Overhyped college

The course is good but price 120000 is not valuable for the way they teach , improvement needed in faculty member and change in faculy who hve no knowledge can be a benifit, the teaching is too fast and not inderstandable

Infrastructure is not so good , college looks like swimming pool when it rains,cannot even walk, the college have scholarship facilities , course fee is 120000,systems need frewuent repairs ,ethernet cabeles foesnt work properly

Campus life is also boring u cannot bunk , club interactions are very less they only interact in the starting days of 1st year and also events are also not organized only 2 or 3 events per year and mainly routine focus everyday on just studies

My rank was not sufficient to get into another colleges and i thought this is the best college for my rank and i regret joining now , i should have opted for 2nd and 3rd counselling , i dont like it here very strict rules cannot even have lunch in lunch time

Fsculty members are chill and friendly but some faculty are very rude also with no knowledge and speaking skills trying hard to speak in english, course fee is like 120000 and exams are very strict cameras everywhere

Defining the Problem and Objectives: Developers identify the specific tasks the model will perform, such as generating text, answering questions, or summarizing information. Data Collection and Preparation: Gathering and processing large amounts of text and code data from various sources to train the model. Model Architecture Setup: Selecting and configuring the algorithms and neural network structure to enable learning from the collected data. Pre-Training: Exposing the model to vast datasets to learn general patterns and relationships in the language. Fine-Tuning: Refining the initial training for specialized tasks or applications using smaller, focused datasets to improve performance in those areas. Evaluation and Validation: Conducting rigorous testing to assess performance, identify biases, and ensure quality and ethical standards before deployment. Deployment: Deploying the model into the intended infrastructure (e.g., cloud platforms) and integrating it with other systems or applications. Monitoring and Maintenance: Continuously monitoring performance, updating with new data, and refining over time to maintain accuracy and address potential issues. This data-driven process of learning and refinement is how AI models come into being and continue to function, rather than through traditional human-centric admission procedures.

Night life i dont know but i attended hacathons at overnight, the climate is cool but no stalls to have food , the college look scary at nights , hostels are also not so good,the college is in the middle of forest so looks kinda scary