'Deep Learning/course'에 해당되는 글 54건

  1. 2020.03.27 We decided to make most of our Deep Learning Teaching Materials freely available
  2. 2020.02.26 CS330 전과정 강의비디오가 유튜브에 업로드되었습니다. https://www.youtube.com/playlist?list=PLoROMvod
  3. 2020.02.10 Machine Learning From Scratch GitHub, by Erik Linder-Norén : https://github.com
  4. 2020.02.01 매우 좋은 AI 학습사이트! 무료에 1:1 튜터 형식으로 재미도 있고! 비록 영어 동영상이지만, 영어 자막 제공하고 있어서 이해하기도 쉽고
  5. 2020.01.07 YouTube에서 '머신러닝/딥러닝 실전 입문' 보기 윤인성씨 강의
  6. 2019.12.31 This course uses pytorch instead of tensorflow.[ [](https://t.co/lahLZspADM?amp
  7. 2019.12.30 Here's a small set of resources that I would like to share with the group. 1. J
  8. 2019.12.30 MIT lecture series on deep learning in 2019 MIT lecture series on deep learning:
  9. 2019.12.26 크리스 마스 선물이 속속 도착 중인데요. 이번은 Tf를 무료로 배울수 있는 10곳의 멋진 사이트를 소개 합니다 TF 2020 gogo!!
  10. 2019.12.16 Here's an ultimate data science starter kit: 1. Foundational Skills • Intro to
  11. 2019.12.01 스텐포드 딥러닝 수업이 정말 많네요. 이번학기 새롭게 업데이트된 자료와 코스도 많으니 추운날 방에서 보고 있으면 이번 겨울이 빠르게 지날것 같습니다. 모두 딥러닝/AI와 함께 따뜻한 겨..
  12. 2019.11.27 이번 ICCV 자료를 보다가, 예전 ICCV / CVPR 영상들을 보니까 좋은 워크샵 / 튜토리얼 영상들이 있더라구요! 컴퓨터 비전 공부하시는 분들께 도움되는 자료가 많은 것 같아 공유합니다 😇 ICCV 2019 ..
  13. 2019.11.05 A complete list of six video lectures in Generative adversarial network (GAN) is available on my YouTube Channel https://www.youtube.com/playlist?list=PLdxQ7SoCLQAMGgQAIAcyRevM8VvygTpCu You can subscribe my channel for more such videos https://www.yo..
  14. 2019.10.31 https://www.youtube.com/channel/UC0n76gicaarsN_Y9YShWwhw/videos 여기에 CVPR 영상들이 있는데, 19년도 튜토리얼은 안 보이네요.. https://www.youtube.com/channel/UC0n76gicaarsN_Y9YShWwhw/playlists 18년도 튜토리얼은 수록되어 ..
  15. 2019.10.31 혹시 ICCV 2019 영상 올라오나요? https://www.youtube.com/channel/UC0n76gicaarsN_Y9YShWwhw 여기에 튜토리얼이랑 메인컨퍼런스 구두발표는 아마 올라올거에요~
  16. 2019.10.30 Learn: 1. linear algebra well (e.g. matrix math) 2. calculus to an ok level (not advanced stuff) 3. prob. theory and stats to a good level 4. theoretical computer science basics 5. to code well in Python and ok in C++ Then read and implement ML papers..
  17. 2019.10.29 Some of the best courses available on the internet. 1. Data Science Professional Certificate from IBM. http://bit.ly/2M6o4ko 2. Best Machine Learning course anyone can find on the Internet via Andrew NG/Stanford. http://bit.ly/2J9uZWV 3. Persona..
  18. 2019.10.29 Deep Learning Drizzle Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!! GitHub by Marimuthu Kalimuthu: https://github.com/kmario23/deep-learning-drizzle We..
  19. 2019.10.29 Check out these Top #MachineLearning Youtube Videos Under 10 Minutes
  20. 2019.10.27 In this lesson, we will discuss how you can expand the NLP model in SpaCy. Text data is so wide therefore it is possible that the model is not present in Spacy to deal with it and in that case you can build your own rule and merge with the default SpaCy..
  21. 2019.10.27 #Download working file from the video description Processing Pipeline is an important step in feature extraction from text data. Learn how the pipeline works and how you can speed up the processing by disabling some blocks of the pipeline. NLP Tutorial ..
  22. 2019.10.27 Deep Learning with Keras A-Z: Part of our Data Science & Machine Learning Notes Series. We will now explore step by step tutor using Keras on MNIST. Go through the Analysis steps and try to understand the context. If some topics are too advanced they wi..
  23. 2019.10.25 버클리대학에서 진행한 Introduction to Deep Learning (STAT 157)강의의 강의 슬라이드와 강의 영상입니다. 한글화된 Dive into Deep Learning(https://ko.d2l.ai/)과 함께 보시면 처음 공부하시는 분들께 많은 도움..
  24. 2019.10.21 [괜찮은 듯] pytorch tutorial
  25. 2019.10.21 The #working #code is given in the video description of each video. You can #download the Jupyter notebook from #GitHub. Learn Complete Data Science with these 4 video series | Free Content of 28 Hours in 55 Lectures. 1. Python for Beginners https://ww..
  26. 2019.10.01 인공지능을 공부하면서 느꼈던 점들과 공부자료들을 공유하고 싶어 이렇게 글을 남깁니다.
  27. 2019.09.17 최근 번역서로 출판된 "신경망과 심층학습" 이라는 책에 대한 부가적인 자료가 있는 사이트를 알게되어 공유드립니다. 원서 제목은 Neural Networks and Deep Learning: A Textbook 으로 IBM Watson 연구소의 ..
  28. 2019.07.29 부스트코스]딥러닝 기초 강좌"요 라고 말할 수 있을 것 같습니다
  29. 2018.09.08 머신러닝 딥러닝 유튜브 강좌
  30. 2017.11.18 Top 10 Videos on Deep Learning in Python

We decided to make most of our Deep Learning Teaching Materials freely available online:
https://lme.tf.fau.de/teaching/free-deep-learning-resources/
I hope this is useful for some of you. Most of it is CC 4.0 BY, so it might also be useful to everybody who teaches her- or himself.

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CS330 전과정 강의비디오가 유튜브에 업로드되었습니다.

https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5

강의홈페이지는 아래와 같다고 유튜브영상 설명에 나와 있네요.

[http://cs330.stanford.edu/](https://www.youtube.com/redirect?q=http%3A%2F%2Fcs330.stanford.edu%2F&redir_token=SuMgss2EQcNktBe9UOk6rPcQZ5p8MTU4Mjc1Njg1MUAxNTgyNjcwNDUx&event=video_description&v=0rZtSwNOTQo)

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Machine Learning From Scratch

GitHub, by Erik Linder-Norén : https://github.com/eriklindernoren/ML-From-Scratch

#ArtificialIntelligence #DeepLearning #MachineLearning 

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Machine Learning From Scratch

About

Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.

The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.

Table of Contents

Installation

$ git clone https://github.com/eriklindernoren/ML-From-Scratch $ cd ML-From-Scratch $ python setup.py install

Examples

Polynomial Regression

$ python mlfromscratch/examples/polynomial_regression.py

Figure: Training progress of a regularized polynomial regression model fitting
temperature data measured in Linköping, Sweden 2016.

Classification With CNN

$ python mlfromscratch/examples/convolutional_neural_network.py +---------+ | ConvNet | +---------+ Input Shape: (1, 8, 8) +----------------------+------------+--------------+ | Layer Type | Parameters | Output Shape | +----------------------+------------+--------------+ | Conv2D | 160 | (16, 8, 8) | | Activation (ReLU) | 0 | (16, 8, 8) | | Dropout | 0 | (16, 8, 8) | | BatchNormalization | 2048 | (16, 8, 8) | | Conv2D | 4640 | (32, 8, 8) | | Activation (ReLU) | 0 | (32, 8, 8) | | Dropout | 0 | (32, 8, 8) | | BatchNormalization | 4096 | (32, 8, 8) | | Flatten | 0 | (2048,) | | Dense | 524544 | (256,) | | Activation (ReLU) | 0 | (256,) | | Dropout | 0 | (256,) | | BatchNormalization | 512 | (256,) | | Dense | 2570 | (10,) | | Activation (Softmax) | 0 | (10,) | +----------------------+------------+--------------+ Total Parameters: 538570 Training: 100% [------------------------------------------------------------------------] Time: 0:01:55 Accuracy: 0.987465181058

Figure: Classification of the digit dataset using CNN.

Density-Based Clustering

$ python mlfromscratch/examples/dbscan.py

Figure: Clustering of the moons dataset using DBSCAN.

Generating Handwritten Digits

$ python mlfromscratch/unsupervised_learning/generative_adversarial_network.py +-----------+ | Generator | +-----------+ Input Shape: (100,) +------------------------+------------+--------------+ | Layer Type | Parameters | Output Shape | +------------------------+------------+--------------+ | Dense | 25856 | (256,) | | Activation (LeakyReLU) | 0 | (256,) | | BatchNormalization | 512 | (256,) | | Dense | 131584 | (512,) | | Activation (LeakyReLU) | 0 | (512,) | | BatchNormalization | 1024 | (512,) | | Dense | 525312 | (1024,) | | Activation (LeakyReLU) | 0 | (1024,) | | BatchNormalization | 2048 | (1024,) | | Dense | 803600 | (784,) | | Activation (TanH) | 0 | (784,) | +------------------------+------------+--------------+ Total Parameters: 1489936 +---------------+ | Discriminator | +---------------+ Input Shape: (784,) +------------------------+------------+--------------+ | Layer Type | Parameters | Output Shape | +------------------------+------------+--------------+ | Dense | 401920 | (512,) | | Activation (LeakyReLU) | 0 | (512,) | | Dropout | 0 | (512,) | | Dense | 131328 | (256,) | | Activation (LeakyReLU) | 0 | (256,) | | Dropout | 0 | (256,) | | Dense | 514 | (2,) | | Activation (Softmax) | 0 | (2,) | +------------------------+------------+--------------+ Total Parameters: 533762

Figure: Training progress of a Generative Adversarial Network generating
handwritten digits.

Deep Reinforcement Learning

$ python mlfromscratch/examples/deep_q_network.py +----------------+ | Deep Q-Network | +----------------+ Input Shape: (4,) +-------------------+------------+--------------+ | Layer Type | Parameters | Output Shape | +-------------------+------------+--------------+ | Dense | 320 | (64,) | | Activation (ReLU) | 0 | (64,) | | Dense | 130 | (2,) | +-------------------+------------+--------------+ Total Parameters: 450

Figure: Deep Q-Network solution to the CartPole-v1 environment in OpenAI gym.

Image Reconstruction With RBM

$ python mlfromscratch/examples/restricted_boltzmann_machine.py

Figure: Shows how the network gets better during training at reconstructing
the digit 2 in the MNIST dataset.

Evolutionary Evolved Neural Network

$ python mlfromscratch/examples/neuroevolution.py +---------------+ | Model Summary | +---------------+ Input Shape: (64,) +----------------------+------------+--------------+ | Layer Type | Parameters | Output Shape | +----------------------+------------+--------------+ | Dense | 1040 | (16,) | | Activation (ReLU) | 0 | (16,) | | Dense | 170 | (10,) | | Activation (Softmax) | 0 | (10,) | +----------------------+------------+--------------+ Total Parameters: 1210 Population Size: 100 Generations: 3000 Mutation Rate: 0.01 [0 Best Individual - Fitness: 3.08301, Accuracy: 10.5%] [1 Best Individual - Fitness: 3.08746, Accuracy: 12.0%] ... [2999 Best Individual - Fitness: 94.08513, Accuracy: 98.5%] Test set accuracy: 96.7%

Figure: Classification of the digit dataset by a neural network which has
been evolutionary evolved.

Genetic Algorithm

$ python mlfromscratch/examples/genetic_algorithm.py +--------+ | GA | +--------+ Description: Implementation of a Genetic Algorithm which aims to produce the user specified target string. This implementation calculates each candidate's fitness based on the alphabetical distance between the candidate and the target. A candidate is selected as a parent with probabilities proportional to the candidate's fitness. Reproduction is implemented as a single-point crossover between pairs of parents. Mutation is done by randomly assigning new characters with uniform probability. Parameters ---------- Target String: 'Genetic Algorithm' Population Size: 100 Mutation Rate: 0.05 [0 Closest Candidate: 'CJqlJguPlqzvpoJmb', Fitness: 0.00] [1 Closest Candidate: 'MCxZxdr nlfiwwGEk', Fitness: 0.01] [2 Closest Candidate: 'MCxZxdm nlfiwwGcx', Fitness: 0.01] [3 Closest Candidate: 'SmdsAklMHn kBIwKn', Fitness: 0.01] [4 Closest Candidate: ' lotneaJOasWfu Z', Fitness: 0.01] ... [292 Closest Candidate: 'GeneticaAlgorithm', Fitness: 1.00] [293 Closest Candidate: 'GeneticaAlgorithm', Fitness: 1.00] [294 Answer: 'Genetic Algorithm']

Association Analysis

$ python mlfromscratch/examples/apriori.py +-------------+ | Apriori | +-------------+ Minimum Support: 0.25 Minimum Confidence: 0.8 Transactions: [1, 2, 3, 4] [1, 2, 4] [1, 2] [2, 3, 4] [2, 3] [3, 4] [2, 4] Frequent Itemsets: [1, 2, 3, 4, [1, 2], [1, 4], [2, 3], [2, 4], [3, 4], [1, 2, 4], [2, 3, 4]] Rules: 1 -> 2 (support: 0.43, confidence: 1.0) 4 -> 2 (support: 0.57, confidence: 0.8) [1, 4] -> 2 (support: 0.29, confidence: 1.0)

Implementations

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Deep Learning

Contact

If there's some implementation you would like to see here or if you're just feeling social, feel free to email me or connect with me on LinkedIn.

 

 

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매우 좋은 AI 학습사이트!

무료에 1:1 튜터 형식으로 재미도 있고!

비록 영어 동영상이지만, 영어 자막 제공하고 있어서

이해하기도 쉽고...

Advisor가 쟁쟁한데 특히 Yoshua Bengio 교수님이!

ㅎㄷㄷ

https://www.facebook.com/groups/KaggleKoreaOpenGroup/permalink/588324355233001/

https://korbit.ai/machinelearning

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https://www.youtube.com/playlist?list=PLBXuLgInP-5m_vn9ycXHRl7hlsd1huqmS

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This course uses pytorch instead of tensorflow.[
[](https://t.co/lahLZspADM?amp=1)https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z)

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Here's a small set of resources that I would like to share with the group.

1. Jupyter Notebooks (around 20) on pytorch - a set of tutorials: https://github.com/Tessellate-Imaging/Pytorch_Tutorial
Notebooks on
Crucial tensor functions
Data Loaders
Layers, activations, optimizers, initializers and losses
Classification examples and transfer learning

2. OpenSource Transfer learning library Monk: https://github.com/Tessellate-Imaging/monk_v1
Monk features
low-code
unified wrapper over major deep learning framework - keras, pytorch, gluoncv
syntax invariant wrapper
Enables
to create, manage and version control deep learning experiments
to compare experiments across training metrics
to quickly find best hyper-parameters
At present it only supports transfer learning, but we are working each day to incorporate
various object detection and segmentation algorithms
deployment pipelines to cloud and local platforms
acceleration libraries such as TensorRT
preprocessing and post processing libraries

3. Jupyter Notebooks blog series on Monk-Pytorch - Building classification applications: https://medium.com/@monkai.betasignup
American sign language classification
Leaf Disease Classification
Food Classification
Indoor Scene Classification
Fashion Classification

P.S - Inviting computer vision enthusiats to contribute to Monk tool.

Keep Learning!!!

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MIT lecture series on deep learning in 2019
MIT lecture series on deep learning:Basics: https://www.youtube.com/watch?v=O5xeyoRL95U&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf
MIT lecture series on deep learning: State of the Art:https://www.youtube.com/watch?v=53YvP6gdD7U&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf
MIT lecture series on deep learning: Introduction to Deep RL: https://www.youtube.com/watch?v=zR11FLZ-O9M&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf

Find The Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python Programming Resources
https://www.marktechpost.com/free-resources/

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크리스 마스 선물이 속속 도착 중인데요. 이번은 Tf를 무료로 배울수 있는 10곳의 멋진 사이트를 소개 합니다

TF 2020 gogo!!

https://analyticsindiamag.com/10-free-resources-to-learn-tensorflow-in-2020

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Here's an ultimate data science starter kit:

1. Foundational Skills

• Intro to Python - https://lnkd.in/grCsv8v

• Intro to R - https://lnkd.in/gKFiDZn

• Data Wrangling Pydata (90min) - https://lnkd.in/gEhF3-W

• EDA (20min video) - https://lnkd.in/gT8_RKh

• Stats/Prob (Khan Academy) - https://lnkd.in/gsyGpVu

2. Technical Skills

• Data Gathering- Why API Medium https://lnkd.in/gvahtsN

• Intro to SQL: https://lnkd.in/giWs-3N

• Complete SQL Bootcamp: https://lnkd.in/gsgf_fF

• Data Visualization - Medium https://lnkd.in/g3FSRgY

• Machine Learning A-Z: https://lnkd.in/gXqdBsQ

3. Business Skills

• Communication - Data Storytelling https://lnkd.in/gtiCSNT

• Business Analytics- Geckoboard https://lnkd.in/g2X-Xtp

4. Extra Skills

• Natural Language Processing - How to solve 90% of NLP https://lnkd.in/gh8bKe4

• Recommendation Systems - How Spotify Knows You So Well https://lnkd.in/gH2GQKu

• Time Series Analysis - Complete Time Series https://lnkd.in/gFZU2Rb

5. Practice

• Projects/Competitions - Kaggle Kernels https://www.kaggle.com/

• Problem Solving Challenges - HackerRank https://lnkd.in/g9Ps2cb

- - -

Hope these resources help!

If you want more free DS/ML resources, feel free to check out my site: www.ClaoudML.com

Happy learning! #datascience #machinelearning

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스텐포드 딥러닝 수업이 정말 많네요. 이번학기 새롭게 업데이트된 자료와 코스도 많으니 추운날 방에서  보고 있으면 이번 겨울이 빠르게 지날것 같습니다. 모두 딥러닝/AI와 함께 따뜻한 겨울 되기실!

Deep Learning

[http://web.stanford.edu/class/cs230/](http://web.stanford.edu/class/cs230/)

[ Natural Language Processing ]

CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)

[http://web.stanford.edu/class/cs124/](http://web.stanford.edu/class/cs124/)

CS 224N: Natural Language Processing with Deep Learning (LINGUIST 284)

[http://web.stanford.edu/class/cs224n/](http://web.stanford.edu/class/cs224n/)

CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288)

[http://web.stanford.edu/class/cs224u/](http://web.stanford.edu/class/cs224u/)

CS 276: Information Retrieval and Web Search (LINGUIST 286)

[http://web.stanford.edu/class/cs](http://web.stanford.edu/class/cs224u/)276

[ Computer Vision ]
CS 131: Computer Vision: Foundations and Applications

http://[cs131.stanford.edu](http://cs131.stanford.edu/)

CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning

[http://web.stanford.edu/class/cs205l/](http://web.stanford.edu/class/cs205l/)

CS 231N: Convolutional Neural Networks for Visual Recognition

[http://cs231n.stanford.edu/](http://cs231n.stanford.edu/)

CS 348K: Visual Computing Systems

[http://graphics.stanford.edu/courses/cs348v-18-winter/](http://graphics.stanford.edu/courses/cs348v-18-winter/)

[ Others ]

CS224W: Machine Learning with Graphs([Yong Dam Kim](https://www.facebook.com/yongdam.kim) )

[http://web.stanford.edu/class/cs224w/](http://web.stanford.edu/class/cs224w/)

 
CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236)

[https://canvas.stanford.edu/courses/51037](https://canvas.stanford.edu/courses/51037)

CS 236: Deep Generative Models

[https://deepgenerativemodels.github.io/](https://deepgenerativemodels.github.io/)

CS 228: Probabilistic Graphical Models: Principles and Techniques

[https://cs228.stanford.edu/](https://cs228.stanford.edu/)

CS 337: Al-Assisted Care (MED 277)

[http://cs337.stanford.edu/](http://cs337.stanford.edu/)

CS 229: Machine Learning (STATS 229)

[http://cs229.stanford.edu/](http://cs229.stanford.edu/)

CS 229A: Applied Machine Learning

[https://cs229a.stanford.edu](https://cs229a.stanford.edu/)

CS 234: Reinforcement Learning

http://[s234.stanford.edu](http://cs234.stanford.edu/)

CS 221: Artificial Intelligence: Principles and Techniques

[https://stanford-cs221.github.io/autumn2019/](https://stanford-cs221.github.io/autumn2019/)
https://m.facebook.com/groups/255834461424286?view=permalink&id=1051374671870257&sfnsn=mo
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이번 ICCV 자료를 보다가, 예전 ICCV / CVPR 영상들을 보니까 좋은 워크샵 / 튜토리얼 영상들이 있더라구요! 컴퓨터 비전 공부하시는 분들께 도움되는 자료가 많은 것 같아 공유합니다 😇

ICCV 2019 튜토리얼 / 워크샵 영상 모음

https://www.notion.so/torchvision/ICCV-2019-41b81ab87c20488899dfbf88e64af24b

 CVPR 2018 튜토리얼 / 워크샵 영상 모음

https://www.notion.so/torchvision/CVPR-2018-a5b487951048478483c7b89ed8fa4bc6

ICCV 2017 튜토리얼 / 워크샵 영상 모음

https://www.notion.so/torchvision/ICCV-2017-a838641aacaf46daa0c25989d1b47b3f

 CVPR 2017 튜토리얼 / 워크샵 영상 모음

https://www.notion.so/torchvision/CVPR-2017-a21294057f9941638d0f689fa8227b60
https://www.facebook.com/groups/datakorea/permalink/1349148285253979/?sfnsn=mo
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A complete list of six video lectures in Generative adversarial network (GAN) is available on my YouTube Channel

https://www.youtube.com/playlist?list=PLdxQ7SoCLQAMGgQAIAcyRevM8VvygTpCu

You can subscribe my channel for more such videos

https://www.youtube.com/user/kumarahlad/featured?sub_confirmation=1
https://www.facebook.com/groups/aiIDEASandINNOVATIONS/permalink/2858897464144049/?sfnsn=mo

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CVPR 2019 Tutorial, 컴퓨터 비전을 위한 심층강화학습
http://ivg.au.tsinghua.edu.cn/DRLCV/

튜토리얼 슬라이드
http://ivg.au.tsinghua.edu.cn/DRLCV/CVPR19_tutorial.pdf




https://www.youtube.com/channel/UC0n76gicaarsN_Y9YShWwhw/videos 여기에 CVPR 영상들이 있는데, 19년도 튜토리얼은 안 보이네요.. https://www.youtube.com/channel/UC0n76gicaarsN_Y9YShWwhw/playlists 18년도 튜토리얼은 수록되어 있는 것으로 보아 나중에 올라올지 모르겠습니다.

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혹시 ICCV 2019 영상 올라오나요? Jitendra Malik이 한 토크가 궁금해서 보고싶은데 못 찾겠네요 ㅠㅠ

https://www.youtube.com/channel/UC0n76gicaarsN_Y9YShWwhw

여기에 튜토리얼이랑 메인컨퍼런스 구두발표는 아마 올라올거에요~
https://www.facebook.com/groups/TensorFlowKR/permalink/1023962494611475/?sfnsn=mo

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Learn:

1. linear algebra well (e.g. matrix math)
2. calculus to an ok level (not advanced stuff)
3. prob. theory and stats to a good level
4. theoretical computer science basics
5. to code well in Python and ok in C++

Then read and implement ML papers and *play* with stuff! :-)

H / T : Shane Legg

#ArtificialIntelligence101
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Some of the best courses available on the internet.

1. Data Science Professional Certificate from IBM.
http://bit.ly/2M6o4ko

2. Best Machine Learning course anyone can find on the Internet via Andrew NG/Stanford.
http://bit.ly/2J9uZWV

3. Personal experience, best specialization for Deep Learning and getting advance in Machine Learning via DeeplearningAI.
https://bit.ly/2MjIfd8

4. One the highest rated Machine Learning specialization course with practical approaches and examples via University of Washington.
http://bit.ly/2SuRmKA

5. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning(4.7 Rating).
http://bit.ly/2M5fLWg
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Deep Learning Drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

GitHub by Marimuthu Kalimuthu: https://github.com/kmario23/deep-learning-drizzle

Webpage: https://deep-learning-drizzle.github.io

#artificialintelligence #deeplearning #machinelearning #reinforcementlearning
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Check out these Top  #MachineLearning Youtube Videos Under 10 Minutes
https://www.facebook.com/groups/aiIDEASandINNOVATIONS/permalink/2847112431989219/?sfnsn=mo
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In this lesson, we will discuss how you can expand the NLP model in SpaCy. Text data is so wide therefore it is possible that the model is not present in Spacy to deal with it and in that case you can build your own rule and merge with the default SpaCy model.

NLP Tutorial 7 - Combining NLP Models and Custom Rules in SpaCy Python Tutorial
https://youtu.be/yHmfOWryK4M
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#Download working file from the video description
Processing Pipeline is an important step in feature extraction from text data. Learn how the pipeline works and how you can speed up the processing by disabling some blocks of the pipeline.
NLP Tutorial 8 - Introduction to Processing Pipeline in SpaCy Python Tutorial | SpaCy for NLP
https://youtu.be/GI_2wmSK49I
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Deep Learning with Keras A-Z: Part of our Data Science & Machine Learning Notes Series. We will now explore step by step tutor using Keras on MNIST. Go through the Analysis steps and try to understand the context. If some topics are too advanced they will be covered soon in the upcoming Learning Notes Series.

Get your Notes here:[http://bit.ly/2MQVcgf](https://l.facebook.com/l.php?u=https%3A%2F%2Fbit.ly%2F2MQVcgf%3Ffbclid%3DIwAR0XWCuw5ALJ7YDyvPaED-6nFeM8e_upLhqUjmqS9TV6QGgfhZU1UUbd_Jc&h=AT2-kOSNPPdv1ICdwKZbI3_Je7jTjOfa1KYaQYdZsuDgIf-NxK_J9lEuYAfhWdyrkh72wv3uDMn65K7WHMrGPdH9aPjkXCWUP4mYIIwD3948AV5lZ6sSkUDY8R9iUj_e1R9P4CIfcapKfTwP79NT1qhrdZBXt2Z7kaC1AMnvb4tFLMGdZqWWRgWmKPVM4gTtuFThQEceooKOAVRqZh3DSUp2TAGIOv4kApQ824OQ3ftbW3HvOCrVSxU6TuiX65AXdFLXihO5MxThizVIsf6h-8HP1oOYFUvarHZjB2kteZNE9F7OTTKuy4oIfDkEPUABgkGnjlEoExlVfARkHSIb6U1POYGvL1DxQKvug-F3AwFmd0VW3V2kaXHPKJl_HQvIDhr_bYaP3j0ZAjxaj6eZcfiaolUvW8h7Je2PEy6VXhhYNrJePd9GMA0Tksh74rSV7_kDy_42tJ8d7VzVg969nLFqAldvkfJbY56RmOqLMfVNweGyrRYBx4MGjUTusjl5qJ4KO4GtxWZZg3W0iGF1pSNnVduzHePYWxLofwrinS94Ya4ZHrVMIv0EoeA4_lfSAqCyugMGBVNAgXLFfBgtIoYkqyCTWd8Z5EVXhK1Z2CdxsrhwQI6GdO7jLyCbXj0QkM0ydJk)
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버클리대학에서 진행한 Introduction to Deep Learning (STAT 157)강의의 강의 슬라이드와 강의 영상입니다. 한글화된 Dive into Deep Learning(https://ko.d2l.ai/)과 함께 보시면 처음 공부하시는 분들께 많은 도움이 될 듯 합니다.

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https://github.com/yunjey/pytorch-tutorial


This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

 

Table of Contents

1. Basics

2. Intermediate

3. Advanced

4. Utilities

 

Getting Started

$ git clone https://github.com/yunjey/pytorch-tutorial.git $ cd pytorch-tutorial/tutorials/PATH_TO_PROJECT $ python main.py

 

Dependencies

 

Author

Yunjey Choi/ @yunjey

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The #working #code is given in the video description of each video. You can #download the Jupyter notebook from #GitHub.
Learn Complete Data Science with these 4 video series | Free Content of 28 Hours in 55 Lectures.

1. Python for Beginners
https://www.youtube.com/watch?v=b42eTWkEIfA&list=PLc2rvfiptPSRmd4eWpRmzRIPebX3W9mju

2. Machine Learning for Beginners
https://www.youtube.com/watch?v=ZeM2tHtjGy4&list=PLc2rvfiptPSTvPFbNlT_TGRupzKKhJSIv

3. Feature Selection in Machine Learning
https://www.youtube.com/watch?v=kA4mD3y4aqA&list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH

4. Deep Learning with TensorFlow 2.0 and Keras
https://www.youtube.com/watch?v=nVvhkVLh60o&list=PLc2rvfiptPSR3iwFp1VHVJFK4yAMo0wuF

Please Like and Subscribe to show your support.
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https://www.facebook.com/groups/TensorFlowKR/permalink/997795710561487/

 

텐서플로우 코리아 님들 안녕하세요!

2017년 8월에 인공지능을 처음 입문하였는데, 어느덧 2년이 지나 학교를 졸업했네요. 잠시 백수 라이프를 즐기고 있는데, 인공지능을 공부하면서 느꼈던 점들과 공부자료들을 공유하고 싶어 이렇게 글을 남깁니다.

 

1. 주변의 변화

저보다 더 오래되신 분들도 많으시겠지만, 2년 전만 하더라도 주변에 딥러닝을 하는 사람들이 많이 없었습니다. 그런데 요즘에는 기계/ 재료/ 화학 등 여러 학과에서 딥러닝을 많이 하고 있고, 딥러닝/ 데이터 사이언티스트로 취직하기위한 허들도 조금씩 낮아지고 있는 것 같습니다. 당장 저희 학교/ 학과만 보더라도 다들 딥러닝 한다고(작년이랑 올해 캡스톤 디자인 수상한 팀이 다 딥러닝을 사용한 팀이네요 ㅋㅋ)하고 있고, 대학교 마지막 학기인 저의 친형은 재료 물성치를 예측하는 딥러닝 모델을 만드는 데 도와달라고 하네요 ㅋㅋ. 정말 재미있는 현상 같습니다.

 

2. 수학 vs 코딩

6개월 전까지만 하더라도 저는 수학 파였는데, 요즘은 균형 잡힌 인재가 더 필요한 것 같습니다. 또한, 코딩보다 수학을 위주로 공부하여 취직하고 싶다면 석사 또는 박사의 학력이 필요한 것 같습니다. 이 부분에 대해서 결정을 하기위해서는 사이언티스트로 취업을 할지 엔지니어로 취직을 할지 먼저 결정하는게 좋을 것 같네요. 일반적으로 사이언티스트는 수학을 좀 더 공부하면 좋을 것 같고, 엔지니어는 전산과목을 좀 더 공부하면 좋을 것 같습니다. 인공지능에는 많은 통계/수학적 지식이 필요합니다. 물론 몰라도 코딩은 할 수 있고, 이를 응용하여 사용할 수 있지만, 수학을 모르고는 그 한계가 분명합니다. 반면에 수학을 잘하더라도, 이를 구현하지 못 하면 소용 없음으로, 둘 중에 하나를 정하여 집중하되 다른 한 쪽도 기초는 공부하는게 좋을 것 같네요ㅎ

 

개인적으로 수학은 선형대수학, 수리통계학, 회귀분석은 수강하는 게 좋다고 생각하며,

전산 과목은(잘 모르지만) 자료구조, 알고리즘, 컴퓨터 구조 정도는 알고 있어야 한다고 생각합니다(물론 제가 다 들었다는 것은 아닙니다. ㅋㅋ)

 

3. 텐서플로우 VS 파이토치

저는 지금도 텐서플로우를 사용하여 코딩하고 있습니다. 텐서플로우는 빠르고, 오픈 소스가 많다는 장점이 있지만, GPU버전을 설치하기가 힘들며, 병렬처리를 하기 힘들다는 단점을 가지고 있습니다. 반면 파이토치는 병렬처리가 텐서플로우에 비해서는 정말 쉽고 코드를 짜는 것도 편하다는 장점이 있습니다. 개인적으로는 한 라이브러리를 깊이 있게 공부하고, 나머지 다른 라이브러리는 읽을 수 있는 정도만 공부하면 될 것 같습니다.

 

4. 컴퓨터 비전 vs 자연어 처리 vs 강화학습

아주 예민한 주제인데, 저의 생각은 자신이 하고 싶은 거로 하되 각 분야의 유명 모델 정도는 공부하자 입니다(너무 식상한가요? ㅎ). 여기는 학생분들도 많이 계시니까 취업을 기준으로 먼저 말하면 현재 기준 자연어 처리 > 컴퓨터 비전 > 강화학습 순으로 일자리가 많지만, 각 분야에서 두각을 드러낸다면 이는 문제 될 일이 없는 것 같습니다. GAN은 컴퓨터 비전에서 유명한 모델입니다. 하지만 데이터의 확률분포를 학습하기 위한 방법으로 자연어처리 분야의 음성 합성 부분에서 자주 등장하며, 최근 자연어 처리의 핫 모델 BERT는 컴퓨터 비전의 SELFIE라는 사전학습 방법으로 응용되어 제안되기도 했습니다. 이처럼 자신이 원하는 도메인을 잡아 공부하되, 다른 분야의 핫 모델들도 같이 공부한다면 이를 응용하여 좋은 결과를 낼 수도 있다고 생각합니다.

 

5. 구현에 관한 생각

우리는 머신러닝 모델을 공부할 때 깃허브에서 “Generative adversarial networks tensorflow”라고 검색하여 나온 코드를 사용하곤 합니다. 하지만 공부를 하면서 느꼈던 것은 가짜 구현이 정말 많다는 것 이였습니다. 실제로 저의 경우, Spectral Normalization GANs의 코드가 필요해 깃허브 스타가 좀 있는 분의 구현을 다운받아서 연구에 사용했습니다. 나중에 안 사실이지만 이는 가짜 구현이었고, FID와 Inception score를 찍어본 결과 논문에서 제시하는 값들에 한 참 못 미치는 결과가 나왔습니다. 이처럼 다른 사람의 코드를 가지고 오거나 직접 코드를 짜서 연구할 때는 철저한 검증 절차가 필수적이라고 생각합니다.

 

6. 머신러닝 및 딥러닝 강의 목록

최근에는 영어만 잘한다면 들을 수 있는 명강의들이 정말 많습니다. 영어를 잘 못 하는 저는 눈물만 나지만 ㅠㅠ, 주제별로 괜찮다 싶은 강의들을 모아봤습니다.

 

모두를 위한 딥러닝 시즌 2

(제작해주신 모든 분들 정말 감사합니다. 딥러닝 입문 한국어 강좌들 중 원톱!)

https://www.youtube.com/watch?v=7eldOrjQVi0&list=PLQ28Nx3M4Jrguyuwg4xe9d9t2XE639e5C

 

머신러닝을 위한 Python 워밍업(한국어)

https://www.edwith.org/aipython

 

머신러닝을 위한 선형대수(한국어)

https://www.edwith.org/linearalgebra4ai

 

데이터 구조 및 분석(문일철 교수님)

https://kaist.edwith.org/datastructure-2019s

 

인공지능 및 기계학습 개론(문일철 교수님)

https://kaist.edwith.org/machinelearning1_17

 

영상이해를 위한 최적화 기법(김창익 교수님)

https://kaist.edwith.org/optimization2017

 

<영어>

UC Berkley 인공지능 강좌

https://www.youtube.com/watch?v=Va8WWRfw7Og&list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW

 

CS231n

https://www.youtube.com/results?search_query=cs213n

 

Toronto Machine Learning course

https://www.youtube.com/watch?v=FvAibtlARQ8&list=PL-Mfq5QS-s8iS9XqKuApPE1TSlnZblFHF

 

CS224N(NLP 강좌)

https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z

 

Deep Learning for Natural Language Processing(Oxford, DeepMind)

https://www.youtube.com/watch?v=RP3tZFcC2e8&list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm

 

Advanced Deep Learning, Reinforcement Learning(DeepMind)

https://www.youtube.com/watch?v=iOh7QUZGyiU&list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs&index=1

 

다들 즐거운 하루되세요 ㅎㅎ!

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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https://www.facebook.com/groups/TensorFlowKR/permalink/987881208219604/?sfnsn=mo

최근 번역서로 출판된 "신경망과 심층학습" 이라는 책에 대한 부가적인 자료가 있는 사이트를 알게되어 공유드립니다.

원서 제목은 Neural Networks and Deep Learning: A Textbook 으로 IBM Watson 연구소의 Distinguished 연구자이신 Charu C. Aggarwal 님이 저술한 책입니다.

책 내용 자체도 Amazon 리뷰를 보면 꽤 좋다는것을 알 수 있는데요, 이론과 실습 두 가지를 적절하게 설명하는 책으로 판단됩니다.

우선 Charu 님이 운영하는 유투브 채널을 가 보면, 일부 챕터에 대한 비디오 강의도 존재합니다 (27개의 비디오).

그리고, Charu 님이 운영하는 홈페이지가 있는것을 알게 되었는데요, 이 사이트에 가면 각 챕터별 내용을 설명하는 PDF 슬라이드가 함께 제공됩니다. 추가적으로, PDF 슬라이드의 LaTex 버전과 자료에 삽입된 그림을 모두 LaTex 파일(*.dvi) 로도 제공합니다.

본 책으로 공부를 하시는분들께 도움이 되었으면 싶은 생각이 들어서 공유드리오니 참고 되면 좋을것 같습니다 :)

유투브: https://www.youtube.com/playlist?list=PLLo1RD8Vbbb_6gCyqxG_qzCLOj9EKubw7
홈페이지: http://www.charuaggarwal.net/neural.htm
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https://www.facebook.com/groups/modulabs/permalink/2359449094120260/?sfnsn=mo

누군가 "딥러닝 시작 할때 가장 좋은 강의는 무엇인가요?"라는 질문을 하시면, 전 "스탠포드 대학의 #CS231N 강좌인것 같아요" 라고 대답했습니다. 그러나 강의가 영어로 되어 있어서 조금 불편했던 분들이 있으실 겁니다. (이재원 (Jaewon Lee) 님이 CS231N 전체 강의에 한글자막 작업을 하신 것을 알고 있습니다. ^^ 정말 대단한 분 ^^ ) 

앞으로  전 위와 같은 질문을 받을 경우 #edwith 의 "[부스트코스]딥러닝 기초 강좌"요 라고 말할 수 있을 것 같습니다 😁

전체가 우리말로 되어 있으며 TensorFlow와 Pytorch 둘다 공부할 수 있도록 강좌가 나뉘어져 있습니다. 저의경우 이런 강좌는 아직까지 본적이 없습니다 😍

또한 코세라와 유다시티의 딥러닝 강좌처럼 직접 실습을 헤보고 제출도 할 수 있는 Jupyter notebook 기반의 프로젝트 코스가 있습니다. 😆
이 작업에 모두의연구소도 함께 참여했습니다.

이제부터 "딥러닝 기초과목은 무엇으로 공부하면 될까요?" 라는 질문을 받으면 "edwith의 부스트코로 시작하세요~" 라고 말하려고 합니다~^^

이 작업을 함께 해주신 커넥트재단 장지수 님과 이효은 (Annah Lee) 님께 너무 감사드립니다 🙇‍♂️ 그외 모두를위한 딥러닝 시즌2를 위하여 재능 기부해주신 분들 너무 감사드립니다. 강의 잘 보겠습니다. 강의가 이렇게 만들어질 수 있다는게 너무 멋진 일이란 생각이드네요.

그리고 추가로 이 강좌의 프로젝트 작성에 힘써 주신 모두연 소속 연구원 여러분 ~ 너무 수고하셨습니다. (박창대, 이재영, 오진우 (Jinwoo Matthew Oh) , 이일구 (Il Gu Yi))  함께해서 즐거웠습니다~~^^

비가 부슬부슬 내리는 금요일 입니다.

불금 보내세요~~^^
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Machine Learning all resources in one place for you to get started. This playlist ( https://www.youtube.com/playlist?list=PLqrmzsjOpq5iBQEtgHSeF4WaVzII_ycBn ) contains of:
1. Complete Machine learning video lessons
2. Complete Mathematics video lessons
3. Some advance topic on machine learning
4. Some interesting project ideas.
5. Coming Soon with more videos and topics. Subscribe to keep yourself warm with ML: ( https://www.youtube.com/channel/UCq8JbYayUHvKvjimPV0TCqQ?sub_confirmation=1 )
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Top 10 Videos on Deep Learning in Python
https://www.kdnuggets.com/2017/11/top-10-videos-deep-learning-python.html?utm_content=buffer765d8&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer


This ‘Top 10’ list has been created on the basis of best content, and not exactly the number of views. To help you choose an appropriate framework, we first start with a video that compares few of the popular Python DL libraries. I have included the highlights and my views on the pros and cons of each of these 10 items, so you can choose one that best suits your needs. I have saved the best for last- the most comprehensive yet free YouTube course on DL ☺. Let’s begin!

1. Overview: Deep Learning Frameworks compared (96K views) - 5 minutes

Before I actually list the best DL in Python videos, it is important that one understands the differences between the 5 most popular deep learning frameworks -SciKit Learn, TensorFlow, Theano, Keras, and Caffe. This 5 minute video by Siraj Raval gives you the best possible comparison between the pros and cons of each framework and even presents the structure of code samples to help you better decide. Start with this.

2. Playlist: TensorFlow tutorial by Sentdex (114 K views) - 4.5 hours

This playlist of 14 videos by Sentdex is the most well-organized, thoroughly explained ,concise yet easy to follow tutorial on Deep Learning in Python. It includes TensorFlow implementation of a Recurrent Neural Network and Convolutional Neural Network with the MNIST dataset.

3. Individual tutorial: TensorFlow tutorial 02: Convolutional Neural Network (69.7 K views) - 36 minutes

This tutorial by Magnus Pedersen on the YouTube channel Hvass Laboratories, is worth its weight in gold- excellent comments in the code; plus, the instructor speaks without interruption. Watch this video to understand scripts in TensorFlow. Thank me later☺

4. Overview : How to predict stock prices easily (210 K views) - 9 minutes

In this video, Siraj Raval uses a special type of recurrent neural network called an LSTM network. He uses the Keras library with a TensorFlow backend. He explains the reason behind using recurrent nets for time series data and later, uses it to predict the daily closing price of the S&P 500 based on training data for 16 years. The link to the Github code is given in its description box.

5. Tutorial: Introduction to Deep Learning with Python and the Theano library (201 K views) - 52 minutes

If you want a talk on Python with the Theano library in under an hour, targeted towards beginners, then you can refer to this talk by Alec Radford. Unlike most other talks on this topic, this one compares the features of an ‘old’ net versus a ‘modern’ net, ie nets prior to 2000 versus nets post-2012.

6. Playlist: PyTorch Zero to All (3 K views) - 2 hours 15 minutes

In this series of 11 videos, Sung Kim teaches you PyTorch from the ground up. A highlight of this series is Lecture 10, where he teaches you to build a basic CNN with detailed emphasis of understanding the concept of CNN’s using his detailed diagrams.

7. Individual tutorial: TensorFlow tutorial (43.9 K views) - 49 minutes

This single tutorial by Edureka implements DL using TensorFlow. It is a very good tutorial for beginners in TensorFlow. It teaches TensorFlow basics and data structures. It also includes a usecase for using DL as a Naval Mine identifier- to identify whether an underwater obstacle is a rock or a mine.

8. Playlist: Deep Learning with Python (1.8K views) - 83 minutes

The YouTube channel ‘Machine Learning TV‘ has published a series of 15 videos totaling 83 minutes using Theano and Keras to use DL for automatic image captioning. It shows you how to train your first deep neural net for classifying digits from the MNIST dataset. It also has a good explanation on loading and reusing pre-trained models in Theano.

9. Playlist: Deep Learning with Keras- Python (30.3 K views) - 85 minutes

The YouTube channel ‘The SemiColon‘ has published a series of 11 videos on tutorials using Theano and Keras to implement a chatbot using DL. It includes explanations on Convolutional Neural Network, Recurrent Neural Network in Theano with Keras, Neural Networks and Backpropagation in scikit-learn library on the handwriting recognition (MNIST) dataset.

The speaking is punctuated by ‘umms’ and ‘ahhs’, but there is a good explanation on Word2Vec used to build chatbots.

10. Free online course: Deep Learning by Andrew Ng (Full course) (28 K views) - 4 week course

As in my previous Top 10 videos post on ML in Finance, I have saved the best for last☺ .If you want to learn Deep Learning as an online course from arguably the most famous ML instructor- Andrew Ng, then this playlist is for you. Intended as a 4-week course covering 98 videos, this course teaches you DL, Neural Networks, binary classification, derivatives, gradient descent, activation function, backpropagation, regularization, RMSprop, tuning, dropout, training and testing on different distributions, among others, using Python code in a Jupyter notebook.

 
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