Hindi is one of the many official languages of India but spoken by the majority of Indians. More than 500 million Hindi (with Urdu) speakers as the first language [1] and probably another 500 million a second language, hence in my estimate, more than a billion people speak Hindi [*]. Only in Tamil Nadu which is a state with 60 million people, politicians are against Hindi [2], while the rest of India takes Hindi either as a first or second language. The Indian government tried to make Hindi the national language of India but facing difficulties [3].
A major limitation is that Fiji Hindi does not have enough books to be taught as a language, nor do teachers have qualifications, such as Bachelor of Education in Fiji Hindi (they do have Hindi major in Bachelor of Education). In my view, there are a number of inconsistencies and it cannot be considered as a full academic language; however, there has been research done to document Fiji Hindi, grammar and a lot of effort has been done by a limited number of academics [4]Nobody suggests that Fiji Hindi should not be recognised or not spoken. Pure or Sudh Hindi should be the main language as a subject in Fiji schools, and Fiji Hindi can be part of it. Fiji Hindi is needed, especially in the case of teaching a spoken language to indigenous (iTaukei, Rotuman) or Chinese students, who also attend Indian or Hindu schools.
Fijian government tried to implement Hindi and iTaukei language as separate mandatory subjects in all schools but failed since they don’t have enough Hindi and iTaukei teachers. Fiji Hindi does not exist as an academic language to teach, its a spoken language and will remain so unless there is a massive literature (novels, newspaper, etc) and research in Fiji Hindi. One way ahead in recruiting teachers would be to increase the pay scale for Hindi and iTaukei teachers, and then you will have more graduates in these subjects.I am aware that there is some work done in terms of linguistics and structure of Fiji Hindi, but there are limited literature and not much advantage in making it a written language in terms of using it for global communication with Indians worldwide. The matter is not about whether Fiji Hindi has a structure or not, but about teaching it in schools and implications of it, especially for Hinduism — as there is no Hindu text in Fiji Hindi. Everyone has an opinion about it, and we must listen to all groups, not just a single group of academics.
As for sacred Hindu texts, there exist translations in my languages including English [5]. I as a Fiji Indian do study the Bhagwad Gita in English apart from Hindi since at times some Hindi terms is a bit too hard to get as I have not done enough Hindi classes in secondary school. You can translate Bhagawad Gita in Fiji Hindi and other texts as well, but they will lose the essence. Especially in case of the Ramayana [6], since its a poem which is sung as Kirtans in weekly Ramayana events by Hindus. Note that the Hindi version of Ramayana, known as the Ramcharitmanas [7] is not a translation, but a poetic depiction of Ramayana from Sanskrit in Hindi by Tulsi Das [8]. It loses its essence when translated in English and will also do when translated in Fiji Hindi. Moreover, Ramayana and Gita in Fiji Hindi will not sound formal and will not be taken seriously and this is why most Hindu organisations are against it.
I believe that Hindi as a subject should be amended a bit to incorporate Fiji Hindi. Fiji Hindi is a spoken language, it does not have enough literature nor research to be taught as a language on its own. I think Hindi classes can incorporate Fiji Hindi with examples about spoken and written Fiji Hindi, and a nationwide curriculum can be developed irrespective of ethnicity and religion. I think all Fiji students should take Hindi and iTaukei languages in schools just as they take English and mathematics. This will help in Fijian reconciliation and unity. Those that have missed can easily take them as electives at the university level. Universities can provide iTaukei and Hindi as general subjects. There is no use in doing mandatory courses in the university such as Pacific philosophy and consciousness etc when you don’t understand Hindi and iTaukei languages and respective cultures.
Apart from Hindi, the government should provide funds for Telugu, Punjabi, Tamil and Bengali languages to be part of possible degrees that study Indian culture and diaspora. Furthermore, other Pacific languages, such as Rotuman, Samoan, Tongan and Chinese languages such as Mandarin and Cantonese need to be taught at the university level for possible teaching in primary and secondary schools.
Hindi is the language of the future for Indians and for the world!
Lakhan Gusain, “The Effectiveness of Establishing Hindi as a National Language”, Georgetown Journal of International Affairs, Vol. 13(1), 2012, pp. 43–50
Tutor: Mr. Rafael Possas, Research Engineer, Sydney Informatics Hub (2017)
Guest Speakers: TBA
Duration: This will be a 10 weeks course
Programming Language: The course will use Python as the main language. C++ will be used as an additional programming language.
Assignments: Assignments can be done in any language of choice by the participant.
Location: Rm 538, Centre for Translational Data Science (CDTS), 5th Floor, School of Information TechnologiesBuildingg (J12), The University of Sydney, 1 Cleveland Street, NSW 2006.
Contact: rohitash.chandra (at) sydney.edu.au
Disclaimer: The course is not accredited towards any of the programmes at the University of Sydney. There will be no certificates issued upon completion of the course.
Feedforward Neural Networks
Backpropagation and Stochastic Gradient Descent (SGD)
Bayesian Neural Networks (with MCMC and SGD)
Recurrent Neural Networks
Long Short-Term Memory (LSTM)
Neuroevolution (Coevolution and Direct Neuro-evolution)
Deep Learning with Convolutional Neural Networks
Multi-task learning and Transfer Learning
Ensemble learning and Hybrid Methods
Modular Neural Networks
Applications: Pattern Classification, Time Series Prediction, and Computer Vision
Implement simple neural network architectures from scratch (without relying on machine learning libraries)
Develop rich applications using neural networks that involve real world problems
Become ready to work and contribute to challenging problems that arise in training and representation of knowledge in different neural network architectures.
Choose either Option I or Option II, or tackle both.
Group: 1 – 4 members.
Deadline: Begining of Week 3 of the course.
Option I: Fundamentals
Extend the basic FNN python code (fnn_v1.py, fnn_v2.py or fnn.v3.py) to include an additional hidden layer and compare the performance with original FNN with a single hidden layer. Carry out 30 experimental runs and report the mean and std or training and test datasets.
Extend your further so that it can be generalized for any number of hidden layers. Run experiences to see the effect from 1 – 5 hidden layers.
Experiment on different combinations of hidden layers and neurons for each hidden layer.
Apply the strategy used in (2 and 3) for chaotic time series problems that include Sunspot, Lazer, Mackey-Glass and Lorenz problems (Discussed in Week 2).
Option II: Application
Extend the application FNN code in Keras for additional number of hidden layers as given in Option I
Apply FNN in Keras for chaotic time series problems (Discussed in week 2)
Deliverable
Present your results in a Technical Report and discuss your major findings for the different problems ( 10 min presentation per group will be organized).
Submission: email pdf of the technical report to rohitash.chandra@sydney.edu.au
Deadline: 26th June 2017.
Best code will become part of the solution set for the course.
Extend FNN Python code and into a Recurrent Neural Network. You can use a reference to C++ Elman RNN code discussed in class. Implement BPTT for Python RNN.
Use time series prediction problems and Tomita Grammer (Chandra, 2011 Neurocomputing paper) problem to test your RNN
Bayesian Recurrent Neural Networks: Use MCMC for training RNNs
Option 2: Applications
Implement a simple (Elman) RNN from any existing libraries (Keras preferred) for time series prediction.
Use Keras implementation for RNN for any other problem of your choice (Speech, signature, or NLP)
Extend FNN Python code and into a Recurrent Neural Network. You can use a reference to C++ Elman RNN code discussed in class. Implement BPTT for Python RNN.
Use time series prediction problems and Tomita Grammer (Chandra, 2011 Neurocomputing paper) problem to test your RNN
Bayesian Recurrent Neural Networks: Use MCMC for training RNNs
Option 2: Applications
Implement a simple (Elman) RNN from any existing libraries (Keras preferred) for time series prediction.
Use Keras implementation for RNN for any other problem of your choice (Speech, signature, or NLP)
Use Keras for LSTM implementation for any selected pattern recognition, time series or classification problems that involve long term-dependencies. Language models could be also considered.
Use Keras for CNN implementation for any selected datasets that involve, face, object and gesture recognition.
Highlight an application when it’s necessary to have a combination of LSTMs and CNN’s and discuss implementation issues.
Here is my Netflix list of international movies and series – keeping in mind those not heard of and those with good art and drama. Those below 7/10 not listed.
Rohitash Chandra 