Python for AI

5.

Specialized AI Applications

5.1.

Natural Language Processing

5.1.1.

Text Preprocessing

5.1.1.1.

5.1.1.2.

5.1.1.2.1.

Word Tokenization

5.1.1.2.2.

Sentence Tokenization

5.1.1.2.3.

Subword Tokenization

5.1.1.3.

5.1.1.3.1.

5.1.1.3.2.

Punctuation Removal

5.1.1.3.3.

Special Character Handling

5.1.1.4.

Stop Word Removal

5.1.1.5.

5.1.1.5.1.

5.1.1.5.2.

Snowball Stemmer

5.1.1.6.

5.1.1.7.

Text Segmentation

5.1.2.

Feature Extraction

5.1.2.1.

5.1.2.1.1.

5.1.2.1.2.

5.1.2.1.3.

5.1.2.2.

5.1.2.2.1.

5.1.2.2.2.

Inverse Document Frequency

5.1.2.2.3.

TF-IDF Calculation

5.1.2.3.

5.1.2.3.1.

5.1.2.3.2.

5.1.2.3.3.

5.1.2.3.4.

Character N-grams

5.1.3.

Word Embeddings

5.1.3.1.

5.1.3.1.1.

Skip-gram Model

5.1.3.1.2.

5.1.3.1.3.

Training Word2Vec

5.1.3.2.

5.1.3.2.1.

5.1.3.2.2.

Co-occurrence Matrix

5.1.3.3.

5.1.3.3.1.

Subword Information

5.1.3.3.2.

Out-of-vocabulary Handling

5.1.3.4.

Embedding Visualization

5.1.3.5.

Embedding Evaluation

5.1.4.

Language Models

5.1.4.1.

Statistical Language Models

5.1.4.2.

Neural Language Models

5.1.4.3.

Transformer Architecture

5.1.4.4.

Attention Mechanisms

5.1.5.

5.1.5.1.

5.1.5.1.1.

Tokenization Tools

5.1.5.1.2.

5.1.5.1.3.

Part-of-speech Tagging

5.1.5.1.4.

Named Entity Recognition

5.1.5.2.

5.1.5.2.1.

5.1.5.2.2.

Pipeline Components

5.1.5.2.3.

5.1.5.3.

5.1.5.3.1.

5.1.5.3.2.

Word Embeddings

5.1.5.3.3.

Document Similarity

5.1.6.

Advanced NLP with Transformers

5.1.6.1.

Transformer Architecture

5.1.6.2.

Self-attention Mechanism

5.1.6.3.

Multi-head Attention

5.1.6.4.

Positional Encoding

5.1.7.

Pre-trained Models

5.1.7.1.

5.1.7.1.1.

Bidirectional Encoding

5.1.7.1.2.

Masked Language Modeling

5.1.7.1.3.

Next Sentence Prediction

5.1.7.2.

5.1.7.2.1.

Generative Pre-training

5.1.7.2.2.

Autoregressive Generation

5.1.7.3.

5.1.7.4.

5.1.7.5.

5.1.8.

Hugging Face Ecosystem

5.1.8.1.

Transformers Library

5.1.8.2.

5.1.8.3.

5.1.8.4.

Datasets Library

5.1.9.

Fine-tuning Transformers

5.1.9.1.

Task-specific Fine-tuning

5.1.9.2.

Transfer Learning

5.1.9.3.

Domain Adaptation

5.1.10.

5.1.10.1.

Text Classification

5.1.10.1.1.

Sentiment Analysis

5.1.10.1.2.

Topic Classification

5.1.10.1.3.

Intent Recognition

5.1.10.2.

Named Entity Recognition

5.1.10.2.1.

5.1.10.2.2.

5.1.10.2.3.

Custom NER Models

5.1.10.3.

Question Answering

5.1.10.3.1.

5.1.10.3.2.

5.1.10.3.3.

Reading Comprehension

5.1.10.4.

Text Generation

5.1.10.4.1.

Language Generation

5.1.10.4.2.

Text Completion

5.1.10.4.3.

Creative Writing

5.1.10.5.

Machine Translation

5.1.10.5.1.

Sequence-to-sequence Models

5.1.10.5.2.

Attention in Translation

5.1.10.5.3.

Evaluation Metrics

5.2.

Computer Vision

5.2.1.

Image Processing Fundamentals

5.2.1.1.

Digital Image Representation

5.2.1.2.

5.2.1.2.1.

RGB Color Space

5.2.1.2.2.

HSV Color Space

5.2.1.2.3.

Grayscale Conversion

5.2.1.3.

Image Properties

5.2.1.3.1.

5.2.1.3.2.

5.2.1.3.3.

5.2.2.

OpenCV for Image Processing

5.2.2.1.

Reading and Writing Images

5.2.2.2.

5.2.2.3.

Basic Image Operations

5.2.2.3.1.

5.2.2.3.2.

5.2.2.3.3.

5.2.2.3.4.

5.2.2.3.5.

5.2.2.4.

Color Space Conversions

5.2.2.5.

Image Filtering

5.2.2.5.1.

5.2.2.5.2.

5.2.2.5.3.

Bilateral Filter

5.2.2.6.

5.2.2.6.1.

Canny Edge Detection

5.2.2.6.2.

5.2.2.6.3.

Laplacian Edge Detection

5.2.2.7.

Morphological Operations

5.2.2.7.1.

5.2.2.7.2.

5.2.2.7.3.

5.2.2.7.4.

5.2.2.8.

Contour Detection

5.2.2.9.

Feature Detection

5.2.2.9.1.

Corner Detection

5.2.2.9.2.

5.2.2.9.3.

5.2.3.

Deep Learning for Computer Vision

5.2.3.1.

Convolutional Neural Networks

5.2.3.1.1.

Convolution Operation

5.2.3.1.2.

Convolutional Layers

5.2.3.1.3.

5.2.3.1.3.1.

5.2.3.1.3.2.

Average Pooling

5.2.3.1.3.3.

5.2.3.1.4.

Fully Connected Layers

5.2.3.1.5.

CNN Architecture Design

5.2.3.2.

Popular CNN Architectures

5.2.3.2.1.

5.2.3.2.2.

5.2.3.2.3.

5.2.3.2.4.

5.2.3.2.5.

Inception Networks

5.2.3.2.6.

5.2.3.2.7.

5.2.4.

Computer Vision Tasks

5.2.4.1.

Image Classification

5.2.4.1.1.

Single-label Classification

5.2.4.1.2.

Multi-label Classification

5.2.4.1.3.

Fine-grained Classification

5.2.4.2.

Object Detection

5.2.4.2.1.

Bounding Box Prediction

5.2.4.2.2.

5.2.4.2.3.

5.2.4.2.4.

5.2.4.2.5.

5.2.4.3.

Image Segmentation

5.2.4.3.1.

Semantic Segmentation

5.2.4.3.2.

Instance Segmentation

5.2.4.3.3.

Panoptic Segmentation

5.2.4.3.4.

U-Net Architecture

5.2.4.4.

Face Recognition

5.2.4.4.1.

5.2.4.4.2.

Face Verification

5.2.4.4.3.

Face Identification

5.2.4.5.

Optical Character Recognition

5.2.4.5.1.

5.2.4.5.2.

Text Recognition

5.2.4.5.3.

Document Analysis

5.2.5.

Transfer Learning in Computer Vision

5.2.5.1.

Pre-trained Models

5.2.5.2.

Feature Extraction

5.2.5.3.

Fine-tuning Strategies

5.2.5.4.

Domain Adaptation

5.2.6.

Data Augmentation

5.2.6.1.

Geometric Transformations

5.2.6.2.

Color Transformations

5.2.6.3.

5.2.6.4.

Cutout and Mixup

5.2.6.5.

Advanced Augmentation Techniques

5.2.7.

Computer Vision Libraries

5.2.7.1.

5.2.7.2.

5.2.7.3.

5.2.7.4.

5.2.7.5.

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