DEEP LEARNING PHD TOPICS

Accomplishing a Ph.D. in Deep Learning (DL) provides improvement in various specific domains and also gives opportunity to explore more innovative concepts. Even though the deep learning has a large and non-static nature, we carried out all concepts of deep learning. Customised service will be provided for all deep learning PhD projects as per scholars wish. Best thesis ideas will be shared by referring reputed journals. Several research topics of DL are discussed in our below description, they are:

1. Foundations of Deep Learning:

• Interpretation of Deep Representation: We examine about the formation of deep networks’ representation and its robustness.
• Enhancing Training Methods: To maintain and execute neural network training, a new optimization techniques and regularization approaches are constructed by us.
• Deep Learning Generalization: Even in the case of over parameterized condition, the DL method will work effectively, so we need to know about the tactics and enhancement of deep networks generalization.

2. Self-Supervised and Unsupervised Learning:

• Without the requirement of enormous labeled data, we can train the deep models by employing these techniques.

3. Reinforcement Learning:

• Tasks such as series-based decision-making problem, robotics and game playing are accomplished by the integration of DL method with reinforcement learning in our research.

4. Privacy and Robustness in Deep Learning:

• By considering these, we carried out adversarial assaults and defense-based projects.
• We need to check whether our frameworks are robust against several fraudulent attacks or not.

5. Deep Learning based Biomedical Applications:

• Projects related to clinical image analysis, genomic data analysis and drug findings are carried out through employing deep learning by us.

6. Neural Architecture Search (NAS):

• From this, we can automatically discover the optimal neural network framework for a specified work.

7. Understandable and Explainable AI (XAI):

• In our research, we build a black box framework that also provides an understandable decision-making process.
• To interpret and visualize the inner process of complicated framework, we developed an innovative method.

8. Ethical Implications of Deep Learning:

• To construct an efficient and unbiased system, we examined the common considerations in AI projects and approaches and the technique’s biases.

9. Energy Efficient Deep Learning:

• For better executing on devices varying from IoT devices to data centers, we developed a neural network model and hardware co-optimizations.

10. Generative Framework:

• Various advancements are made by us in Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and other generative frameworks.
• We carried out several procedures such as data augmentation, anomaly identification and synthetics data production.

11. Multimodal and Cross modal Learning:

• Frameworks are developed by us that can process and compare the variations with other several approaches like integrating visual and auditory information.

12. Natural Language processing (NLP):

• For the tasks based on language conversion, sentiment analysis and question-answering, we utilized a new framework and training techniques.
• Interpretation of language is accomplished by integrating symbolic reasoning with DL related method in our paper.

13. Transfer and Few-Shot Learning:

• When we have an insufficient data, we create a model that can learn skills from a field or task to improve the efficiency on the specific task that are related to the learned task.

14. Neural Network Pruning and Quantization:

• To minimize the computational cost and dimension of deep networks while maintain its efficiency, we developed this methodology. It is a considerable one, when we aimed to implement it on edge devices.

15. Neuroscience and Deep Learning:

• Relations among biological neural networks and artificial neural networks are investigated by us.
• We need to interpret about how the brain gets motivated to create a new approach and frameworks.

The concept we selected is not only be a new approach but also it is based on our interested domain. We make appropriate selection by thoroughly investigated various present research studies, and concept discussions with specific research professionals or experts. Grab your PhD proposal from us to excel in the field of research.

How do you start deep learning research?

            We state that, beginning of DL based projects comprises of various research related skills, detection of issues in specific domain and experimental analysis. Deep learning projects is carried out by merging it with various other sub fields. Some of the progressive procedures for developing deep learning projects which comes under our framework are listed below:

1. Effective Foundation development:

• Courses: First, we have to begin with some virtual courses like Coursera for example: Andrew Ng’s Deep Learning Specialization, then edX, MIT Open Courseware or Fast.ai
• Books: Deep Learning based books by Goodfellow, Courville and Bengio and some Neural Networks and DL books by Michael Nielsen are utilized by us.
• Tutorials: By utilizing TensorFlow, PyTorch or Keras frameworks, we can make use of manual or direct tutorials.

2. Survey on Literature:

• Academic Journals: Different conference papers such as ICML, ICLR, ACL, CVPR and NeurIPS are often read by us.
• Review Papers: From studying some review papers, we can get to learn various innovative techniques and approaches and description of particular subdomains.
• Newsletters and Blog: Present knowledge related to AI researches is gained by us through the utilization of daily papers or websites such as ArXiv sanity, the Gradient, and Distill.

3. Detect a Query or Issue:

• Discover of Research gap: By the survey on literatures, we identify the issues that are not yet resolved in various papers efficiently and find out the domains where we can offer enhancements.
• Collaborate: Research related queries get cleared for us by keeping in touch with the specific domain experts, professionals and staffs.
• Real World Issues: Various real-world issues are clarified through the association with some labs or businesses.