A Step-by-Step Introduction to the Basic Object Detection Algorithms

Table of Contents
A Simple Way of Solving an Object Detection Task (using Deep Learning)
Understanding Region-Based Convolutional Neural Networks
1. Intuition of RCNN
2. Problems with RCNN
Understanding Fast RCNN
1. Intuition of Fast RCNN
2. Problems with Fast RCNN
Understanding Faster RCNN
1. Intuition of Faster RCNN
2. Problems with Faster RCNN
Summary of the Algorithms covered

Edge Detection in Opencv 4.0, A 15 Minutes Tutorial

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https://blog.sicara.com/opencv-edge-detection-tutorial-7c3303f10788

Live Object Detection – Towards Data Science

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https://towardsdatascience.com/live-object-detection-26cd50cceffd

My Top 5 Recommended Places to Learn about Deep Learning and Machine Learning

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https://medium.com/datadriveninvestor/my-top-5-recommended-places-to-learn-about-deep-learning-and-machine-learning-f95153a847e

How to become an expert in NLP in 2019 (1)

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https://medium.com/@kushajreal/how-to-become-an-expert-in-nlp-in-2019-1-945f4e9073c0

Fun with Snapchat's Gender Swapping Filter

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On Choosing a Deep Reinforcement Learning Library

As Deep Reinforcement Learning is becoming one of the most hyped strategies to achieve AGI (aka Artificial General Intelligence) more and more libraries are developed.
And choosing the best for your needs can be a daunting task…


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https://medium.com/data-from-the-trenches/choosing-a-deep-reinforcement-learning-library-890fb0307092

DeepMind & Google Graph Matching Network Outperforms GNN

DeepMind and Google researchers have proposed a powerful new graph matching network (GMN) model for the retrieval and matching of graph structured objects. GMN uses similarity learning for graph structured objects and outperforms graph neural network (GNN) models on graph similarity learning (GSL) tasks.

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https://medium.com/syncedreview/deepmind-google-graph-matching-network-outperforms-gnn-c277d3ca6f75

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https://towardsdatascience.com/the-quest-of-higher-accuracy-for-cnn-models-42df5d731faf

Algorithms online Course from PRINCETON UNIVERSITY

About this Course
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

All the features of this course are available for free. It does not offer a certificate upon completion

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https://www.coursera.org/learn/algorithms-part1?ranMID=40328&ranEAID=SAyYsTvLiGQ&ranSiteID=SAyYsTvLiGQ-ayH4CcL5jMTprP4tidKo4g&siteID=SAyYsTvLiGQ-ayH4CcL5jMTprP4tidKo4g&utm_content=10&utm_medium=partners&utm_source=linkshare&utm_campaign=SAyYsTvLiGQ

Diving into Deep Convolutional Semantic Segmentation Networks and Deeplab_V3

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https://sthalles.github.io/deep_segmentation_network/

Not just another GAN paper — SAGAN – Towards Data Science

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https://towardsdatascience.com/not-just-another-gan-paper-sagan-96e649f01a6b

Deep learning lecture

Deep Learning lecture
The full deck of (600+) slides, by Gilles Louppe:


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https://glouppe.github.io/info8010-deep-learning/pdf/lec-all.pdf

Stanford Machine Learning

Content
01 and 02: Introduction, Regression Analysis and Gradient Descent

03: Linear Algebra - review

04: Linear Regression with Multiple Variables

05: Octave[incomplete]

06: Logistic Regression

07: Regularization

08: Neural Networks - Representation

09: Neural Networks - Learning

10: Advice for applying machine learning techniques

11: Machine Learning System Design

12: Support Vector Machines

13: Clustering

14: Dimensionality Reduction

15: Anomaly Detection

16: Recommender Systems

17: Large Scale Machine Learning

18: Application Example - Photo OCR

19: Course Summary

http://www.holehouse.org/mlclass/

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