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AI- Notes

Data Science

AI

Robotics

Web Development

ML

DL

NLP

CV

AA

Big Data

Web Mining


Kaggle

ML


Weak AI/Narrow AI

Strong AI/General AI

Artificial Super Intelligence


Python

Bots


Research AI

Applied AI


Maths


Deploy and Compute


H/W


Colab


GANs


Learn Linear Algebra and Multivariate Calculus

Learn Statistics

Learn Python


Supervised Learning labeled data classification and regression models

Unsupervised Learning unlabelled data factor and cluster analysis models

Semi-supervised Learning Unsupervised Learning unlabelled data small amount of labeled data

Reinforcement Learning trial and error


journals


Numpy

Pandas

Matplotlib

Seaborn

Scikit-Learn

Tensorflow and Pytorch


Python

Scipy

Numpy

Pandas

Matplotlib

Data Exploration / Cleaning / Preparation

ML

Scikit-learn

Kaggle

Deep Learning

Ensemble modeling

Machine Learning with Big Data

Text mining and databases

genetic algorithm based robot


Math:

Calculus

Linear Algebra

Discrete mathematics

Probability theory

Descriptive and Inferential Statistics

Programming:

Basic Python

Advanced Python (OOP, main libraries like Pandas, Numpy, Matplotlib, etc…)

Algorithms

SQL

Second programming language: C++ or R (optional)

Machine Learning:

Supervised algorithms (regression, classification)

Unsupervised and semi-supervised algorithms (clustering, dimensionality reduction, graph-based algorithms)

Deep learning (CNNs and RNNs)

Reinforcement learning (dynamic programming, Monte Carlo methods, heuristic methods)

Machine Learning algorithms for different tasks:

Computer vision (e.g.: classification, object detection, semantic segmentation)

Natural language processing (e.g.: text classification, sentiment analysis, language modeling, machine translation)

Recommending systems

Classic machine learning


Math

Python

Statistics

Machine Learning


A good enough GPU (4+ GB), preferably Nvidia

An OK CPU (eg. Intel Core i3 is ok, Intel Pentium may not be)

4 GB RAM or depending upon the dataset.


Caffe

DeepLearning4j

Tensorflow

Theano

Torch


Deep Learning for Computer Vision

Deep Learning for Natural Language Processing

Deep Learning for Speech/Audio

Deep Learning for Reinforcement Learning


RTX 2070


Implement a Paper


TPUs


Nvidia GTX 10

8 GB RAM

1 TB HDD

256 GB SSD

i5 8th Gen


Python

Linear Algebra

Calculus

Probability and Statistics

Key Machine Learning Concepts


CNNs

Sequence Models

NLP

Unsupervised Deep Learning

GANs


Computer vision

Natural Language Processing (NLP)

Memory Network (RNN-LSTM)

Deep Reinforcement Learning

Generative Models


Supervised: Output

Unsupervised: Pattern

Reinforcement: Behaviour



AI, ML, DL- Best way to get started, Best way to learn


Further: H/W required

 
 
 

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