Today, we will see Deep Learning with Python Tutorial. Deep Learning, a Machine Learning method that has taken the world by awe with its capabilities. In this Deep Learning Tutorial Python, we will discuss the meaning of Deep Learning With Python. Also, we will learn why we call it Deep Learning. Moreover, this Python Deep learning Tutorial will go through artificial neural networks and Deep Neural Networks, along with deep learning applications.

Deep learning is the new big trend in machine learning. It had many recent successes in computer vision, automatic speech recognition and natural language processing. The goal of this blog post is to give you a hands-on introduction to deep learning. To do this, we will build a Cat/Dog image classifier using a deep learning algorithm called convolutional neural network (CNN) and a Kaggle dataset. This post is divided into 2 main parts. The first part covers some core concepts behind deep learning, while the second part is structured in a hands-on tutorial format. In the first part of the hands-on tutorial (section 4), we will build a Cat/Dog image classifier using a convolutional neural network from scratch.

Nowadays, there are many types of neural networks in deep learning which are used for different purposes. In this article, we will go through the most used topologies in neural networks, briefly introduce how they work, along with some of their applications to real-world challenges. This article is our third tutorial on neural networks, to start with our first one, check out neural networks from scratch with Python code and math in detail. The perceptron model is also known as a single-layer neural network. In this type of neural network, there are no hidden layers.

Neural Networks is one of the most popular machine learning algorithms at present. It has been decisively proven over time that neural networks outperform other algorithms in accuracy and speed. With various variants like CNN (Convolutional Neural Networks), RNN(Recurrent Neural Networks), AutoEncoders, Deep Learning etc. neural networks are slowly becoming for data scientists or machine learning practitioners what linear regression was one for statisticians. It is thus imperative to have a fundamental understanding of what a Neural Network is, how it is made up and what is its reach and limitations. This post is an attempt to explain a neural network starting from its most basic building block a neuron, and later delving into its most popular variations like CNN, RNN etc.