- Courses
- Coursera
- fast.ai
- fast.ai
- Deep Learning Part 1: Practical Deep Learning for Coders
- Deep Learning Part 1: 2018 Edition (v2)
- Lesson 1
- Deep Learning Part 1: 2019 Edition (v3)
- Lesson 1 - Image Recognition
- Lesson 2 - Computer Vision: Deeper Applications
- Lesson 3 - Multi-label, Segmentation, Image Regression, and More
- Lesson 4 - NLP, Tabular, and Collaborative Filtering
- Lesson 5 - Foundations of Neural Networks
- Lesson 6 - Foundations of Convolutional Neural Networks
- Lesson 7 - ResNets, U-Nets, GANs and RNNs
- Deep Learning Part 1: 2018 Edition (v2)
- Deep Learning Part 2: Cutting Edge Deep Learning for Coders
- Deep Learning Part 2: 2017 Edition (v1)
- Deep Learning Part 2: 2018 Edition (v2)
- Lesson 8 - Object Detection
- Lesson 9 - Single Shot Multibox Detector (SSD)
- Lesson 10 - Transfer Learning for NLP and NLP Classification
- Lesson 11 - Neural Translation; Multi-modal Learning
- Lesson 12 - DarkNet; Generative Adversarial Networks (GANs)
- Lesson 13 - Image Enhancement; Style Transfer; Data Ethics
- Lesson 14 - Super Resolution; Image Segmentation with U-Net
- Machine Learning: Intro to Machine Learning for Coders
- Machine Learning: 2017 Edition
- Lesson 1 - Introduction to Random Forests
- Lesson 2 - Random Forest Deep Dive
- Lesson 3 - Feature Engineering
- Lesson 4 - Random Forest Interpretation
- Lesson 5 - Train vs Test
- Lesson 6 - What is Machine Learning and Why Do We Use It
- Lesson 7 - Decision Trees Ensemble
- Lesson 8 - Basic Neural Networks
- Lesson 9 - SGD; Neural Network Training; Broadcasting
- Lesson 10 - Logistic Regression; NLP; Naive Bayes
- Lesson 11 - Structured and Time-Series Data
- Lesson 12 - Entity Embeddings; Data Science and Ethics
- Machine Learning: 2017 Edition
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