Deep Learning Introduction

main-moderator Oct 15, 2019
  • Information Technology

Share with:


Functioning of Deep Learning 

Deep learning models are trained by using large sets of labeled data and neural network architectures that learn features directly from the data without the need for manual feature extraction.

Most deep learning methods use neural network architectures, which is why deep learning models are often referred to as deep neural networksThe term “deep” usually refers to the number of hidden layers in the neural network. Traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150.
One of the most popular types of deep neural networks is known as convolutional neural networks (CNN or ConvNet). A CNN convolves learned features with input data, and uses 2D convolutional layers, making this architecture well suited to processing 2D data, such as images.
CNNs eliminate the need for manual feature extraction, so you do not need to identify features used to classify images. The CNN works by extracting features directly from images. The relevant features are not pre-trained; they are learned while the network trains on a collection of Images. This automated feature extraction makes deep learning models highly accurate for computer vision tasks such as object classification.
CNNs learn to detect different features of an image using tens or hundreds of hidden layers. Every hidden layer increases the complexity of the learned image features. For example, the first hidden layer could learn how to detect edges, and the last learns how to detect more complex shapes specifically catered to the shape of the object we are trying to recognize.

Creating and Training Deep Learning Models

The three most common ways people use deep learning to perform object classification are:

Training from Scratch

To train a deep network from scratch, you gather a very large labeled data set and design a network architecture that will learn the features and model. This is good for new applications, or applications that will have a large number of output categories. This is a less common approach because with the large amount of data and rate of learning, these networks typically take days or weeks to train.

Transfer Learning

Most deep learning applications use the transfer learning approach, a process that involves fine-tuning a pretrained model. You start with an existing network, such as AlexNet or GoogLeNet, and feed in new data containing previously unknown classes. After making some tweaks to the network, you can now perform a new task, such as categorizing only dogs or cats instead of 1000 different objects. This also has the advantage of needing much less data (processing thousands of images, rather than millions), so computation time drops to minutes or hours. Transfer learning requires an interface to the internals of the pre-existing network, so it can be surgically modified and enhanced for the new task.
Feature Extraction
A slightly less common, more specialized approach to deep learning is to use the network as a feature extractor. Since all the layers are tasked with learning certain features from images, we can pull these features out of the network at any time during the training process. These features can then be used as input to a machine learning model such as support vector machines (SVM).
Accelerating Deep Learning Models with GPUs
Training a deep learning model can take a long time, from days to weeks. Using GPU acceleration can speed up the process significantly. GPU reduces the time required to train a network and can cut the training time for an image classification problem from days down to hours. In training deep learning models, GPUs (when available) without requiring you to understand how to program GPUs explicitly.

Future Trends for Deep Learning

According to a leading source, the deep learning market is expected to exceed $18 billion by 2024, growing at a CAGR of 42%. Deep learning algorithms have a huge potential and take messy data like video, images, audio recordings, and text to make business-friendly predictions. Deep learning systems form a strong foundation of modern online services, used by giants like Amazon to understand what the users say understanding speech and the language they use through Alexa virtual assistant or by Google to translate text when the users visit a foreign-language website.

2019 and years to come will be dominated by deep learning trends that will create a disrupting impact in the technology and business world, here are the Top 5 Deep Learning Trends that will dominate 2019.

1. Training Datasets Bias will Influence AI

Human bias is a significant challenge for a majority of decision-making models. The difference and variability of artificial intelligence algorithms are based on the inputs they are fed. Data scientists have come to a conclusion that even machine learning solutions have their own biases that may compromise on the integrity of their data and outputs. Artificial intelligence biases can go undetected for a number of reasons, prominently being training data biases. Bias in training datasets impacts real-world applications that have come up from the biases in machine learning datasets including poorly targeted web-based marketing campaigns, racially discriminatory facial recognition algorithms and gender recruiting biases on employment websites.

2. AI will Rise Amongst Business and Society

Gone are the times when AI was the toast of sci-fi movies, but technology has finally caught up with imagination and adaptability. In the present times, AI has become a reality and amazingly, business and society encounter some form of artificial intelligence in their everyday operations.

Deep learning has dramatically improved the way we live and interact with technology. Amazon’s deep learning offering Alexa is powered to carry out a number of functions via voice interactions, like playing music, making online purchases and answering factual questions. Amazon’s latest offering, AmazonGo that works on AI allows shoppers to walk out of a shop with their shopping bags and automatically get charged with a purchase invoice sent directly to their phone.

3. AI Reality, the Hype will Outrun Reality

Deep learning powered Robots that serve dinner, self-driving cars and drone-taxis could be fun and hugely profitable but exists in far off future than the hype suggests. The overhype surrounding AI and deep learning will propel venture capitalists to redirect their capital elsewhere to the next big thing like 4d printing or quantum computing. Entry bars for deep learning project investments will be higher and at that point, the AI bubble will plunge. To avoid that, technology needs to help users to recognize that AI, machine learning, and deep learning are much more than just buzzwords and have the power to make our every day much easier. Reality says the time is ripe to spend fewer efforts on the exploration of deep learning possibilities and instead focus on delivering solutions to actual, real-life problems.

4. Solving The ‘Black Box’ Problem with Audit Trails

AI and its adaptability come with one of the biggest barriers to its deployment particularly in regulated industries, is the explanation as to how AI reached a decision and gave its predictions. 2019 will mark a new era in creating AI audit trails explaining the nitty-gritties of how AI and deep learning reach a conclusion.

In the future times to come, AI will be explored and deployed for groundbreaking applications like drug discovery which can have a detrimental impact on human life if an incorrect decision is made. Thus, audit trails to AI and deep learning predictions are extremely important.

5. AI Innovations will be Built on Cloud Adoption Capabilities

Come 2019 and beyond and business enterprises will seek to improve their technological infrastructure and cloud hosting processes for supporting their machine learning and AI efforts. As deep learning makes businesses innovate and improve with their machine learning and artificial intelligence offerings, more specialized tooling and infrastructure will be needed to be hosted on the cloud to support customised use cases, like solutions for merging multi-modal sensory inputs for human interaction (like think sound, touch, and vision) or solutions for merging satellite imagery with financial data for enhanced trading capabilities.

Comments (7)

Leave a Reply

Your email address will not be published.

ปั้มไลค์ May 21st, 2020
Like!! Really appreciate you sharing this blog post.Really thank you! Keep writing.
Sharma Mar 2nd, 2020
Really useful information. I learned some new points here.I wish you luck as you continue to follow that passion.
Nikhil Reddy Feb 24th, 2020
Content Mentioned Above Is useful.Thanks For Sharing It
Costin PROTOPOPESCU Jan 21st, 2020
Excellent !!! Very very well made, very informative... Thank you very much. Congratulations ! Best wishes from France
NA Nov 1st, 2019
Valuable information about the Deep learning technology. Keep updating new contents like this post.
Main Moderator Oct 31st, 2019
Dear Reader - Join our community & promote your learning programs within our community.
Nikhil Reddy Oct 31st, 2019
I love your blog, Very informative post that resolved all me queries, keep updating, Thanks

Top Brands

People with similar interest

Witan Search

I am looking for

Witan Search