Deep
Learning
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Introduction
Motivation
History of Deep Learning
Data Framework
History of Data Frame Works
Era of Deep Learning
Applications
Tools
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Introduction
 Deep learning is an artificial
intelligence (AI) technology
that trains computers to
analyze data in a manner
similar to that of the human
brain.
 Deep learning models are
capable of recognizing
complex patterns in images,
text, sounds, and other data in
order to generate accurate
insights and predictions.
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Motivation
Diagnosis Segmentation Exploring
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Diagnosis
Deep learning algorithms are
particularly well-suited for medical
diagnosis jobs because they can learn to
extract significant features from
enormous datasets without explicit
programming. Deep learning systems,
for example, can learn to identify
abnormalities in X-rays or MRIs by
evaluating hundreds of images and
identifying patterns that suggest
sickness.
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Segmentation
§ Deep learning-based segmentation
approaches have gained traction in
recent years, particularly in medical
imaging applications. Typically,
these methods require training a
neural network to recognize and
segment specific structures or
features in an image.
§ Segmentation is the process of
identifying and isolating various
items or structures within an image
or video.
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Exploring
• Deep learning exploration is the process
of evaluating and comprehending the
behavior and performance of deep
learning models. This process entails
assessing the model's strengths,
shortcomings, and limits, as well as
identifying areas for development.
• Visualization techniques, performance
indicators, and explainability tools are
among the tools and strategies available
for analyzing deep learning models.
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History of Deep Learning
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Deep learning data frames are a sort of data structure that is
extensively used in machine learning and data analysis. Adata frame is
a two-dimensional table-like structure with rows and columns, each of
which represents an observation and each column a variable.
Data frames are commonly used in deep learning to hold training data,
which comprises of input characteristics and output labels. The data
required to train the model is represented by the input features, while
the output labels reflect the desired output for each input.
Data frames can be built from a variety of data sources, including CSV
files, databases, and web APIs. Data frames can be preprocessed and
changed after they are created, employing techniques such as
normalization, feature scaling, and one-hot encoding, among others.
Data Frame
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History of Data
Frameworks
Deep learning frameworks like Kera's, MX Net, and
CNTK have proliferated in recent years. Each framework
has advantages and disadvantages, and the framework
chosen typically relies on the project's specific
requirements.
Caffe, the first deep learning framework, was created in
2014 by the Berkeley Vision and Learning Center.
Google launched TensorFlow as an open-source deep
learning framework in 2015.
Facebook published PyTorch as an open-source deep-
learning platform in 2016.
The Montreal Institute for Learning Algorithms also
released Theano in 2015. (MILA).
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Why this is the era of Deep Learning?
Deep learning has gained popularity in recent years for numerous reasons:
Big Data
Better Hardware
Improved Algorithms
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Applications
Natural Language Processing
Computer Vision
Health Care
Finance
Gaming
Robotics
Marketing
Agriculture
Energy
Social Media
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Tools
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Popular Tool
TensorFlow is the most widely used deep learning
technology. TensorFlow is a Google open-source
software library for constructing and training neural
networks. It supports a wide range of platforms,
including CPUs, GPUs, and TPUs, and provides a
high-level interface for constructing and deploying
deep learning models (Tensor Processing Units).
TensorFlow is widely used in academia and
industry, and it has a large user and developer
community. It includes data preparation, data
augmentation, visualization, and model tweaking
tools and features for developing and training deep
learning models.
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