Top 8 Sources For Machine Learning and Analytics Datasets
1. Kaggle datasets
2. Amazon datasets
3. UCI Machine Learning Repository
4. Google Datasets Search Engine
5. Microsoft Research Open Data
6. Awesome Public Datasets Collection

... https://t.co/X3e7JEqvTf

Reinforcement Learning with Python

A Deep Introduction and a programming approach to Reinforcement Learning Using Python.

Hot topic for project, thesis and research -- Machine Learning

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

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Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data
https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463

FOUNDATIONS OF MACHINE LEARNING

This book is a general introduction to machine learning that can serve as a textbook for students and researchers in the field.

It covers fundamental modern topics in machine learning while providing theoretical basis and conceptual tools needed for the discussion and justification algorithm.
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@DeepLearning_AI

This book covers:

Chapter 1, Getting Started with OpenCV.

Chapter 2, An Introduction to the Basics of OpenCV.

Chapter 3, Learning the Graphical User Interface and Basic Filtering.

Chapter 4, Delving into Histograms and Filters.

Chapter 5, Automated Optical Inspection, Object Segmentation, and Detection.

Chapter 6, Learning Object Classification

Chapter 7, Detecting Face Parts and Overlaying Masks,

Chapter 8, Video Surveillance, Background Modeling, and Morphological Operations,

Chapter 9, Learning Object Tracking

Chapter 10, Developing Segmentation Algorithms for Text Recognition,

Chapter 11, Text Recognition with Tesseract


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Adversarial Autoencoders on MNIST dataset Python Keras Implementation

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Deep Learning for Cosmetics

In this blog post, how we can use computer vision to solve a particularly poignant instance of this problem: finding influencers, images and videos that address a specific eye shape and complexion. Along the way, we’ll illustrate how three simple yet powerful ideas — geometric transformations, the triplet loss function and transfer learning — allow us to solve a variety of difficult inference problems with minimal human input.

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@DeepLearning_AI

Three models for Kaggle’s “Flowers Recognition” Dataset (6 min read)

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