Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz

Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|

Tensorflow | 177,889 | 327 | 77 | 3 hours ago | 46 | October 23, 2019 | 2,045 | apache-2.0 | C++ | |

An Open Source Machine Learning Framework for Everyone | ||||||||||

Pytorch | 71,230 | 3,341 | 6,728 | 3 hours ago | 37 | May 08, 2023 | 12,813 | other | Python | |

Tensors and Dynamic neural networks in Python with strong GPU acceleration | ||||||||||

Keras | 59,447 | 578 | 3 hours ago | 80 | June 27, 2023 | 100 | apache-2.0 | Python | ||

Deep Learning for humans | ||||||||||

Faceswap | 47,163 | 2 days ago | 23 | gpl-3.0 | Python | |||||

Deepfakes Software For All | ||||||||||

Deepfacelab | 42,238 | a month ago | 536 | gpl-3.0 | Python | |||||

DeepFaceLab is the leading software for creating deepfakes. | ||||||||||

Annotated_deep_learning_paper_implementations | 36,223 | 1 | 11 days ago | 78 | September 24, 2022 | 27 | mit | Jupyter Notebook | ||

🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠 | ||||||||||

Spacy | 27,244 | 1,533 | 1,198 | 4 hours ago | 222 | July 07, 2023 | 94 | mit | Python | |

💫 Industrial-strength Natural Language Processing (NLP) in Python | ||||||||||

Data Science Ipython Notebooks | 25,242 | 3 months ago | 34 | other | Python | |||||

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. | ||||||||||

Handson Ml | 25,030 | 3 months ago | 139 | apache-2.0 | Jupyter Notebook | |||||

⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead. | ||||||||||

Netron | 24,093 | 4 | 69 | 12 hours ago | 587 | August 01, 2023 | 23 | mit | JavaScript | |

Visualizer for neural network, deep learning, and machine learning models |

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Codes and Projects for Machine Learning Course, University of Tabriz.

- download slides in Persian (pdf)

- Linear regression
- Gradient descent algorithm (video)
- Multi-variable linear regression
- Polynomial regression (video)
- Normal equation
- Locally weighted regression
- Probabilistic interpretation (video)
- Download slides in Persian (pdf)

- Python basics
- Creating vectors and matrices in
`numpy`

- Reading and writing data from/to files
- Matrix operations (video)
- Colon (:) operator
- Plotting using
`matplotlib`

(video) - Control structures in python
- Implementing linear regression cost function (video)

- Classification and logistic regression
- Probabilistic interpretation
- Logistic regression cost function
- Logistic regression and gradient descent
- Multi-class logistic regression
- Advanced optimization methods
- Download slides in Persian (pdf)

- Artificial Intelligence: A Modern Approach (3rd Edition), pages 725-727
- An Introduction to Statistical Learning: with Applications in R, pages 130-137
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, pages 119-128

- Overfitting and Regularization
- L2-Regularization (Ridge)
- L1-Regularization (Lasso)
- Regression with regularization
- Classification with regularization
- Download slides in Persian (pdf)

- Milti-class logistic regression
- Softmax classifier
- Training softmax classifier
- Geometric interpretation
- Non-linear classification
- Neural Networks (video: part 2)
- Training neural networks: Backpropagation
- Training neural networks: advanced optimization methods (video: part 3)
- Gradient checking
- Mini-batch gradient descent
- Download slides in Persian (pdf)

- Step by step Implementation of a multi-layer neural network in Python
- Backpropagation algorithm: Step by step example
- Activation functions (Sigmoid, Tanh, ReLU, PReLU, Maxout) and weight initialization methods
- How to solve problems using neural networks
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- Neurons and how they work

- Neural Networks and Deep Learning; Michael Nielsen: This book is a very good place to start learning about neural networks and deep learning.
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: For a more technical review of neural networks and deep learning, I recommend this book.

- Motivation: optimal decision boundary (video: part 1)
- Support vectors and margin
- Objective function formulation: primal and dual
- Non-linear classification: soft margin (video: part 2)
- Non-linear classification: kernel trick
- Multi-class SVM
- Download slides in persian (pdf)

- Supervised vs unsupervised learning
- Clustering
- K-Means clustering algorithm (demo)
- Determining number of clusters: Elbow method
- Postprocessing methods: Merge and Split clusters
- Bisectioning clustering
- Hierarchical clustering
- Application 1: Clustering digits
- Application 2: Image Compression
- Download slides in Persian (pdf)

- Introduction to PCA
- PCA implementation in python
- PCA Applications
- Singular Value Decomposition (SVD)
- Downloas slides in Persian (pdf)

- Intoduction to anomaly detection
- Some applications (security, manufacturing, fraud detection)
- Anoamly detection using probabilitic modelling
- Uni-variate normal distribution for anomaly detection
- Multi-variate normal distribution for anomaly detection
- Evaluation measures (TP, FP, TN, FN, Precision, Recall, F-score)
- Anomaly detection as one-class classification
- Classification vs anomaly detection
- Download slides in Persian (pdf)

- Introduction to recommender systems
- Collaborative filtering approach
- User-based collaborative filtering
- Item-based collaborative filtering
- Similarity measures (Pearson, Cosine, Euclidian)
- Cold start problem
- Singular value decomposition
- Content-based recommendation
- Cost function and minimization
- Download slides in Persian (pdf)

- Optimization: Convex Optimization, Stephan Boyd, Stanford
- Linear algebra: pdf
- Calculus: Khan Accademy
- Probability: Khan Accademy

- Regression and Gradient Descent
- Classification, Logistic Regression and Regularization
- Multi-Class Logistic Regression
- Neural Networks Training
- Neural Networks Implementing
- Clustering
- Dimensionallity Reduction and PCA
- Recommender Systems

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Python

Jupyter Notebook

Machine Learning

Neural Network

Neural

Classification

Slides

Recommender System

Pca

Linear Regression

Logistic Regression

Anomaly Detection

Persian

Gradient Descent

Supervised Learning

Backpropagation