Thesis on Deep Learning

2. Data Gathering and Understanding:

• Data Source: Proper datasets have to be detected in our research and it may be web scrapping, proprietary data or publicly available datasets.
• Exploratory Data Analysis (EDA): Information like type of data, missing values, outliers, distributions must be interpreted by us.
• Data Annotation: We need to check the existence of labels in data. If it is not, processes such as manual annotation and crowdsourcing are carried out to acquire the label.

3. Preprocessing of Data:

• Cleaning: We managed the issues like outliers, missing values and artifacts data.
• Feature Engineering: From the data, only relevant features are obtained by us.
• Normalization and Scaling: We must have to check whether all the data are on same dimensions or not.
• Augmenting data: For efficient generalization, process like data augmentation are carried out by us in the image categorization based tasks.
• Division of Dataset: The data are divided into three phases like training, validation and testing in our project.

4. Model selection and Baseline:

• Select Architecture: Based on our project, we selected a proper neural network framework such as RNN for sequence-based tasks and CNN for image-based tasks.
• Baseline Model: To implement baseline function, we begin with an easy framework or a pre-defined framework.

5. Training:

• Loss Function: We chosen a proper loss function related to our work.
• Optimizer: Optimization techniques such as Adam or SGD are selected by us.
• Learning Rate and Batch Size: In our project, we tuned or utilized some methods including learning rate annealing.
• Regularization: To restrict overfitting issue, approaches such as weight decay or dropout are executed.
• Monitoring: Several metrics like loss, accuracy etc is monitored by us and we visualize by employing techniques such as TensorBoard.

6. Evaluation:

• Metrics: By using validation dataset, we examined efficiency of our framework in terms of important metrics like curacy, MSE and F1-Score.
• Cross-validation: K-fold cross-validation method is utilized in our work to enhance the testing process.
• Error Analysis: We examine the errors in the framework’s flow and understand the reason for that.

7. Hyperparameter Tuning:

• Grid Search or random Search: For carry out our framework’s testing, we employed various hyperparameter integrations.
• Automated approaches: Different libraries or tools such as Hyperopt, Bayesian optimization or Optuna are used by us for more robust tuning.

8. Deployment:

• Model Export: We transform our framework into a proper format like ONNX, TensorFlow Serving that are appropriate for our implementation.
• Combination: With the necessary server or application, the frameworks are combined by us.
• Monitoring in Production: In an actual time, environment, the framework’s efficiency is monitored and we have to be ready to tune or redevelop the framework.

9. Feedback Loop:

• To enhance and reconstruct the framework, we gathered the reviews from the implemented framework’s forecasting.

10. Documentation and Reporting:

• We should document each and every procedural flow, results and the considered metrics.
• An elaborate description related to our project’s results is necessary, if we are intended to do a research study.

            It is very important to note that, the integration, iterative creation and to gain knowledge up-to-date from the evolving approaches are more significant throughout the whole process of research, but for scholars among their hectic schedules it might not be possible but we stay updated so be free and get PhD professionals service for all your deep learning work. Mostly, deep learning needs efficient testing and by considering interpretations and limitations, we can also re-explore previous phases of research.

What are the advanced topics in deep learning?

Some of the advanced topics in deep learning are shared below further if you have any doubts feel free to contact us. From reputable journals such as IEEE, SCI, SCOPUS…we direct you for topic selection. Our subject matter experts will talk about queries if you have during the topic decision process. While scholars own topics are also welcomed.

1. DLOT-Net: A Deep Learning Tool for Outlier Identification
2. Film CTR Prediction Based on Deep Learning
3. Deep Learning for Ventricular Arrhythmia Prediction Using Fibrosis Segmentations on Cardiac MRI Data
4. A Novel Deep Learning-based Framework for Blackhole Attack Detection in Unsecured RPL Networks
5. Deep Learning Based Intrusion Detection System: Modern Approach
6. Application and Comparative Analysis of Traditional Machine Learning and Deep Learning in Transmission Line Fault Classification
7. Applying Deep Learning in Forecasting Stock Index: Evidence from RTS Index
8. A Survey on Mobile Edge Computing for Deep Learning
9. Malware Analysis in Cyber Security based on Deep Learning; Recognition and Classification
10. DeepSen: A Deep Learning-based Framework for Sentiment Analysis from Multi-Domain Heterogeneous Data
11. Deep learning-based user grouping for FD-MIMO systems exploiting statistical channel state information
12. Web-based personal access control system using facial recognition with deep learning techniques
13. A Vision-based Deep Learning Platform for Human Motor Activity Recognition
14. Algorithm to Estimate Scalloping & Banding in Scansar Images and Deep Learning Based Descalloping Technique
15. A New K Best Sphere Decoder in 16 × 16 MIMO System using Deep Learning Algorithm
16. Stocastic Multimodal Fusion Method for Classifying Emotions with Attention Mechanism Using Deep Learning
17. Comprehensive study on image forgery techniques using deep learning
18. Learned image and video compression with deep neural networks
19. IGPRED-MultiTask: A Deep Learning Model to Predict Protein Secondary Structure, Torsion Angles and Solvent Accessibility
20. Performance Analysis of Distributed Deep Learning using Horovod for Image Classification