Deep Learning Models : An Overview
First Sunidhi Shrivastava1 , Manali Shukla2 and kirti ahrivastava3
ITM University, Gwalior
Sunidhishrivastava.cse@itmuniversity.ac.in,
manali.shukla.cse@itmuniversity.ac,
kirti.shrivastava.mca@itmuniversity.ac.in
Abstract. Robotic Technics encompasses advanced
methodologies and technologies in robotics, driving innovation
across industries. This article explores the evolution, current
applications, challenges, and future prospects of robotic
technics.
Keywords: Deep Learning, Neural Networks, Machine
Learning, Artificial Intelligence, Convolutional Neural
Networks
1 Introduction
Deep Learning refers to a subset of machine learning techniques
inspired by the structure and function of the human brain's neural
networks. Unlike traditional machine learning algorithms, deep
learning models can automatically discover and learn intricate patterns
and representations from raw data, making them exceptionally
powerful for tasks such as image and speech recognition, natural
language processing, and autonomous driving.
This article aims to explore the evolution of deep learning models, their
foundational principles, significant milestones, current methodologies,
and their transformative impact across various industries.
2. Background Study
Understanding deep learning models requires exploring their
historical development and fundamental concepts. The roots of deep
learning can be traced back to the 1940s with the introduction of
artificial neural networks (ANNs), which were initially inspired by
biological neurons and aimed to simulate human cognitive processes.
The resurgence of interest in deep learning occurred in the 2000s,
fueled by advancements in computational power, the availability of
large datasets, and breakthroughs in algorithms such as
backpropagation and stochastic gradient descent. Key milestones
include the development of convolutional neural networks (CNNs) for
image recognition tasks and recurrent neural networks (RNNs) for
sequential data analysis.
.
3. Existing Methods
Deep learning models encompass a diverse array of architectures and
techniques tailored to specific tasks:
Convolutional Neural Networks (CNNs): Primarily used for
image and video recognition tasks due to their ability to
capture spatial hierarchies of features.
Recurrent Neural Networks (RNNs): Effective for sequential
data processing, such as natural language processing and time
series prediction.
Generative Adversarial Networks (GANs): Employed for
generating new data samples, such as images or text, by
learning from existing datasets.
Transformer Models: Introduced attention mechanisms that
have revolutionized natural language processing tasks,
enabling models like BERT and GPT to achieve state-of-the-
art results in various language understanding tasks.
Deep Reinforcement Learning: Integrates deep learning with
reinforcement learning principles to enable agents to learn
optimal behavior through trial and error.
These methods have propelled advancements in diverse fields,
including healthcare diagnostics, autonomous vehicles, finance, and
entertainment.
4. Conclusions
deep learning models represent a paradigm shift in artificial
intelligence, unlocking unprecedented capabilities in data analysis,
pattern recognition, and decision-making. As deep learning continues
to evolve, addressing challenges such as model interpretability,
scalability, and ethical considerations will be critical.
Future directions for deep learning include enhancing model
robustness, integrating multi-modal data sources, advancing self-
supervised learning techniques, and exploring novel architectures to
tackle complex real-world problems.
References/Bibliography
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep
Learning. MIT Press.
Schmidhuber, J. (2015). Deep learning in neural networks: An
overview. Neural Networks, 61, 85-117.
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R.,
Jaitly, N., ... & Kingsbury, B. (2012). Deep neural networks
for acoustic modeling in speech recognition: The shared views
of four research groups. IEEE Signal Processing Magazine,
29(6), 82-97.
