- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- Review: Xception — With Depthwise Separable Convolution, Better Than Inception-v3 (Image Classification)
- Semantic Segmentation — U-Net
- Deep Learning Paper Implementations: Spatial Transformer Networks - Part I
- Deep Learning Paper Implementations: Spatial Transformer Networks - Part II
- Real-time Object Detection with YOLO, YOLOv2 and now YOLOv3
- CS231n: Convolutional Neural Networks for Visual Recognition Stanford University
- The Softmax function and its derivative
- The Chain Rule of Calculus
- Understanding gradient descent
- Logistic regression
- Convolution arithmetic
- An Introduction to different Types of Convolutions in Deep Learning
- A Basic Introduction to Separable Convolutions
- A Recipe for Training Neural Networks
- Initializing neural networks
- Weight Initialization in Neural Networks: A Journey From the Basics to Kaiming
- Differences between L1 and L2 as Loss Function and Regularization
- Understanding LSTM Networks
- Sequence Modeling: Recurrent and Recursive Nets
- Deep Double Descent
- Metrics to Evaluate your Semantic Segmentation Model
- Quantization Algorithms
- Knowledge Distillation
- Yes you should understand backprop
- The Backpropagation Algorithm
- Backpropagation for a Linear Layer
- Derivatives, Backpropagation, and Vectorization
- Efficient BackProp
- Calculus on Computational Graphs: Backpropagation
- How the backpropagation algorithm works
- Understanding the backward pass through Batch Normalization Layer
- What is backpropagation
- Backpropagation calculus
- Writing tests for the Albumentations library with pytest
- Albumentations library fast image augmentation library
- Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
- Introduction to Cyclical Learning Rates
- Cyclical Learning Rates — The ultimate guide for setting learning rates for Neural Networks
- Adaptive - and Cyclical Learning Rates using PyTorch
- Cyclical Learning Rates for Training Neural Networks
- How to Train Your ResNet
- MuZero: The Walkthrough (Part 1/3)
- 9 Distance Measures in Data Science
- Open Machine Learning Course
- Introduction to Machine Learning Algorithms: Linear Regression
- Introduction to Machine Learning Algorithms: Logistic Regression
- Support Vector Machine — Introduction to Machine Learning Algorithms
- Support Vector Machines — Soft Margin Formulation and Kernel Trick
- Support Vector Machines: A Visual Explanation with Sample Python Code
- Singular Value Decomposition (SVD)
- PCA Whitening
- A tutorial on Principal Components Analysis
- PCA with scikit
- Leveraging Early Sensor Fusion for Safer Autonomous Vehicles by Lyft
- Frustum PointNets for 3D Object Detection from RGB-D Data
- Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection
- Understanding Move Semantics and Perfect Forwarding: Part 1
- Understanding Move Semantics and Perfect Forwarding: Part 2
- Understanding Move Semantics and Perfect Forwarding: Part 3
- The Curiously Recurring Template Pattern (CRTP)
- A Recap on User Defined Literals
- Using and Mastering Cookiecutter
- Pattern Matching
- Click package
- Writing tests for the Albumentations library with pytest
- Guido van Rossum: Stanford Seminar - Optional Static Typing for Python
- Greg Price - Clearer Code at Scale: Static Types at Zulip and Dropbox - PyCon 2018
- Carl Meyer - Type-checked Python in the real world - PyCon 2018
- Instagram MonkeyType
- The definitive guide on how to use static, class or abstract methods in Python
- Brett Slatkin: Fan-in and Fan-out: The crucial components of concurrency - PyCon 2014 (asyncio)
- Tulip: Async I/O for Python 3 by Guido van Rossum
- Python Modules and Packages – An Introduction
- Python 3's f-Strings: An Improved String Formatting Syntax (Guide)
- Object-Oriented Programming in Python vs Java
- Defining Main Functions in Python
- Python Metaclasses
- Implementing an Interface in Python
- Python import: Advanced Techniques and Tips
- Absolute vs Relative Imports in Python
- Data Classes in Python 3.7+ (Guide)
- Raymond Hettinger - Dataclasses: The code generator to end all code generators - PyCon 2018
- Explaining Homogeneous Coordinates & Projective Geometry
- Detectors and Descriptors (lecture by Stanford Vision Lab)
- Color balancing imagery with histogram matching
- How to Use t-SNE Effectively
- Visualizing MNIST: An Exploration of Dimensionality Reduction
- Introduction To Feature Detection And Matching
- Introduction to Harris Corner Detector
- Introduction to SIFT( Scale Invariant Feature Transform)
- Introduction to SURF (Speeded-Up Robust Features)
- Introduction to FAST (Features from Accelerated Segment Test)
- Introduction to BRIEF(Binary Robust Independent Elementary Features)
- Introduction to ORB (Oriented FAST and Rotated BRIEF)