Reference no: EM133360710
DL Project Topics
1: Image classification using the CIFAR-10 dataset - This project involves training a CNN to classify images from the CIFAR-10 dataset into their respective categories.
2: Object detection using the COCO dataset - This project involves training a CNN to detect objects in images from the COCO dataset.
3: Facial expression recognition using the FER2013 dataset - This project involves training a CNN to recognize facial expressions in images from the FER2013 dataset. You can use AlexNet for the project.
4: Image segmentation using the Pascal VOC dataset - This project involves training a CNN to perform semantic segmentation on images from the Pascal VOC dataset.
5: Lung nodule detection using the LIDC-IDRI dataset - This project involves training a CNN to detect lung nodules in CT scans from the LIDC-IDRI dataset.
6: Street sign recognition using the GTSRB dataset - This project involves training a CNN to recognize street signs in images from the GTSRB dataset.
7: Chest X-ray classification for pneumonia detection using the ChestX-ray8 dataset - This project involves training a CNN to classify chest X-ray images as either normal or having pneumonia.
8: Video action recognition - This project involves training a CNN to recognize actions in videos from the UCF101 dataset.
9: Semantic segmentation of medical images: Implement a CNN to segment medical images
10: Emotion detection from facial expressions: Implement a CNN to classify facial expressions as different emotions
11: Image captioning: Implement a CNN to generate textual captions for images
12: Video action recognition: Implement a CNN to recognize and classify actions in videos
13: Traffic sign classification: Implement a CNN to classify traffic signs in images
14: Human pose estimation: Implement a CNN to estimate human poses in images
15: Fine-grained image classification: Implement a CNN to classify images with fine-grained distinctions
16: Salient object detection: Implement a CNN to detect and highlight the most visually salient objects in images
17: Sentiment Analysis using LSTM: Use LSTM to predict the sentiment of a given text.
18: Music Generation with RNN: Train an RNN on a dataset of MIDI files to generate new
19: Music Genre Classification using LSTM: Develop an LSTM model to classify music into different genres.
20: Stock Price Prediction using LSTM: Use LSTM to predict the future stock price of a company based on its historical price data.
21: Speech Recognition using LSTM: Train an LSTM to recognize spoken words from an audio input.
22: Chatbot using LSTM: Train an LSTM to generate responses to text input
23: Poetry Generation using LSTM: Develop an LSTM model to generate new poetry based on existing poetry.
24: Language Translation using LSTM: Develop an LSTM model to translate text from one language to another.
25: Image Captioning using LSTM: Develop an LSTM model to generate captions for images.
26: Speech Emotion Recognition using LSTM: Develop an LSTM model to recognize the emotions in spoken words or phrases.
27: Text Generation using LSTM: Develop an LSTM model to generate new text based on existing text.
28: Sentiment analysis - The goal of this project is to train a Word2vec model on a dataset of movie reviews and use the learned word embeddings to predict the sentiment of a given sentence.
29: Information retrieval - The goal of this project is to train a Word2vec model on a corpus of documents and use the learned word embeddings to retrieve relevant documents for a given query.
If you're looking for help with a Deep Learning project assignment, there are a few key steps you can take to get started:
Understand the assignment requirements: Make sure you fully understand what the assignment is asking you to do. This will help you identify the relevant tools, techniques, and datasets you'll need to complete the project.
Research relevant resources: Look for online resources, tutorials, and example projects that relate to your assignment. This can help you understand the concepts and techniques you'll need to apply in your own project.
Plan your approach: Break the project down into smaller tasks and develop a plan for how you'll complete each one. This can help you stay organized and ensure you're making progress towards the final deliverable.
Practice coding: Deep Learning projects typically require coding, so it's important to practice writing code in the relevant programming language(s) you'll be using.
Seek help if needed: Don't be afraid to reach out to your instructor or online assistance if you're struggling with a particular aspect of the project. There are also online communities and websites dedicated to Deep Learning where you can ask questions and get advice from experts in the field.