Deep learning is a Subclass of Machine learning and a superclass of Artificial Intelligence(AI) and how Machine Learning (ML) is a subclass of Artificial Intelligence(AI). Deep learning Also called as Deep analytical Learning or Self-Taught Learning and Unsupervised Feature Learning.
Deep Learning Models are Build on artificial neural networks, serve as a human brain. especially Convolutional Neural Networks (CNN). This network allows machines to determine the data just like humans can do.
Deep learning replicates or acts like the workings of the human brain in transforming the data and generating the models to be used in the higher cognitive process. Deep Learning Machines are capable of cognitive tasks without any help of a human.
Deep learning neural networks are capable of learning, the unsupervised huge amount of Unstructured data call big data. Deep Learning Models Will Helpful to simplify data processing in Big Data. Deep learning designs are constructed with the greedy algorithm (layer-by-layer) Model. or (Deep learning design constructions are based on a greedy algorithm (layer-by-layer) Model).
Deep learning algorithms may be enforced or used to unsupervised learning tasks. This is a crucial benefit because undescribed data is larger than the described data.
In Deep Learning, every learn should be converted its input data into a marginally more intellectual and complex representation.
In Deep Learning the word, Deep indicates to the multiple layers during which the data is modified. Deep Learning uses different layers to improved and excerpt or Removed from Higher rank features from the fresh data input.
For example, in Digital Image Processing, low-level Layers can be recognized boundaries, At the same time, high-level layers can recognize the images or views significant to the Human being like any objects or letters or digits and faces recognition etc.
Table of Contents
a) Deep Learning (DL) Applications:
The number of industries uses Deep Learning Applications for various tasks.
1. Banking and Online Transactions:
It can be used to detect fraud or money laundering.in digital transaction systems and find exact address of the fraud include time area, IP Address, retailer Tye etc.
2. Transportation (Driverless Cars):
3. Electronics and Digital Media Platforms:
Image and Video Recognization:
Commercial applications use image recognition for identification purposes like facebook and other apps etc.
Any human interferes the driverless cars can work Automatically like Stop and Run Based on image/video recognization.
4. Aerospace and Defense:
Deep learning is used to find out objects in the space from the satellites can find out the secure and unsecured areas and what type of object u want like image/video/voice or sound etc.
5. Medical Research Tools (For Reusing Drugs):
With the help of Deep learning Cancer, researchers can be exactly detected and find out cancer cells Automatically.
6. High Machinery Industries:
Deep learning Can find out the Machine falts and loops holes and find out the objects which are the unsafe distance to machines, protecting human beings and gives alerts to humans to shift through safety areas.
b) Deep Learning Neural Networks and its Architectures
Different types of Deep learning Neural Networks and its architectures are
1) Deep Neural Networks
2) Unsupervised Pretrained Networks (UPNs)
- Autoencoders (Variational autoencoders (VAEs) )
- Deep Belief Networks (DBNs)
- Generative Adversarial Networks (GANs)
- Variational Autoencoder Generative Adversarial Networks (VAE-GANs)
3) Convolutional Neural Networks (CNNs)
- ZF Net20
- GoogLeNet21 (or Inception Network)
- VGGNet22 (Visual Geometry Group)
- ResNet23 (Residual Networks )
- RCNN (Region Based CNN)
- ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
- Capsule Neural Networks (CapsNet)
4) Recursive Neural Networks
5) Artificial Neural Networks (ANNs) and its Types
- Feedforward neural network
- Group Method of Data Handling (GMDH)
- probabilistic neural network (PNN)
- time delay neural network (TDNN)
- deep stacking network (DSN) (deep convex network)
- Regulatory feedback networks
c. Radial basis function (RBF)
- Radial basis function network (RBF)
d. General regression neural network
e. Recurrent Neural Networks
- LSTM28 (LSTM Networks)
- Backpropagation through time (BPTT)
f. Fully recurrent
- Hopfield Network
- Simple Recurrent Networks
- Reservoir computing
- Echo State Network (ESN)
- Long Short-Term Memory (LSTM)
- Bi-directional RNN, or BRNN
- Stochastic Neural Network
- Modular Neural Network
• committee of machines (CoM)
• associative neural network (ASNN)
- Physical Neural Network
h. Deep predictive coding networks
i. Multilayer kernel machines (MKM)
j. Instantaneously trained neural networks (ITNN)
k. Spiking neural networks (SNN)
l. Dynamic neural networks
- neuro-fuzzy network
- Compositional pattern-producing networks (CPPNs)
m. Memory networks
- One-shot associative memory
- Hierarchical temporal memory (HTM)
- Holographic Associative Memory (HAM)
- long short-term memory (LSTM)
- Neural Turing machines
- Semantic hashing
- Pointer networks
- Encoder–decoder Networks
Some other most important Deep Learning Architectures
- YOLO (You Only Look Once)
- SegNet (segmentation networks)
- Tensor deep stacking networks (TDSNs)
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