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Deep Learning Models For Beginners

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Understanding the AI’s peculiarities, beginners must understand the examples of deep learning models used across the industry.

Deep learning is a very important aspect to learn and understand artificial intelligence. It is a subset of machine learning that processes a large number of datasets, to identify patterns in human behaviour. Deep learning algorithms are trained in a manner that it accumulates, analyses and processes exponential datasets, without any human intervention. The use of deep learning model ranges from Google search to Amazon’s recommendation engine. Owing to its robust mechanism, it is getting promptly adopted across the industry.

Understanding AI has become one of the most demanded skills across the industry. To fully comprehend the peculiarities of AI, deep learning models come in handy. Here are the examples of 10 deep learning models that will help beginners to understand AI better.


Detectron is Facebook AI Research’s software system integrated with object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework. To provide a high-quality, high-performance codebase for object detection research, Detectron is designed to be flexible to support rapid implementation and evaluation of novel research.


WaveGlow is a flow-based network for speech synthesis designed by NVIDIA. It is capable of generating high-quality speech from Mel-spectrograms. It provides fast, efficient and high-quality audio synthesis, without the need for auto-regression.


OpenCog is a framework for developing AI systems, especially appropriate for integrative multi-algorithm systems, and artificial general intelligence systems. It has an assortment of natural language processing systems for expressing thoughts, reading, hearing, and using logical expressions.

Chatbot with Machine Learning Translation

Chatbot with Neural Machine Translation (NMT) is a vast artificial neural network that uses deep learning and feature learning to model full sentences with machine translation. It specializes in producing continuous sequences of words better than the traditional approach of using a recurrent neural network (RNN) because it mimics how humans translate sentences.

IBM Watson

Integrated with deep learning algorithms, IBM Watson performs smart image analysis, scientific research, drug discovery, prediction of health problems and symptoms of diseases amongst others.

Image Caption Generator

Image Caption Generator is a form of deep learning model that generates captions for an image. Combined with the capabilities of computer vision and natural language processing, it uses convolutional neural networks and LTSM units.

Music Genre Classification 

Music genre Classification creates a deep learning model that automatically classifies music based on genre. It uses GTZAN genre classification dataset for classifying the music genre.

Image Classification

Image Classification is a form of deep learning model, which is used to build a convolutional neural network model in Pytorch for classifying images. Built to promptly classify images, image classification forms an integral part to train the deep learning datasets.

Face Detection System

Face Detection system is an example of deep learning model, which is used to identify the pattern and track human images.

Visual Tracking System

The visual Tracking system is an application of computer vision technique integrated with deep learning model is used to perform tasks such as surveillance, traffic control, and video editing amongst others.

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