From 2ed34cc35ece1ee1bc3709f51b2b0c68e954b6e4 Mon Sep 17 00:00:00 2001 From: medusa Date: Fri, 26 Apr 2024 11:09:43 +0000 Subject: [PATCH] Add docs/tech_docs/llm/ai_over_view.md --- docs/tech_docs/llm/ai_over_view.md | 56 ++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) create mode 100644 docs/tech_docs/llm/ai_over_view.md diff --git a/docs/tech_docs/llm/ai_over_view.md b/docs/tech_docs/llm/ai_over_view.md new file mode 100644 index 0000000..4571be0 --- /dev/null +++ b/docs/tech_docs/llm/ai_over_view.md @@ -0,0 +1,56 @@ +# Technical Deep-Dive: AI Fundamentals + +## 1. Generative AI + +Generative AI focuses on creating new content, such as images, text, or audio, using deep learning models. The most popular generative models include: + +- Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, that compete against each other. The generator learns to create realistic content, while the discriminator learns to distinguish between real and generated content. +- Variational Autoencoders (VAEs): VAEs are probabilistic models that learn to encode input data into a lower-dimensional latent space and then decode it back to the original space, enabling the generation of new samples. +- Transformer-based Models: Models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) have shown remarkable success in generating coherent and contextualized text. + +Key techniques in Generative AI include adversarial training, latent space manipulation, and attention mechanisms. + +## 2. Natural Language Processing (NLP) + +NLP deals with the interaction between computers and human language. It involves various tasks, such as: + +- Text Preprocessing: Tokenization, stemming, lemmatization, and removing stop words. +- Word Embeddings: Representing words as dense vectors to capture semantic relationships (e.g., Word2Vec, GloVe). +- Sequence Modeling: Capturing sequential dependencies using models like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. +- Attention Mechanisms: Allowing models to focus on relevant parts of the input (e.g., self-attention in Transformers). +- Named Entity Recognition (NER): Identifying and classifying named entities in text. +- Sentiment Analysis: Determining the sentiment or emotion expressed in text. +- Machine Translation: Translating text from one language to another. + +Popular NLP libraries include NLTK, spaCy, and Hugging Face's Transformers. + +## 3. Deep Learning + +Deep Learning involves training artificial neural networks with multiple layers to learn hierarchical representations from data. Key concepts and architectures in deep learning include: + +- Feedforward Neural Networks: The simplest type of neural network where information flows in one direction from input to output. +- Convolutional Neural Networks (CNNs): Designed to process grid-like data (e.g., images), CNNs use convolutional layers to learn local patterns and hierarchical features. +- Recurrent Neural Networks (RNNs): Designed to handle sequential data, RNNs have feedback connections that allow information to persist across time steps. +- Autoencoders: Neural networks that learn to compress and reconstruct input data, often used for dimensionality reduction and feature learning. +- Regularization Techniques: Methods like L1/L2 regularization, dropout, and early stopping to prevent overfitting. +- Optimization Algorithms: Techniques like Stochastic Gradient Descent (SGD), Adam, and RMSprop for training neural networks efficiently. + +Popular deep learning frameworks include TensorFlow, PyTorch, and Keras. + +## 4. Computer Vision + +Computer Vision focuses on enabling computers to interpret and understand visual information from images and videos. Key tasks and techniques in computer vision include: + +- Image Preprocessing: Resizing, normalization, and data augmentation techniques. +- Feature Extraction: Techniques like edge detection, corner detection, and scale-invariant feature transform (SIFT) to extract meaningful features from images. +- Object Detection: Identifying and localizing objects within an image using models like YOLO (You Only Look Once), Faster R-CNN, and SSD (Single Shot MultiBox Detector). +- Semantic Segmentation: Assigning a class label to each pixel in an image, enabling precise object boundaries and scene understanding. +- Image Classification: Assigning a class label to an entire image using deep learning models like CNNs (e.g., ResNet, Inception, VGGNet). +- Optical Flow: Estimating the motion of objects between consecutive frames in a video. +- Pose Estimation: Detecting and tracking the location and orientation of objects or body parts in images or videos. + +Popular computer vision libraries include OpenCV, scikit-image, and TensorFlow's object detection API. + +This technical deep-dive provides an overview of the key concepts, techniques, and libraries in Generative AI, Natural Language Processing, Deep Learning, and Computer Vision. Each of these topics is vast and rapidly evolving, with numerous subtopics and advanced techniques. + +To dive deeper into any specific area, I recommend exploring research papers, online courses, and practical projects that align with your interests. Feel free to ask for more detailed explanations or examples on any particular subtopic within these domains. \ No newline at end of file