Perceptron - Study guides, Class notes & Summaries

CS559 Machine Learning Support Vector Machine CS559 Machine Learning Support Vector Machine Tian Han Department of Computer Science Stevens Institute of Technology Week 14 • Linear classifier, large margin, SVM • Non-separable case, slack variable • Non-linearity, kernels Linear classifier, large margin, SVM • Linear classifiers construct linear decision boundaries(hyperplanes) that try to separate the data into dif...

Deep Learning – History - Key facts – Biological Neuron- Artificial Neuron – Mc Culloch Pitts Neuron, Limitations of MP Neuron – Perceptron – Multi-Layer Perceptron – Basics of Neural Networks :Neurons, Output Functions – Activation Functions – Weights and Biases

The curriculum begins with Module 1: Hypothesis Testing, which lays the groundwork for statistical analysis in biomedical data. It starts with an introduction to Python, essential for the practical components of the course, followed by reviews of probability and statistics to refresh and solidify foundational knowledge. Students learn various hypothesis testing methods, including parametric and non-parametric statistics, the considerations for multiple comparisons, and resampling-based statistic...

An extensive summary of the Machine Learning course at UvT (Fall 2021).

It is the notes that I have written while I was learning deep learning. It contains a deep learning introduction, ANN, CNN, Activation functions, RNN, Perceptron, etc. with a well-mannered summarised format.

Cheat sheet for the course 'Introduction to Deep Learning' at Tilburg University, including some practice exercises. 3rd year course for CSAI, I got a 7.5 using this!

Unlock the power of deep learning with these comprehensive and meticulously crafted notes. Designed for students, researchers, and practitioners, this invaluable resource offers a comprehensive exploration of key concepts and techniques in the field of deep learning. Immerse yourself in the world of Convolutional Neural Networks (CNNs), understanding how they revolutionize computer vision tasks such as image classification, object detection, and semantic segmentation. Dive deep into Recurrent...

All lectures including additional explanations where the lectures/slides fall short!

Deze samenvatting bevat alle stof voor het vak machine learning op UVT. Alle notebooks inclusief voorbeelden zijn toegevoegd. Alle colleges zijn bijgewoond, samengevat en uitgewerkt.

Summary for Tilburg University's Deep Learning course (academic year 2019-2020). All 6 lectures are discussed: introduction, Multi-Layer Perceptron, Convolutional Neural Networks, Optimizers and Regularization, Sequence Modeling and Generative Models.