Top 5 Recent Research on Machine Learning and Deep Learning

March 7, 2018
Posted by: Rohit Shelwante
Category: Blog

Table of Contents

Introduction: What are Machine Learning and Deep Learning?

Machine Learning is a subfield of AI and is a machine’s ability to learn from its environment by analyzing data in order to improve its performance. Deep Learning is the application of Machine Learning that uses a neural network of layered algorithms in an attempt to mimic the workings of neurons in the human brain.
These two fields are closely intertwined because deep learning enables machines to work more like humans and process information with fewer data.
Machine learning has given rise to many applications, such as voice recognition, search engine optimization, and content filtering. However, deep learning has been in development for longer and can be used for more complex tasks such as image recognition and natural language processing.

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What are the Most Important Applications of Machine Learning and Deep Learning

Recently, machine learning and deep learning have been dominating the tech world. They are being applied to many different fields, both commercially and academically. In this article, I will explore some of the most important applications of machine learning and deep learning.
Machine Learning is a branch of Artificial Intelligence. It has two main concepts: supervised and unsupervised learning. Unsupervised Machine Learning uses unlabeled data to find patterns in data without being told what these patterns are in advance. Supervised Machine Learning uses labelled data to learn about patterns and then generalizes these patterns for future use. Deep Learning is a subfield of Machine Learning that focuses on the application of neural networks with multiple layers that allow it to work with combinations of raw data types (continuous and discrete). Deep Learning is often used to process large amounts of raw sensor data as well as images, text, and voice recordings which have been pre-labelled with metadata such as pixel values or textual annotations in order to generate

How to Implement Machine Learning in Your Business?

Machine learning is an artificial intelligence technique that gives computers the ability to learn from data without being explicitly programmed. It is a subset of artificial intelligence.
Machine learning can be applied to many different areas, such as pattern recognition and classification, predictive modelling, and data mining.
With machine learning, you can make predictions about the future based on past events. You can also use it to identify patterns in your data that you may not have known existed before.
In this article, we will discuss how to implement machine learning in your business.

Top 5 Recent Research on Machine Learning and Deep Learning

1. Dropout: a simple way to prevent neural networks from overfitting,

It is well-known that the more training data a neural network gets, the better it will generalize to new data. But it turns out that there’s a limit: when the training data becomes too large, the neural network can “overfit” and stop generalizing from old data to new. This problem is called the overfitting or underfitting of a model. To address this problem, we have recently proposed an algorithm called dropout for regularizing deep networks (Srivastava et al., 2014). The idea behind dropout is very simple: randomly drop out hidden units with probability p during training time and keep them in test time. So if we use dropout with a probability of 0.5, then every time our network trains on 80% of its weights, 20% of its weights will be held back and used in future tests instead (Lee & Sheppard, 2015).

2. Deep Residual Learning for Image Recognition,

Deep learning is a popular field in Artificial Intelligence research, but it is still not good enough to perform complicated tasks. Deep residual learning provides a new direction in AI research which can solve many problems that deep learning cannot yet overcome.
Compared to traditional supervised learning, deep residual network structure has two important features: Firstly, the high-level features are fully connected with the low-level features, which makes it easier to learn and handle complex data structures such as text, images, and video. Secondly, it stores and updates past information on its own instead of relying on supervised training. This enables the model to learn from unsupervised data and find higher-level abstractions from unlabeled data sets.

3. Large-scale Video Classification with Convolutional Neural Networks

Small-scale video classification algorithms are limited to working with a limited number of classes (e.g., 15 classes), which can be sufficient for small-scale datasets but not for larger ones. Convolutional neural networks (CNNs) provide an effective means of coping with this limitation and can be applied to large datasets. CNN’s are biologically inspired and have been the most successful class of models for explaining the functions of the brain.
Video classification algorithms are limited by the number of different classes they can handle. In small-scale video classification, there may only be 15 different categories that a video could fall into, but in large-scale video classification, there will be many more than that. CNN’s are capable of working better on larger data sets as opposed to smaller scales because they were inspired by how the brain works and how it’s also been successful at understanding how it works.

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4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,

Batch normalization is a technique that accelerates deep network training by reducing internal covariate shifts. It also helps in reducing computational cost, i.e., the computational cost of updating each layer instead of doing it for each batch.
The paper states that this technique has lots of advantages over the current techniques used for fast and efficient deep learning.

5. Microsoft COCO: Common Objects in Context

Microsoft COCO is a general-purpose computer vision system that teaches machines to see. It uses deep neural networks to learn the shapes and textures of everyday objects and scenes, with a focus on those that are most common.

The intention of this system is to make it easier for other AI agents (like a photo or video captioning systems) to interpret the contents of an image, by using the existing knowledge they have about common objects and scenes. This would help them interpret details in an image they might otherwise not understand