Deep Learning with Python is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Deep Learning architectures such as convolutional and recurrent neural networks (CNNs and RNNs) have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics and drug design.

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Deep learning with python is the new concept in machine learning. The core of deep learning is a neural network, a big computational graph with many layers between the input and output. This tutorial will guide you through the process of creating your first deep neural network model in Python, step by step.

Deep Learning is an area of Machine Learning concerned with algorithms that attempt to model high level abstractions in data. Deep learning architectures such as Convolutional Neural Networks have dramatically improved the state-of-the-art for many important challenging problems including image recognition, speech recognition, and natural language processing. In this post, we’ll walk through some basic concepts in deep learning and then describe the process we used to build a convolutional neural network from scratch.

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