Skip to content

Machine Learning (ML)

Machine learning is a subset of artificial intelligence (AI) that focuses on developing algorithms that allow computers to learn from and make predictions or decisions based on data. Machine learning algorithms can be classified into three categories: supervised learning, unsupervised learning, and reinforcement learning.

Deep Learning

Deep learning is a subset of machine learning that focuses on developing neural networks with multiple layers to model complex patterns in large datasets. Deep learning algorithms have been used to achieve state-of-the-art performance in various tasks, such as image recognition, natural language processing, and speech recognition.

  • Uses large, layered neural networks to learn from a massive amount of data
  • neural network uses multiple layers
  • Input layer: Where raw data is fed into the model
  • Hidden layers: Intermediate layers that analyze and transform the data
  • Output layer: The final layer that produces the model's predictions

Deep learning example

  • Image recognition
  • Natural language processing
  • Speech recognition
  • Autonomous vehicles
  • Healthcare
  • Finance

ML Terms

GPT

Generative Pre-trained Transformer (GPT) is a type of deep learning model that uses transformers to generate human-like text. GPT models have been used for various natural language processing tasks, such as text generation, translation, and summarization.

bert

Bidirectional Encoder Representations from Transformers (BERT) is a type of deep learning model that uses transformers to understand the context of words in a sentence. BERT models have been used for various natural language processing tasks, such as question answering, sentiment analysis, and named entity recognition.

RNN

Recurrent Neural Networks (RNNs) are a type of neural network that is designed to handle sequential data. RNNs have been used for various tasks, such as speech recognition, language modeling, and machine translation.

ResNet

Residual Networks (ResNets) are a type of deep learning model that uses residual connections to enable the training of very deep neural networks. ResNets have been used for various computer vision tasks, such as image classification, object detection, and image segmentation.

SVM

Support Vector Machines (SVMs) are a type of supervised learning algorithm that is used for classification and regression tasks. SVMs work by finding the optimal hyperplane that separates the data points into different classes.

Wavenet

WaveNet is a type of deep learning model that uses dilated convolutions to generate high-quality audio waveforms. WaveNet models have been used for various audio processing tasks, such as speech synthesis, music generation, and noise reduction.

Gan

Generative Adversarial Networks (GANs) are a type of deep learning model that consists of two neural networks: a generator and a discriminator. GANs are used to generate realistic data samples, such as images, audio, and text.

XGBoost

Extreme Gradient Boosting (XGBoost) is a type of ensemble learning algorithm that is used for classification and regression tasks. XGBoost works by combining multiple weak learners to create a strong learner that can make accurate predictions.