I am talking all about deep learning in which we can talk about introduction ,Benefits , Applications , Advantages Frameworks and what is the beginners(code) of deep learning . In CETPA infotech , they provide deep learning course and training in noida.

1. All About Deep Learning
www.cetpainfotech.co
phone no : 9212172602
Email id : query@cetpainfotech

2. Overview
• Introduction
• Benefits
• Applications
• Advantages
• Frameworks
• Code(Beginners)
• Contacts

3. Introductions
 Deep learning is a branch of machine
learning which is based on artificial
neural networks. It is capable of
learning complex patterns and
relationships within data. In deep
learning, we don’t need to explicitly
program everything. It has become
increasingly popular in recent years due
to the advances in processing power
and the availability of large datasets.
Because it is based on artificial neural
networks (ANNs) also known as deep
neural networks (DNNs). These neural
networks are inspired by the structure
and function of the human brain’s
biological neurons, and they are
designed to learn from large amounts of
data.

4. Benefits
•Better predictions: Which business wouldn’t want to be
able to call just the customers who are ready to buy or keep
just the right amount of stock? All of these decisions can be
improved with better predictions.
•Faster predictions: Deep learning, and machine learning in
general, automates a company’s decision making increasing
its execution speed. Consider customers that leave their
contact info to get more details about a tech solution for their
company. Maybe it is obvious from the contact info that this
is a very high potential and needs to be contacted.
•Cheaper predictions: Companies that do not implement
operational decision making models, rely on analysts to
make decisions which are orders of magnitude costlier than
running deep-learning models. However, deep learning
models also have setup time and costs. Therefore, the
business case for models need to be investigated before
rolling out models.

5. Applications
Some of the main applications of deep learning in computer vision include:
• Object detection and recognition: Deep learning model can be used to identify
and locate objects within images and videos, making it possible for machines to
perform tasks such as self-driving cars, surveillance, and robotics.
• Image classification: Deep learning models can be used to classify images into
categories such as animals, plants, and buildings. This is used in applications such
as medical imaging, quality control, and image retrieval.
 Image segmentation: Deep learning models can be used for image segmentation
into different regions, making it possible to identify specific features within images.
 Some of the main applications of deep learning in NLP include:
• Automatic Text Generation – Deep learning model can learn the corpus of text
and new text like summaries, essays can be automatically generated using these
trained models.
• Language translation: Deep learning models can translate text from one
language to another, making it possible to communicate with people from
different linguistic backgrounds.
• Speech recognition: Deep learning models can recognize and transcribe spoken
words, making it possible to perform tasks such as speech-to-text conversion,
voice search, and voice-controlled devices.

6. Advantages
 Deep learning has several advantages over traditional machine learning methods, some of the main ones include:
1. Automatic feature learning: Deep learning algorithms can automatically learn features from the data, which means that
they don’t require the features to be hand-engineered. This is particularly useful for tasks where the features are difficult
to define, such as image recognition.
2. Handling large and complex data: Deep learning algorithms can handle large and complex datasets that would be
difficult for traditional machine learning algorithms to process. This makes it a useful tool for extracting insights from big
data.
3. Improved performance: Deep learning algorithms have been shown to achieve state-of-the-art performance on a wide
range of problems, including image and speech recognition, natural language processing, and computer vision.
4. Handling non-linear relationships: Deep learning can uncover non-linear relationships in data that would be difficult to
detect through traditional methods.
5. Handling structured and unstructured data: Deep learning algorithms can handle both structured and unstructured
data such as images, text, and audio.
6. Scalability: Deep learning models can be easily scaled to handle an increasing amount of data and can be deployed on
cloud platforms and edge devices.
7. Generalization: Deep learning models can generalize well to new situations or contexts, as they are able to learn abstract
and hierarchical representations of the data.

7. Frameworks

8. Deep learning code(beginners)

9. THANK YOU!!!!
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PHONE NO : 9212172602
Email id : query@cetpainfotech.com
Website : https://www.cetpainfotech.com/