Computer-Vision-Natural-Language-Processing

StartupCampus AI Assignment - Batch 7

This repository contains the solutions for the AI assignments in StartupCampus AI Batch 7. The project covers four cases, two focused on Computer Vision and two on Natural Language Processing (NLP). Below are the details of the assignments.

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Case 1: Computer Vision - Image Classification (CIFAR-10)

Problem Statement:

In this case, the task is to classify images from the CIFAR-10 dataset into one of ten categories. The goal is to build a model using a deep learning approach, particularly using Convolutional Neural Networks (CNNs), to achieve high accuracy in classifying these images.

Approach:

• Dataset: CIFAR-10, which contains 60,000 32x32 color images in 10 classes, with 6,000 images per class.
• Model: Convolutional Neural Network (CNN) with multiple convolutional layers and fully connected layers.
• Framework: TensorFlow and Keras.
• Preprocessing: Image normalization, data augmentation to improve model robustness.

Implementation Steps:

1. Load and preprocess the CIFAR-10 dataset.
2. Define the CNN model architecture.
3. Compile the model with an appropriate optimizer and loss function.
4. Train the model on the training dataset.
5. Evaluate the model on the test set.

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Case 2: Computer Vision - Object Detection (YOLO)

Problem Statement:

This case focuses on object detection using the YOLO (You Only Look Once) algorithm, which is designed for real-time object detection. The goal is to detect various objects in images and classify them into predefined categories.

Approach:

• Dataset: Custom dataset with labeled objects for detection.
• Model: YOLO (You Only Look Once), a popular real-time object detection algorithm.
• Framework: TensorFlow, Keras, and OpenCV.
• Preprocessing: Image resizing, normalization, and bounding box adjustments.

Implementation Steps:

1. Load and preprocess the dataset (images with bounding boxes).
2. Define the YOLO architecture.
3. Train the model using the labeled dataset.
4. Implement real-time detection using the trained model.
5. Evaluate the performance using precision, recall, and mAP (mean Average Precision).

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Case 3: Natural Language Processing (NLP) - Text Classification (Sentiment Analysis)

Problem Statement:

The objective of this case is to build a sentiment analysis model that can classify movie reviews as positive or negative. The task involves preprocessing text data and applying deep learning techniques for text classification.

Approach:

• Dataset: IMDb movie reviews dataset with labeled sentiment (positive or negative).
• Model: Recurrent Neural Networks (RNNs) or LSTM (Long Short-Term Memory) for sequence modeling.
• Framework: TensorFlow and Keras.
• Preprocessing: Tokenization, padding, and word embedding (e.g., GloVe or Word2Vec).

Implementation Steps:

1. Load and preprocess the IMDb dataset.
2. Tokenize the text data and pad the sequences.
3. Build and train the LSTM model for sentiment classification.
4. Evaluate the model’s performance on test data using accuracy and F1-score.
5. Fine-tune the model for better performance.

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Case 4: Natural Language Processing (NLP) - Named Entity Recognition (NER)

Problem Statement:

This case focuses on the task of Named Entity Recognition (NER), where the goal is to identify and classify named entities (such as person names, locations, organizations, etc.) in a given text.

Approach:

• Dataset: CoNLL-2003 dataset or custom NER dataset.
• Model: Bidirectional LSTM (BiLSTM) with Conditional Random Fields (CRF) for sequence tagging.
• Framework: TensorFlow and Keras.
• Preprocessing: Tokenization, sequence labeling, and embedding layer for words.

Implementation Steps:

1. Load and preprocess the NER dataset.
2. Tokenize the text and prepare the sequences with labels.
3. Build the BiLSTM model with CRF layers.
4. Train the model and evaluate its performance using metrics like precision, recall, and F1-score.
5. Perform error analysis and improve the model as necessary.

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Technologies Used:

• Python 3.x
• TensorFlow and Keras for deep learning models
• NumPy and Pandas for data manipulation
• Matplotlib and Seaborn for visualization
• OpenCV for image processing
• NLTK, SpaCy, and TensorFlow Hub for NLP tasks