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Deep learning a subset of machine learning and has been gained a huge significance in scientific computing and the algorithms of deep learning are widely used to solve complicated problems. Deep learning algorithms have different types of neural networks to carry out particular tasks. Deep learning will use an artificial neural network to do computations on large data. This type of machine learning concept would work alike that of the human brain. Various industries that use deep learning are e-commerce, entertainment, healthcare, ads and so on. Deep learning will mirror the brain and compute the information as the human brain does. In the training, the algorithms will various elements in the input distribution to group objects and find out the data patterns that are useful. It uses different algorithms to build deep learning models.
Convolutional neural networks
CNN is also known as ConvNets and is widely used for image processing and object detection. This algorithm is used in satellite images, processing medical images, detecting anomalies and forecasting time series. There are different layers that CNN has to process as well as extract the data. The convolution layer will use filters to perform different operations. The rectified layer will carry out convolution operations on different elements. The pooling layer will reduce the dimensions on the features map. The fully connected layer will classify as well as identify images.
Long short-term memory networks
This type of neural network will learn and recalls the information when needed. It is widely used for time-series prediction since it remembers the previous inputs. This type of algorithm is used for speech recognition, music composition and pharma development.
Recurrent neural network
It forms connections to form a direct cycle to take inputs from Long short-term memory networks and feed them as input to the current phase. This type of algorithm is used for handwriting recognition, machine translation, time-series analysis as well as natural language processing.
Generative adversarial networks
GANs will be used to create data instances which would be the copy of the training data. There are two components that GANs hold. The first is the generator that will help you generate fake information and the discriminator is used to learn things from the false data. It is used to improve astronomical images and create high-resolution images. Using this you can also create cartoon characters and photos with human faces and render 3D objects.
Radial basis function networks
It is a type of feedforward neural network which makes use of radial basic functions which are the activation functions. It has an input layer, hidden layer and output layer that is used for classification, regression and for time-series prediction.
Multi-layer perceptrons
This belongs to a feedforward neural network with multiple layers of perceptrons. It has an input layer and an output layer that are connected fully. They equally have input and output layers but have many hidden layers which are used for image recognition, machine translation and speech recognition.
Self-organizing maps
This type of algorithm is used for data visualization to reduce data dimensions with the help of self-organizing neural networks. Data visualization helps you to solve problems which are challenging for humans to visualize.
Deep belief networks
These are generative models that will have different layers of latent as well as stochastic variables. This is a stack of different Boltzmann machines that are connected to different layers and is widely used for video recognition, image recognition and monitor-capture data.
Following are the areas where deep learning is used to solve complicated problems:
Natural Language processing
It allows the systems to learn and generate human speech. You can use this for text-to-voice, voice-to-text, machine learning, tagging, named entity recognition and sentimental analysis. It is used in apps like chatbots, spam detection, digital assistants and so on. It also pushes the natural language processing engines to get better by finding out the patterns in human speech.
Image processing
It is used in different applications like biometrics, recognizing a face, analysing medical scans, finding out faulty parts, sharpening images and autonomous vehicles.
Fraud prevention
The deep learning techniques would be used to find out patterns in sales as well as financial data. Financial institutions and transportation would use deep learning to find out fraudulent activities.
Some of the popular topics in Deep Learning on which our programming assignment experts work on a daily basis are listed below:
| Caffe |
Transformer Networks |
| Torch |
Neural Machine Translation |
| OverFeat |
Speech Recognition |
| Cuda |
Object Detection |
| CNNs / convnet |
Image Segmentation |
| Deeplearning4j |
Language Modeling |
| OpenCL |
Machine Learning Explainability |
| DeepCL |
Neural Style Transfer |
| Pytorch |
Adversarial Attack and Defense |
| Convolutional Neural Networks (CNNs) |
Face Recognition |
| Recurrent Neural Networks (RNNs) |
Image Generation |
| Generative Adversarial Networks (GANs) |
Time Series Forecasting |
| Long Short-Term Memory (LSTM) |
Natural Language Processing (NLP) |
| Deep Q-Networks |
Federated Learning |
| YOLO (You Only Look Once) | Meta-learning |
| U-Net |
Self-Supervised Learning |
| Batch Normalization |
Contrastive Learning |
| Dropout |
Few-shot Learning |
| Residual Networks (ResNets) |
Evolutionary Deep Learning |
| Attention Mechanisms |
Deep Learning on Edge Devices |
|
Capsule Networks |
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