資訊科技產業專案設計課程作業 3

# 資訊科技產業專案設計課程作業 3 [Resume](https://drive.google.com/file/d/1O2QYe8qdBpErPHQhk6-kmxnQ6qbD4C1S/view?usp=drive_link) # [NVIDIA - AI Computing Software Development Engineer, TensorRT](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/Taiwan-Taipei/AI-Computing-Software-Development-Engineer--TensorRT_JR1970700?locationHierarchy1=2fcb99c455831013ea52ed162d4932c0) What we need to see: * Masters or higher degree in Computer Engineering, Computer Science, Applied Mathematics or related computing focused degree (or equivalent experience) * 3+ years of relevant software development experience. * Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design. * Strong curiosity about artificial intelligence, awareness of the latest developments in deep learning like LLMs, generative and recommender models. * Experience working with deep learning frameworks like TensorFlow and PyTorch. * Proactive and able to work without supervision. * Excellent written and oral communication skills in English. # [NVIDIA - AI Algorithms SW Engineer - New College Graduate](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/Taiwan-Taipei/AI-Algorithms-SW-Engineer---New-College-Graduate_JR1973032?locationHierarchy1=2fcb99c455831013ea52ed162d4932c0) Preferred qualifications: * MS or PhD in Computer Science, Computer Engineering or Electrical Engineering or related field in Deep Learning, Machine Learning and Computer Vision. * Algorithm development experience data analytics, especially with LLM’s and Multi-Modal Foundation models. * Experience working with deep learning frameworks like TensorFlow and pyTorch. * Strong communication skills. :::success 問題 --- Q: Could you provide an example of when you utilized machine learning algorithms in real-world applications? A: My master's thesis focuses on the analysis of pathological images using deep learning techniques. I employ a weakly supervised learning approach to identify potential tumor regions in pathological images without the need for pixel-level annotations. Q: How would you apply your understanding of artificial intelligence to develop autonomous vehicle systems? A: In developing autonomous vehicle systems, I apply computer vision techniques, especially deep learning, to interpret data from sensors such as cameras, LiDAR, and radar. This enables the vehicle to identify and track objects in its environment. Additionally, I integrate information from diverse sensors using AI algorithms, enhancing the overall understanding of the surroundings by combining data sources and improving decision-making accuracy. Q: Share an instance where you had to adapt quickly to new programming languages or tools. A: In 2023, I served as the developer for the MMAsia2023 registration system. Starting from scratch, I learned the Django web framework and successfully deployed the website to a cloud platform. Despite tight deadlines, I dedicated significant time to researching web development and database integration, ensuring the website was launched within the specified timeframe. ::: :::info 資訊整理 --- [Top 25 NVIDIA Software Engineer Interview Questions & Answers]( https://interviewprep.org/nvidia-software-engineer-interview-questions/) ::: # [REALTEK - Machine Learning軟體設計工程師](https://recruit.realtek.com/Job/JobDetail?jobid=1425) 學歷要求 : 碩士工作項目： * Developing image/video and audio related deep neural network models to help improve audio and video quality as well as to help create new applications with artificial intelligence for achieving better user experiences. 應徵條件： 1. 熟悉 Python, matlab, TensorFlow, PyTorch, Keras, C, C++, Java, machine learning frameworks. 2. 論文研究方向或在學期間有修習過 machine learning相關課程為佳。 :::success 問題 --- Q: What is the difference between regularization and normalisation? A: Regularization is about preventing overfitting by penalizing complex models, while normalization is about standardizing and scaling input features to facilitate the learning process for the model. Q: List the advantages and disadvantages of using Neural Networks. A: Advantages: We can store information on the entire network instead of storing it in a database. It has the ability to work and give a good accuracy even with inadequate information. A neural network has parallel processing ability and distributed memory. Disadvantages: Neural Networks requires processors which are capable of parallel processing. It’s unexplained functioning of the network is also quite an issue as it reduces the trust in the network in some situations like when we have to show the problem we noticed to the network. Duration of the network is mostly unknown. We can only know that the training is finished by looking at the error value but it doesn’t give us optimal results. Q: How to leverage deep learning for the analysis of audio or video? A: I can utilize the Transformer architecture for video processing, treating each frame as a time step in the sequence. The attention mechanism in the Transformer can be employed to capture temporal dependencies and dynamic features over time.Regarding audio, similarly, each frame can undergo Fourier transformation or other relevant conversion methods to be transformed into a spectrogram. Just like in video processing, each time step of the spectrogram is treated as a unit in the sequence. Each spectrogram representation can be considered a time step, and these spectrogram sequences can be input into a Transformer model. ::: :::info 資訊整理 --- [Top 170 Machine Learning Interview Questions and Answers (2024)](https://www.mygreatlearning.com/blog/machine-learning-interview-questions/) :::