Deep Learning With Python: All You Need To Know | 7wData

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Python is a general-purpose high level programming language that is widely used in data science and for producing Deep learning algorithms.

This article introduces Python and its libraries like NumPy, SciPy, Pandas, Matplotlib; frameworks like Theano, TensorFlow, Keras. It also explains how the different libraries and frameworks can be applied to solve complex real world problems.

Deep structured learning or hierarchical learning or deep learning in short is part of the family of Machine Learning methods which are themselves a subset of the broader field of Artificial Intelligence.

Deep learning is a class of Machine Learning algorithms that use several layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.

Deep neural networks, deep belief networks and recurrent neural networks have been applied to fields such as computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, and bioinformatics where they produced results comparable to and in some cases better than human experts have.

Here , we will learn about the environment set up for Python Deep Learning. We have to install the following software for making deep learning algorithms.

It is strongly recommend that Python, NumPy, SciPy, and Matplotlib are installed through the Anaconda distribution. It comes with all of those packages.

We need to ensure that the different types of software are installed properly.

Let us go to our command line program and type in the following command −

Next, we can import the required libraries and print their versions −

Before we begin with the installation of the packages − Theano, TensorFlow and Keras, we need to confirm if the pip is installed. The package management system in Anaconda is called the pip.

To confirm the installation of pip, type the following in the command line −

Once the installation of pip is confirmed, we can install TensorFlow and Keras by executing the following command −

Confirm the installation of Theano by executing the following line of code −

Confirm the installation of Tensorflow by executing the following line of code −

Confirm the installation of Keras by executing the following line of code −

Artificial Intelligence (AI) is any code, algorithm or technique that enables a computer to mimic human cognitive behaviour or intelligence. Machine Learning (ML) is a subset of AI that uses statistical methods to enable machines to learn and improve with experience. Deep Learning is a subset of Machine Learning, which makes the computation of multi-layer neural networks feasible. Machine Learning is seen as shallow learning while Deep Learning is seen as hierarchical learning with abstraction.

One of the major challenges encountered in traditional machine learning models is a process called feature extraction. The programmer needs to be specific and tell the computer the features to be looked out for. These features will help in making decisions.

Entering raw data into the algorithm rarely works, so feature extraction is a critical part of the traditional machine learning workflow.

This places a huge responsibility on the programmer, and the algorithm’s efficiency relies heavily on how inventive the programmer is. For complex problems such as object recognition or handwriting recognition, this is a huge issue.

Deep learning, with the ability to learn multiple layers of representation, is one of the few methods that has help us with automatic feature extraction. The lower layers can be assumed to be performing automatic feature extraction, requiring little or no guidance from the programmer.

The Artificial Neural Network, or just neural network for short, is not a new idea. It has been around for about 80 years. The act of sending data straight through a neural network is called a feed forward neural network.