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  • Writer's pictureFusionpact

Deep Learning: An educational blog around Deep Learning.

The world of AI is not easy to understand. There are dozens of terms that are thrown

around and it can be hard to know what something means. I'm going to try to help

people who may not have a strong AI background understand what these terms

mean and how they fit together. I am going to cover topics like:

Artificial Neural Networks, Deep Learning, Convolutional Neural Networks, Recurrent

Neural Networks, Natural Language Processing, and, hopefully, Singularity!

This is going to be a big project and you are going to see every wild theory created by

people on the internet which may have the potential to not make sense at all.

Deep learning is a type of machine learning and artificial intelligence (AI) that

mimics how humans acquire specific types of knowledge. Deep learning is a critical

component of data science, which also includes statistics and predictive modeling.

Deep learning is extremely beneficial to data scientists who are tasked with

collecting, analyzing, and interpreting large amounts of data; deep learning speeds

up and simplifies this process.

Consider a toddler whose first word is "water." By pointing to objects and saying the

word "water," the toddler learns what water is and is not. "Yes, that is water," or "No,

that is not water," the parent says. As the toddler continues to point to objects, he

becomes more aware of the characteristics that all dogs share. Without realizing it,

the toddler clarifies a complex abstraction of the concept of water by constructing a

hierarchy in which each level of abstraction is created with knowledge gained from

the previous layer of the hierarchy.

Working on Deep Learning

Computer programs that use deep learning go through much the same process as

the toddler learning to identify water. Each algorithm in the hierarchy applies a

nonlinear transformation to its input and uses what it learns to create a statistical

model as output. Iterations continue until the output has reached an acceptable

level of accuracy. The number of processing layers through which data must pass is

what inspired the label deep.

The learning process in typical machine learning is supervised, and the programmer

must be exceedingly detailed when instructing the computer on what types of

things it should look for to determine whether an image contains or does not

contain a dog. This is a time-consuming procedure known as feature extraction, and

the computer's success rate is totally dependent on the programmer's ability to

precisely define a feature set for. The benefit of deep learning is that the software

builds the feature set without supervision. Unsupervised learning is not only faster,

but it is also more accurate in most cases.

Deep Learning V Machine Learning

Deep learning is a subset of machine learning that distinguishes itself by

problem-solving. To discover the most often used features in machine learning, a

domain expert is required. Deep learning, on the other hand, learns features

incrementally, reducing the requirement for domain expertise. Deep learning

algorithms, as a result, take significantly longer to train than machine learning

algorithms, which just require a few seconds to a few hours. During testing, however,

the opposite is true. Deep learning methods conduct tests significantly faster than

machine learning algorithms, whose test time increases as the size of the data


Furthermore, machine learning does not necessitate the same expensive, high-end

equipment and high-performance GPUs as deep learning.

Finally, many data scientists prefer traditional machine learning to deep learning

because of its superior interpretability, or ability to make sense of the solutions.

When the data is small, machine learning algorithms are recommended.

Deep learning is preferable in scenarios with a huge amount of data, a lack of

domain awareness for feature introspection, or complicated issues like speech

recognition and NLP.

Application of Deep Learning

1) Customer satisfaction (CX). Chatbots are already using deep learning

algorithms. Deep learning is predicted to be utilized in numerous businesses

to improve CX and raise customer happiness as it matures.

2) Text creation. Machines are taught the grammar and style of a piece of writing

and then use this model to automatically write an entirely new text that

matches the original text's proper spelling, grammar, and style.

3) Military and aerospace. Deep learning is being used to detect items from

satellites, identifying regions of interest as well as safe and dangerous zones

for troops.

4) Automation in industry. Deep learning is increasing worker safety in contexts

such as factories and warehouses by delivering services that recognize when a

human or object is approaching a machine too closely.

5) Computer vision: Deep learning has considerably improved computer vision,

allowing computers to do extremely accurate object identification, image

classification, restoration, and segmentation.

Limitations and challenges that deep learning faces

The fact that deep learning models learn through observations is their most

significant constraint. This means they only know what was in the training data. If a

user just has a small amount of data or data from a single source that is not

necessarily representative of the larger functional area, the models will not learn in a

generalizable manner.

Biases are another key concern for deep learning algorithms. If a model is trained on

biased data, the model will reproduce similar biases in its predictions. Deep learning

programmers have struggled with this since models learn to differentiate based on

tiny differences in data items. Frequently, the factors it determines to be relevant are

not directly stated to the programmer. This means that, for example, a facial

recognition model may make assumptions about people's traits based on factors

such as ethnicity or gender without the programmer's knowledge.

The learning rate can also be a significant difficulty for deep learning models. If the

rate is too fast, the model will converge too rapidly, yielding a suboptimal result. If the

rate is too low, the process may become stalled, making it much more difficult to find

a solution.

Deep learning models' hardware needs might also impose constraints. To boost

efficiency and reduce time consumption, multicore high-performance graphics

processing units (GPUs) and other similar processing units are required. These

devices, however, are pricey and consume a lot of energy. Random access memory

and a hard disc drive (HDD) or RAM-based solid-state drive are also required (SSD).

How does deep learning impact the future of tech?

Deep learning development tools, libraries, and languages may become regular

components of every software development tool kit within the next few years. These

current tool sets will pave the way for simple design, configuration, and training of

new models. Style transformation, auto-tagging, music composition, and other tasks

would be much easier to complete with these skills.

The demand for speedier coding has never been greater. Deep learning developers

will increasingly use integrated, open, cloud-based development environments that

allow access to a wide range of off-the-shelf and pluggable algorithm libraries in the


The prediction that neural architecture search will be crucial in constructing data

sets for deep learning models is still valid.

Deep learning should be able to demonstrate learning from limited training

materials and transfer learning between contexts, continuous learning, and adaptive


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