Mckenzie Osiki


Introduction to Deep Learning with Tensorflow

TensorFlow is one of the most popular Deep Learning libraries as it requires less computation power to produce accurate results in a given timeframe. Deep Learning is a subspace of Machine Learning that uses neural networks to process huge datasets and create Machine Learning models. According to Hacker News Hiring Trends, ML Developers and Engineers are in great demand and earn up to $144,885 per annum. TensorFlow is a great library to work with with Machine Learning and Deep Learning frameworks.

What is Deep Learning?

As mentioned, Deep Learning is a subspace of Machine Learning, which in turn is a subset of Artificial Intelligence that is inspired by the cognitive abilities of human beings. Similar to our brain’s biological neural networks (BNN), Deep Learning uses artificial neural networks that allow a machine to perform various tasks such as speech recognition, Natural Language Processing, object detection, and more.

TensorFlow in Deep Learning (DL) has a layered architecture with an end-to-end problem-solving approach. There are mainly three layers, an input layer, hidden layers, and an output layer. DL algorithms need huge amounts of data to be efficient and precise with the results.

Now, let’s move forward and understand the working of an artificial neural network (ANN). This will help you get the core concept of Deep Learning.

#machine learning

Introduction to Deep Learning with Tensorflow