The term “deep learning” is achieved from the field of machine learning which relies on algorithms for processing a large volume of data. It is stimulated by an artificial neural network (ANN) to perform the functions of the human brain. In specific, the conjunction point of neural network and machine learning is deep learning. This page is prepared with an intention to provide end-to-end research information which is highly required for Deep Learning Based Projects!!!
Deep learning has the main goal to mimic the behavior of the human brain in different aspects as creative thinking, decision making, learning, etc. To imitate the real behavior of the human brain, it has multiple layers of neural networks same as brain neurons. These layers help to extract and process the raw data to achieve higher-level characteristics. Further, it guides you to take effective decisions based on dynamic learning and analyzing. Here, we have given some main operations involved in deep learning.
What are the primary functions of deep learning?
- It trains input data through applied techniques for enhancing the learning experience
- It is a significant one among others where you can recognize in face recognition systems, self-driving car, virtual assistants, etc.
- It is preferred by many experts due to increase learning capabilities and training performance through in-depth data
- It finds new data by neural networks through deep learning at each time to improve performance
Role of Deep Learning
As mentioned earlier, deep learning is part of machine learning which is intended to achieve complete flexibility based on realizing tree-shaped concepts. And, the neural network assists you to find the relations among data for better interpretation. Further, the important data are arranged in hierarchical order (i.e., multi-stages). Then, the algorithms used for processing and analyzing are self-directed for the automatic process in implementing deep learning based projects.
Some of the real-time examples are face recognition, virtual assistance, pharmaceutical, driverless vehicles, translations, etc. In the data analyzing process, the data points are basically quantified in millions. So, deep learning takes more time for training data on account of larger parameters. Further, it also uses neural networks for many purposes such as classification, optimization, etc.
Working of Deep Learning
To train output data obtained from processed input data, deep learning algorithms use both unsupervised and supervised algorithms. In this, the neural network uses a layered approach for the processing which comprises the input layer, hidden layer, and output layer. Here, we have given you the workflow of deep learning through multiple layers (input, hidden, and output).
- Input layer – It collects input data and transfer it to first hidden layer
- Hidden layer – It processes the collected data from the input layer. The main issue is a decision over a number of hidden layers and neurons
- Output layer – It produces the output of processed data
In the computation of data, each connection among neurons holds weight to determine the input data’s importance. Further, it uses activation and optimization functions to standardize the output. Generally, there are two important aspects of training data in deep learning and they are processing power and dataset creation. And, the term “deep” signifies the count of hidden layers used in the model for training. In short and crisp, here we have given the functionalities of deep learning based projects (i.e., common implementation procedure).
Working of Deep learning can be summed up in four final points:
- At first, neural network request combo of binary True or False questions
- Then, extract values of data by numerical range
- Next, categorize data based on acknowledged answers
- At last, label the unlabelled data based on the classification
Our developers are proficient enough to handle current enhanced deep learning technologies. Actually, there are different deep learning algorithms which have individual characteristics. In fact, our experts know the purpose of each algorithm to appropriately choose the research techniques. For your information, here we have given only a few significant algorithms. Similarly, we also support you in other developing algorithms. In the case of requirements, we use hybrid techniques to solve complex problems of research.
Deep Learning Algorithms List
- Back Propagation
- Stochastic Gradient Descent
- DBN – Deep Belief Network
- RNN – Recurrent Neural Network
- DNN – Deep Neural Network
- ANN – Artificial Neural Network
- KNN – K-Nearest Neighbor
- CNN – Convolutional Neural Network
Summary
Let’s have a quick short overview of deep learning (DL). It is the combination of machine learning (ML) and artificial neural networks (ANN) to follow human brain activities. So, it is flexible to decide an automated way. It supports both structured and unstructured data formats for processing/training. In recent days, exploration of ANN is found in every research field due to its incredible benefits.
Specifically, cybersecurity gains great attraction towards ANN. Eventually, deep learning also grows along with ANN where DL is created from ANN. Overall, this neural network makes machines think on their own like the human brain. Some of the real-world examples deep learning based projects are virtual assistants, biometric recognition, self-driving vehicles, automated industrial machines, etc. Below, we have stated the list of commonly used deep learning approaches which are well-suited for current real-time applications.
Important Deep Learning Algorithms
- AE – Auto-Encoder
- SOM – Self-Organizing Map
- MLP – Multi-layer Perceptron
- DBN – Deep Belief Networks
- RNN – Recurrent Neural Network
- Deep TL- Deep Transfer Learning
- CNN – Convolutional Neural Network
- GAN – Generative Adversarial Network
- LSTM – Long Short-Term Memory
- Deep RL – Deep Reinforcement Learning
- RBM – Restricted Boltzmann Machine
In addition, we have given you some current techniques that are largely recommended for Deep Learning Based Projects. Here, we have given you the type of technique along with the key function and purposes. When we suggest the research solutions for your research issues/challenges, we perform a deep survey on widely used techniques in related topics. Then, review the advantages and disadvantages of short-listed techniques. Once we get a clear picture of the handpicked techniques, we suggest the best among them for your project. If no one is best-fitting for your project, then we create new algorithms/techniques to tackle your research challenges.
Latest Techniques used in Deep Learning based Projects
- SOM – Self-Organization Map
- Unsupervised learning type
- Used for competitive learning instead of backpropagation
- Mapping and clustering of the high-dimensional dataset by dimensionality reduction approach
- DRL – Deep Reinforcement Learning
- Track on human experience-based learning abilities
- Used for solving complicated problems
- Combo of deep learning and Q learning
- ConvNet – Convolution Neural Network
- Improved version of MLP
- Used for 2D shapes variability
- Automated identification of data characteristics
- RBM – Restricted Boltzmann Machine
- Unsupervised learning type which works on stochastic and probabilistic oriented statistic distribution
- Develop building blocks of DBN
- Used for feature extraction and selection
- LSTM-RNN – Long Short-Term Memory Recurrent Neural Network
- Speech processing, Time-Series Prediction, and NLP Operation
- Used for analyzing and learning the sequential information
- MLP – Multi-Layer Perception
- Supervised learning type
- Processing of complicated issues is more costly
- Feed-forwarded and completely associated with ANN
- DTL – Deep Transfer Learning
- Crack fundamental training issues
- Used for large-scale training data to guarantee accuracy and less-time
- Utilize pre-trained models where information is passed over from one to other
- AE – Auto-encoder
- Unsupervised learning type
- Used for deterministic input representation and dimensionality reduction
- Minimize noise in input data
- DBN – Deep Belief Network
- Probabilistic generative models
- Used for huge-scale high-dimensional data
- Able to encode network topology to support both unsupervised and supervised learning
- GAN – Generative Adversarial Network
- Unsupervised learning type
- Used for producing new data instance by the generic model which relates to input data
- The fault-tolerant deep learning model
Next, we can see about open research issues of deep learning. Even though deep learning is more popular in smart world transformation, it has technical hurdles in real-time development. This makes deep learning more difficult to attain perfect system performance. So, the research scholars are waiting to provide the best solutions for these kinds of research issues in their exploration.
To provide you latest issues, we undergo deep study on deep learning-related articles, newspapers, magazines, research papers, etc. After reviewing all this information, we have collected a massive volume of significant research issues looking for the best solutions. From that, here we have listed only open issues for your references.
Open Issues in Deep Learning
- When the size of the dataset increases then the processing cost for training is also increases
- When the entire process depends on continuous data flow, then may reduce the enhancement of the training process
- When the training dataset is processing, then there is a requirement of robust GPU and ultra-speed CPU
- When the transparency of fault is lacking, then there is no way to provide a solution to that fault
Furthermore, our developers have given you primary programming languages used to develop Deep Learning Based Projects. From our experience, we found that the following tools are great in yielding expected results. And, our developers are skilled enough to work with not only on these languages but also on other important languages.
So, we are adept to develop any sort of research solutions such as techniques and algorithms in an effective manner. And also, we are good at developing and delivering projects on time with satisfactory results. Similarly, we also help you to choose suitable development tools, technologies, platforms, and datasets.
Best Deep Learning Programming Language
- Javascript
- It is a client-side scripting language
- It provides a robust way to validate data by easy programming
- It seeks the attention of many industries
- It is used for dynamic and responsive web applications
- Python
- It is simple to learn and create code
- It is a fast-evolving high-level programming language
- It is used for machine learning, internet-based applications, etc.
- Java
- It is an evergreen and famous high-level programming languages
- It includes infinite online and offline resources to support beginners
- It is simple to troubleshoot and debug errors
- It is used for all web and mobile related applications
- Ruby
- It is a user-friendly language to learn and code
- It is used for the smooth execution of web-based apps without developers
Last but not least, now we can see about few significant research ideas for deep learning projects in the cybersecurity area. Since we already mentioned that cybersecurity is one of the most important research areas on using deep learning concepts. Like the same, we also support you in other emerging research areas of deep learning. To give you recent updates about deep learning research, we regularly upgrade our knowledge on current scientific developments in the deep learning research field. So, our research ideas are always unique and up-to-date to support you in all aspects.
Cybersecurity Tasks
- Detection of Phishing and Impersonation
- Detection and Prediction of Cyber-Attacks / Threats
- Detection and Prevention of Intruders Activities
- Detection of Cyber-Anomalies and Fraudulent Events
- Detection and Analysis of Malware or Network Botnets
- And many more
Further, if you are interested to know other core research areas of deep learning then approach our team. Our technical legends help you to know the current trends and future technologies in your desired research areas. Overall, we give keen assistance in all the stages of your deep learning based projects research work.
