Machine Learning Grind

Note

helloo ... i'm studying Machine Learning and Deep Learning based on books Understanding Deep Learning, Dive into Deep Learning, Hands-on Machine Learning with Scikit-Learn Keras & TensorFlow & Machine Learning: A Probabilistic Perspective

Content

Stats and Machine Learning

• Random Variables
• Principal Component Analysis
• Support Vectors Machine
• Monte Carlo Approximation
• Univariate Sampling Techniques
• Decision Tree
• Bayesian Networks
• Gaussian Processes
• Bayesian Inference
• Gaussian Discriminant Analysis
• EM Algorithm

Neural Networks

• Shallow Neural Network
• Bias & Variance

Optimization and Regularization

• Weight Decay
• Dropout

Computer Vision

• 1D Convolutional Neural Network VS Fully Connected Network
• LeNet
• AlexNet
• VGGNet
• U-Net

Natural Language Processing

• Introduction: tokenization, bag of words/ngrams, tfidf
• Language modeling
• RNNs
• LSTMs
• GRUs
• Deep RNNs
• Bidirectional RNNs
• Seq2Seq Encoder Decoder
• Seq2Seq Attention Encoder Decoder
• GPT
• GPT 2

Generative Adversarial Networks

• GAN: Introduction
• Conditional GAN
• DCGAN
• InfoGAN
• Least squares GAN
• AutoEncoders
• VAE
• DDPM
• DDIM