DEEP LEARNING WITH TENSORFLOW

Overview

Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised or unsupervised. Artificial neural networks were inspired by information processing and distributed communication nodes in biological systems. There is a lot of different deep learning architecture which we will study in this deep learning using TensorFlow training course ranging from deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks. They have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection. Some of these architectures have known to produce better results than humans and will be discussed in deep learning TensorFlow training course. We will be using the open-source library known as TensorFlow while providing deep learning TensorFlow training course which is developed and maintained by Google Brain. TensorFlow is an API that could be used in Python (as a library) to build and deploy various deep learning models. TensorFlow is very much suited mostly for deep learning algorithms but we can also build a machine learning algorithm. We will understand more about TensorFlow in this course and explore the various operations that we can perform on it. There are many other options available apart from TensorFlow to build deep learning algorithms like Keras, Caffe framework, Mxnet, etc. Since TensorFlow is widely used in the industry, we also get a lot of community support for related issues.