What are hot topics for a Master thesis related to Deep Learning

Deep Learning (DL) is another way of representing data. It is a family of Machine Learning (ML). Some researchers consider DL as an advance form of ML. The other names of DL are deep structure learning and hierarchical learning. Moreover, It has enough potential to keep us busy for a long while.

If you are looking for some good research area in DL for your master’s or PhD thesis, then please have a quick review of Ian Goodfellow, Yoshua Bengio, and Aaron Courville books available for this task. There is a complete section in it for research.

Google, Amazon, Netflix and many other big companies  on the internet using DL to increase their sale rate. Such as from Google point of view, what are you looking for, what you have queried and the information available on the page is accurate or not are some areas where they optimise their results. Google has recently introduced Rank Brain which works according to it. On the other end, which movie you are watching and recommend a movie according to your taste are some areas where deep learning provides better results than the methodology adopted previously.

DL algorithms have various applications where human experts don’t produce efficient results. Other areas of DL are

  1. Computer Vision
  2. Speech Recognition
  3. Natural Language Processing (NLP)
  4. Social Network Analysis
  5. Recommender System
  6. Customer Relationship Management
  7. Machine Translation
  8. Bioinformatics

Dl and Machine Learning (ML) now hot topics for Master’s student. There are many ideas related to machine learning thesis and DL.

One of the hot topics on DL is Natural Language processing. Because Overfitting and underfitting are hot topics of research. Regularization has tried to overcome such issues, but you can extend this work using deep learning approaches.

Secondly, Extreme Learning Machines (ELM) has a lot of space for researchers. ELM is a feedforward neural network which has been used for classification, regressions, clustering of data and features learning. This is a good strategy for producing generalise features performance. It is also much faster than a network which is trained through back propagation.

Reducing the size of a large neural network to something much smaller and manageable is another problem for new researchers. In case of NLP, the only text has been considered for computing results, why not to produce such a model which can handle text, audio, video and images at the same time to produce probabilistic results.

Another research area includes for the optimisations of algorithms that are more Bayesian (rather than based on a single point estimate of the best parameters), that use more non-differentiable operations.

Automatic Machine Translation, for example, conversion of one language text into another language. Simple automatic translation of the text. Google has recently introduced such a technology using DL concepts. A device reads the text from your images and help out to save time. For example, if you are adding your credit card information for payment, just place your card in hand. Your credit card number will be read by a scanner and will be placed in the required field.

Generation of new handwriting and signature, The writing is provided as a sequence of coordinates used by a pen when the handwriting samples were created. From this corpus, the relationship between the pen movement and the letters is learned, and new examples can be generated ad hoc.

The automatic caption of images, for example, a man holding a pen & paper, then train such a model which gives caption. “That man is going to write a something on paper”. He is not going put pen in pocket and making an aeroplane of paper to fly over his head. Similarly, automatic conversion of the sketch into image or painting is another hot topic.

deep learning research ideas

What is the difference between Deep Learning and Machine Learning:

ML is type of Artificial Intelligence, while Deep learning is sub field of machine learning. It is concerned with the algorithms related to human brains functionality and their structure.  It is a very significant neural network which needs much more data and computational power to extract results. This is a field is related to provide such artificial networks where human thoughts involve. DL works more narrowly on ML techniques and its algorithms to provide more optimise results of real world problems.

Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features. Automatically learning features at multiple levels of abstraction allow a system to learn complex functions mapping the input to the output directly from data, without depending entirely on human-crafted features.

What are the limitations of DL:

  1. The biggest problem with DL is to have huge amount of data for better results
  2. Deep networks require massive computational power and resources
  3. Add reasoning capabilities to learning ones
  4. Time consuming
  5. Representation and Generalization