Learn · Neural Networks for Computer Vision and Natural Language Processing
Lecture 4 of 47Week 1: Introduction to Machine Learning
Logistics Regression
Prof Arijit Sur · IIT Guwahati
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- Neural Networks for Computer Vision and Natural Language Processing, IIT Guwahati (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures
- Introduction to Learning
- Supervised Learning (Regression)
- Gradient Descent
- Logistics Regression
- Optimization for supervised models
- Neural Network - I (Single Layer Perceptron)
- Neural Network - II (Multi Layer Perceptron)
- Neural Network – III (Training of the Multi Layer Perceptron)
- Introduction to Deep Learning - I
- Introduction to Deep Learning - II
- Introduction to Deep Learning - III
- Computer Vision Through Machine Learning
- Natural Language Processing Through Machine Learning
- Convolutional Neural Network - I
- Convolutional Neural Network - II (Model Architecture)
- Optimizing Convolutional Neural Network
- Optimizing Convolutional Neural Network - II
- Optimizing Convolutional Neural Network - III
- CNN With Residual Connections - ResNet
- Recurrent Neural Network - I
- Recurrent Neural Network - II
- LSTM
- Sequential Modelling (Part - II)
- Generative AI
- Generative Modelling
- Generative Adversarial Network
- Generative Adversarial Network - II
- Variational Auto-Encoder - I
- Variational Auto-Encoder - II
- Diffusion Model - I
- Diffusion Model - II
- Auto-Regressive Models
- Transormers - I
- Transormers - II
- Large Language Model - I
- Large Language Model - II
- Encoder only Transformer Model
- Decoder only Transformer Model
- Deep Learning for Vision Applications
- Image and Video Restoration
- Medical Imaging
- Zero-shot Learning
- Few-shot Learning
- Multimodal Deep Learning
- Multimodal Deep Models
- Visual Simultaneous Localization and Mapping
- Self-Supervised Learning