AI- Notes
- Tanishq Wadhwani

- Jul 25, 2021
- 2 min read
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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