This repository for noting some resources that may help me in studying ML/DL and applying AI/ML/DL into real world

Basic Courses

[1] CME 323: Distributed Algorithms and Optimization

[2] CME342 - Parallel Methods in Numerical Analysis

[3] Parallel Computer Architecture and Programming (CMU 15-418/618)

ML/DL - Lecture notes

[1] Deep learning for Vision (instructor Yannis Avrithis) - (Univ of Rennes1)

[2] CS231n: Convolutional Neural Networks for Visual Recognition (instructor Fei-Fei Li) - (Stanford Univ)

[3] DEep Learning: Do-It-Yourself (Lecture 1->9) - (instructor Marc Lelarge, Andrei Bursuc, Alexandre Defossesz, Timthee Lacroix, Alexandre Sablayrolles, Pierre Stock, Neil Zeghidour)

[4] EE-559 Deep Learning - 2018 (lecture 4->10) - (instructor Francois Fleuret) (idiap)

[5] CS-498 Introduction to Deep learning (October 2 -> Novermber 8, Novermber 29 -> December 6) (instructor Svetlana Lazebnik) - (Univ illinois)

[6] UVA - Deep learning course (lecture 4 -> 6, 8 -> 14) (Univ of Amsterdam)

[7] Deep learning (lecture 4 -> end) (univ Paris-Saclay)

[8] CSC-2523 Deep Learning in Computer Vision 2016 (instructor Sanja Fidler) - (Univ of Toronto)

[9] Topics Course on Deep Learning 2016 (1st Part) - (Joan Bruna) (UC Berkeley)

[10] COS 598B 2018 Advanced Topics in Computer Science: Visual Recognition (tab outline) (instructor Prof. Olga Russakovsky) (Princeton)

[11] COS 429 - Computer Vision (Outline adn Lecture Notes tab) - (instructor Prof. Olga Russakovsky) - (Princeton)

[12] Computer Science 598F Advanced Topics in Computer Science: Deep Learning for Graphics and Vision (Princeton)

[13] COS 429 - Computer Vision (outline and lecture Notes) - (Princeton)

[14] EE-559 – EPFL – Deep Learning (Spring 2019) (instructor Francois Fleuret) (Ecole Polytecnique Federate De Lausanne)

[15] CS 598/ IE 534, Fall 2018 (instructor Justin Sirignano) - (Univ os illinois)

[16] Machine Learning (instructor Bastian Leibe) - (RWTH AACHEN)

[17] CP8309/CP8315: Deep Learning in Computer Vision (instructor Kosta Derpanis) - (Univ Ryerson)

[18] Machine Learning in Three month (Video)

[19] Deep Learning for Visual Computing (TU WIEN) https://cvl.tuwien.ac.at/course/dlvc/ https://github.com/cpra/dlvc2018

[20] 6.S191: Introduction to Deep Learning (part 1(2, 3, 4), part 2(2), part 3(2, 3) (MIT)

[21] CS 9840 FALL 2015 Machine Learning and Computer Vision (Univ Western Ontario)

[22] Introduction to Computer Vision (UDACITY)

[23] Advanced Topics in Machine Learning MSc - Spring Semester 422828, Lectures and exercises, 5.0 ECTS (instructor (Paolo Favaro) - (Univ of Ben)

[24] (Univ Freiburg)

[25] (Univ of Cambridge)

[26] CS 598 LAZ: Cutting-Edge Trends in Deep Learning and Recognition (instructor Svetlana Lazebnik) - (Univ of illinois)

[27] Convolutional Neural Networks on Graphs (Video)

[28] GAME THEORY AND APPLICATIONS (M2 Course GTA) - (Lecture slides and exercises) - (Patrick maille)

[29] Speed up TensorFlow Inference on GPUs with TensorRT

[30] Optimizing TensorFlow Serving performance with NVIDIA TensorRT

[31] TensorRT for TensorFlow (video)

[32] CS 20: Tensorflow for Deep Learning Research

Specific topics

Image Super-Resolution

[1] Single Image Super-Resolution A collection of high-impact and state-of-the-art SR methods

[2] A collection of Single Image Super-Resolution Methods

[3] Super-Resolution via Deep Learning

Face Detection

[1] MTCNN

[2] SSD

[3] single short learning

Object Detection

[1] Object Detection with Deep Learning: A Review

[2] A Review: Object Detection using Deep Learning

[3] Deep Learning for Generic Object Detection: A Survey

Moving Object Tracking and/or Detecting

[1] New Trends on Moving Object Detection in Video Images Captured by a moving Camera: A Survey

[2] Deep Learning for Moving Object Detection and Tracking from a Single Camera in Unmanned Aerial Vehicles (UAVs)

[3] Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes

Image Semantic Segmentation

[1] awesome semantic segmentatation

Applications

Self-Driving Vehicle

[1] MIT 6.S094: Deep Learning for Self-Driving Cars (instructor Lex Fridman) https://deeplearning.mit.edu/ https://github.com/dattv/mit-deep-learning

Medical Imaging

[1] achine Learning for medical imaging (Video)

[2] An overview of deep learning in medical imaging focusing on MRI

[3] Deep Learning for Medical Image Processing: Overview, Challenges and Future

[4] Medical Imaging with Deep Learning (MIDL 2018) Conference: Exploring Rejected Extended Abstracts

[5] Deep Learning in Medical Image Analysis

[6] Overview of deep learning in medical imaging

[7] NiftyNet: a deep-learning platform for medical imaging

[8] Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

[9] Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods

[10] An overview of deep learning in medical imaging focusing on MRI

[11] Clara AI Platform (NVIDIA)

[12] CAP5516-Medical Image Computing (SPRING 2019)

Smart Farm

[1] Deep Learning in Agriculture

[2] The Future of Farming with AI: Truly Organic at Scale (Video) - (AI with Quantum Computing, DWAVE)

[3] Deep Learning for Smart Argriculture

[4] Deep Learning in Agriculture: A Survey

[5] Machine Learning in Agriculture: A Review

[6] Big Data in Smart Farming - A review

[7] Smart drones and deep learning deliver low-cost precision agriculture for Aussie farmers

[8] Smart Farm 2.0 System Architecture

[9] How machine learning is gradually changing modern agricultural practices

[10] A hybrid machine learning approach to automatic plant phenotyping for smart agriculture

[11] DETECTION & PREDICTION OF PESTS/DISEASES USING DEEP LEARNING

[12] Smart farming: How IoT, robotics, and AI are tackling one of the biggest problems of the century

Smart Aquaculture

[1] DeepFish: Accurate underwater live fish recognition with a deep architecture (98,64% of accuracy)

Game

[1] Playing Atari with Deep Reinforcement Learning

[2] Atari - Solving Games with AI 🤖 (Part 1: Reinforcement Learning)

[3] Atari - Solving Games with AI🤖 (Part 2: Neuroevolution)

[4] Atari Project

[5] AlphaGo

ML/DL Detect Cracks on Surfaces

[1] Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique

How can AI help speed up airport security?

Financial services

ML/DL- Code References

[1] Browse state-of-the-art

[2] semantic-Segmentation

ML/DL- Book References

[1] Dive into Deep Learning

[2] Learning Python

[3] Python Machine Learning (Sebastian RAschka)utm_source=dzone&utm_medium=referral&utm_campaign=outreach

[4] Advanced Machine Learning with Python (john Hearty)

[5] Think Stats – Probability and Statistics for Programmers ( Allan B. Downey)

[6] Understanding Machine Learning: From Theory to Algorithms (Shai Shalev - Shwartz

[7] Deep Learning

[8] MIT Deep Learning Book (beautiful and flawless PDF version)

[9] Computer Vision Book