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Deep Learning Simplified

Encouraged by all the responses to my previous “Simplified” blog series on Reinforcement learning and Ensemble learning, I am writing this blog covering Deep learning basics in a step-by-step manner.

The primary aim of this blog is to enforce mastering the neural networks and related deep learning techniques conceptually. With the help of a complex pattern recognition problem, this blog covers the procedure to develop a typical neural network, which you will be able to use to solve a problem of similar complexity.

The first part of this blog series will introduce to you what deep learning is in simple terms. The following representation shows all the learning methods covered in this book, highlighting the primary subject of learning in this blog — Deep learning.

Let’s first recap the premise of Machine learning and reinforce the purpose and context of learning methods. As we learned, Machine learning is about training machines by building models using observational data, against directly writing specific instructions that define the model for the data to address a particular classification or a prediction problem. The word model is nothing but a system in this context.

The program or system is built using data and hence, looks as though it’s very different from a hand-written one. If the data changes, the program also adapts to it for the next level of training on the new data. So, all it needs is the ability to process large-scale as against getting a skilled programmer to write for all the conditions that could still prove to be massively erroneous. Some examples include recognizing patterns such as speech recognition, object recognition, face detection, and more.

Deep learning is a type of Machine learning that attempts to learn prominent features from the given data and thus, tries to reduce the task of building a feature extractor for every category of data (for example, image, voice, and so on.). For a face de