deephivemind / gateway_to_deepreinforcementlearning_deepnn

:trophy: Welcome to the wonderland of "AI" = f(DL, RL, DRL, ML, NLP, KG, MLOPS)

Technical Nuances of The World of "Enterprise AI"

Deep-Learning | Deep-Reinforcement-Learning | NLP-IE-Pipeline | MLOPS-Real-World-ML-System | Knowledge-Graph | AI-Security-&-Privacy | Serverless-AI/ML | AI-Datasets | AutoML-&-HPO |

This repository is my humble endeavour to take a you through The Best of Enterprise AI by introducing you to very many constructs, design elements, real life AI applications, codesets, FAQs from the captivatingly beautiful landscape of AI. Gear up for the Universe called "AI". Bon Vaoyage!!

A Solemn Commitment:
I, The Captain of the Space Cruiser- DeepHiveMind, would continue to enrich this repository in my relentless persuit of democratizing the field of AI.

Welcome Onboard!

Your journey begins with **The wonderland of AI** wherein you get to relish the gigantic-yet-so-beautiful landscape of AI. Herein, you will be pampered with Infographic panoramic view of the design, the sweeping constructs of AI, and many more such delightful introductory elements. I'm sure, You'll relish the gateway to AI wonderland!

Post that, In this voyage, you will be treated with ultra-luxurious warmth of The REAL world of AI applications, wherein pleathora of actual codesets with varied flavours will welcome you in detail. Please cherish the tangy-yet-so-sweet flavour of AI application codesets! Then comes the next pitstop. Dress up for to be lavishly treated with amazing-awesome-powerful-&-yet-so-beautiful world of Deep Reinforcement Learming (DeepRL), Automated Machine Learning (AutoML), Productionizing of ML Model (MLOps. Yes, this is global AI shift! Let us together unleash the power of DeepRL, AutoML, MLOps, and transcend to the new era of AI!

Every destination of this tour is delicately curated to make your AI experience wonderful. This carefully-curated AI itinerary is planned by me based on my 10+ dedicated years of rich, deep hands-on engagement with various streams of "Enterprise AI, ML, NLP-Generation, Knowlede Graph, Converstaional AI, DL(Deep Learning) & DRL(Deep Reinforcement Learning)" - be it

	- **Cognitive AI** , powered by Deep Neural networks, such as 
		- Computer vision, 
		- Natural Language Proessing, Understanding,  Generataion (NLP-NLU-NLG), 				
		- Audio & Speech analytics, 
		- Conversational AI,
		- Multimodal analytics
	
	
	- **classical Predictive machine learning**,
	- **Strcutured Data AI/ML - Predictive ML** , powered by Deep Neural networks, such as 
			-Structured data classification from scratch
			-Collaborative Filtering for Recommendations
			-credit card fraud detection: Heavily Imbalanced classification
			-Timeseries anomaly detection

	- **AutoML**,
		- AutoKeras (NAS with Bayesian Optimization)
		- Google AutoML (NAS with Reinforcement Leaning)
		- Hyper Parameter Optimization
		- Randmoized/Grid Serach optimization

	- **Knowledge Graph**,
		- Ontology based on RDF/OWL subject-object-predicate semantic triples 
				- Apache JENA/ AWS NEPTUNE with SPARQL
				- GRAKN with GRAQL
		
		- Semantic lexicon for 
			- BioMedical ontology (used for Precision Medicine & Disease Network - BioGrakn)
			- English language (used extensively by computational linguists and cognitive scientists - WordNet/ SynSet
			- Law (legal ontologies for Law based knowledge formalization - JurWordNet/ DOLCE+)
	

	
	- **Deep Reinforcement-learning** (self learning autonomous systems), 

	- **Distributed & Parallel ML**,  
	
	- **AI-as-a-Service**,
		- RESTful API
		- gRPC API

	- **Custom MLOps** (the Next-Gen MLOPS),
	
	- **Comprehensive AI GOVERNANCE**,
		- 'AI TRUST' 
		- 'AI Collaboration, Sharing & Exchange' 
         		- 'AI Security & Privacy' 
		- 'AI Scalability' 
		- 'AI Inference Model Update/ Roll out' Mechanism 
		
	- **SERVERLESS AI/ML**
	
	and so on so forth for variuos industries such as  ***Banking & FS, Insurance, Halthcare, Retail, Energy and utility.***

The vantage view of Agenda

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Please note:

• This page serve as the Index page.
• Please click on hyperlinks of the respective items to delve deep into it, i.e, Click to land at my other delicately handcrafted Repos/info-files for deep dive into respective items..
• Please keep checking your compass (this index page) for to seamlessly steer your way to the next milestone of this wonderful journey.

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In this repository, sharing with you delicately hand crafted exquisite bouquet of offerings: Below is the list of hyperlinks to my other Repo/Readme recepies/notebook/codesets. Just Click on hyperlinks of the respective items to delve deep into it.

• World of AI

  • The 1st, 2nd & 3rd Generations of AI
  • Standard Definition of AI & ML
  • AI ML Applications in Real world
  • The Evolution of AI ML in the Industry
  • AI through Inforgraphics
  • The world of Deep Learning applications

• World of 3D Imaging AI

  • Medical Imaging overview
  • LIDAR Imaging & Point Cloud overview
  • Seismic Geoscience Imaging overview
  • Satellite maging overview
  • Thermal Imaging overview -Geometric, Photometric and Morphological - Image transformations

• World of Popular Datasets for AI SOTA Models

  • Audio SOTA Model Datasets
  • Image SOTA Model Datasets
  • Image Classification SOTA Model Datasets
  • Image Object detection SOTA Model Datasets
  • Visual Question answering SOTA Model Datasets
  • NLP Text SOTA Model Datasets
  • NLP Text Summarization SOTA Model Datasets
  • NLP Text Translate NMT SOTA Model Datasets
  • Video SOTA Model Datasets

• World of Deep_NeuralNet AI_Applications Keras_TF2.0

  • Computer Vision

      -Image classification from scratch
      -Simple MNIST convnet
      -Image segmentation with a U-Net-like architecture
      -Next-frame prediction with Conv-LSTM
      -Grad-CAM class activation visualization
      -Model interpretability with Integrated Gradients
      -Metric learning for image similarity search
      -Point cloud classification with PointNet
      -Few-Shot learning with Reptile
      -Visualizing what convnets learn
    

  • Natural language processing

      -Text classification from scratch
      -Sequence to sequence learning for performing number addition
      -Bidirectional LSTM on IMDB
      -Character-level recurrent sequence-to-sequence model
      -Using pre-trained word embeddings
      -Text classification with Transformer
      -BERT (from HuggingFace Transformers) for Text Extraction
    

  • Structured Data

      -Structured data classification from scratch
      -Collaborative Filtering for Movie Recommendations
      -Imbalanced classification: credit card fraud detection
    

  • Timeseries

      -Timeseries anomaly detection using an Autoencoder
    

  • Generative Deep Learning

      -Variational AutoEncoder
      -GAN overriding Model.train_step
      -WGAN-GP overriding Model.train_step
      -Neural style transfer
      -Deep Dream
      -Character-level text generation with LSTM
      -PixelCNN
      -Text Generation with miniature GPT
    

• Deep NeuralNet AI Applications Additional Codesets

   	-Image Object Detection SSD
   	-Image Segmentation UNET 
   	-Object Detection and Segmentation RCNN MaskRCNN 
   	-Skin Cancer Lesion detection from dermoscopic images
   	-Automated Brain Tumour Segmentation
   	-Scene Text detection from wild Images using CRNN CTPN DenseNet-OCR
   	-Custom OCR Engine
   	-Text detection from IDCard
   	-Automated Number Plate Detection
   	-Microservice Microweb framework for AIaaS
  

• World of Audio & Speech processing

• World of Deep Reinforcement Learning Self evolving systems

  • Introduction to Industry Implementation of DeepRL

  • Introduction to High level constructs of DeepRL

      -Agent
      -Environment
      -Action
      -State
      -Reward
      -Policy
      -Value function
      -Function approximator
      -Markov decision process (MDP)
      -Dynamic programming (DP)
      -Monte Carlo methods
      -Temporal Difference (TD) algorithms
      -Model
    

  • Introduction to Significant DeepRL algorithms

      -Value Optimization Agents Algorithms
          
          -Deep Q Network (DQN)
          -Double Deep Q Network (DDQN)
          -Mixed Monte Carlo (MMC)
          
      -Policy Optimization Agents Algorithms
          
          -Policy Gradients (PG)
          -Asynchronous Advantage Actor-Critic (A3C)
          -Deep Deterministic Policy Gradients (DDPG)
          -Proximal Policy Optimization (PPO)
       
      -General Agents Algorithms
          -Direct Future Prediction (DFP)
    

  • Introduction to Advanced DeepRL algorithms

      - Imitation Learning Agents Algorithms
          -Behavioral Cloning (BC)  
          -Conditional Imitation Learning
      - Hierarchical Reinforcement Learning Agents Algorithms
          -Hierarchical Actor Critic (HAC)
      - Memory Types Algorithms
          -Hindsight Experience Replay (HER)
          -Prioritized Experience Replay (PER)
    

  • Comparison of Main DeepRL frameworks

      Keras-RL (Developed by Matthias Plappert- Employed with OpenAI)
      OpenAI Gym
      Facebook Horizon
      Google Dopamine
      Google DeepMind TensorFlow Reinforcement Learning (TRFL)
      Coach (Developed by Intel Nervana Systems)
      RLLib (Highly customizable open source DeepRL framework with support for TF2.0/PyTorch 1.4, customization for Environments/Policy/Action)
      Tensorforce (Tensorforce is built on top of Google's TensorFlow framework version 2.0 by Alexander Kuhnle - currently with BluePrism)
    

  • DeepRL HANDS-ON with Keras-RL & TF & OpenAI-Gym

      Deep Q Learning (DQN)
      Double DQN 
      Deep Deterministic Policy Gradient (DDPG) 
      Continuous DQN (CDQN or NAF)
      Cross-Entropy Method (CEM)
      Dueling network DQN (Dueling DQN)
      Deep SARSA
      Asynchronous Advantage Actor-Critic (A3C)
      Proximal Policy Optimization Algorithms (PPO)
    

  • Interesting DeepRL Framework RLlib

    • DeepRL HANDS-ON Additional

• World of NLP IE Pipeline

• World of AutoML AutoKeras HPO

  • Introduction to AutoML

  • High Level technique needed in AutoML

  • Comparative view of Commercial AutoML Offerings

  • Comparative view of AutoML Projects TYPES NAS HPO AutoFE

  • World of AutoKeras (Open Source AutoML)

  • Detailed Insight into the constructs of AutoML ecosystems

      - Automated Feature Engineering
        - [Expand Reduce]
        - [Hierarchical Organization of Transformations]
        - [Meta Learning]
        - [Reinforcement Learning]
      - Architecture Search
        - [Evolutionary Algorithms]
        - [Local Search]
        - [Meta Learning]
        - [Reinforcement Learning]
        - [Transfer Learning]
      - Hyperparameter Optimization
        - [Bayesian Optimization]
        - [Evolutionary Algorithms]
        - [Lipschitz Functions]
        - [Local Search]
        - [Meta Learning]
        - [Particle Swarm Optimization]
        - [Random Search]
        - [Transfer Learning]
    

• Hyper Parameter Optimization (HPO) in Classical ML - Grid Serach/Random Search

• Deep Dive into AI/ML SECURITY

  • Why AI/ML SECURITY is important
  • Real World AI/ML System Comprehensive SOC Solution

• Real World ML System Powered by MLOPS

  • INSPIRATION for MLOPS
    • WHY DO WE NEED MLOPS?
      • EVOLUTION JOURNEY IN AI MATURITY CURVE
      • INDUSTRY SURVEY
      • HIDDEN TECH DEBT IN REAL WORLD ML SYSTEM
  • MLOPS VS DATAOPS VS AIOPS VS PLATFORMOPS
  • STATE OF MACHINE LEARNING OPERATIONS IN Y2019
  • MLOPS Reference Architecture
  • High level constructs of MLOPS
  • Detail References to the Constructs and Tools of MLOPS
  • MLOPS Architecture based on KUBEFLOW
    • Train and Serve TensorFlow Models at Scale with KUBERNETES and KUBEFLOW
      • Docker - Containerizatioin of AI Modules
      • Kubernetes - K8S for AI
      • Helm Charts on K8S for AI complex deployment
      • Kubeflow for AI MLOPS
      • JupyterHub on K8S- AI Build Notebook
      • TFJob- K8S custom kind for AI Training on GPU/CPU/TPU
      • Distributed Tensorflow
      • Hyperparameters Sweep with Helm
      • AI Model Serving for prediction/scoring

• Deep Dive into AI/ML SECURITY

• AI Hub

• FAQ Interview on DL/ML/AI

• References to the Constructs and Tools of AI/ML

References to the Constructs and Tools of AI ML

Click below items for safe ladning on the planets of the respective items. These planets are delicately curated handcrafted my preferred list of tools. Takes to my other repos/info-files.

🧵 Data Flow pipelines for AI/ML	💸 Data Stream Processing	🗞️ Data storage	🏷️ Data Labelling
🌀 Feature engineering	🎁 Feature Stores	📓 Reproducible Notebooks	🏁 Model Orchestration & Deployment Framework
🗺️ ML Training Computation distribution	📊 ML Training & Indutrial Visualisation frameworks	⚔ Adversarial Robustness	📥 Model serialisation
🔍 Explaining predictions & models	🔏 Privacy preserving ML	📜 Model & data versioning	🧮 Optimized calculation frameworks
🤖 AutoML	📡 Functions as a service	🔠 Industry-strength NLP	💰 Commercial Platforms

A Quick Recap through Infographics

Hola! You have arrived at your destination. Hope you chrished the journey.

Hurray! You are now Black belt Data Scientist.

Sayonara!! Before, you take a leave, here is a quick recap of interesting journey through Infographics. No Words! Just the Visuals!

DeepHiveMind Cognitive AI Model Hub

A quick recap of the some of the Cognitive models shared in this Repo

Please pay attention to the announcement:

• Some of these Models repos shared hereiwth me (DeepHiveMind) are delicately curated and handcrafted.
• There are many other Repos which I have shared in this project beyond what is listed out below. Takes to my other repos/info-files.

Click below items for happy ladning on & surfing some of the very many Cognitive models shared by in the DeepHiveMind repo.

🧠 Combining Cityscapes and Mapillary to train SSD using Supervise.ly
🧠 Object Detection and Segmentation
🧠 FaceMask-Adaptivity-Detection_Covid19
🧠 Skin lesion detection from dermoscopic images
🧠 Automated_Brain_Tumour_Segmentation
🧠 Scene Text Detection from wild Images
🧠 Road scene segmentation on Cityscapes with Tensorflow and UNet
🧠 Custom OCR Engine and Model
🧠 Automated Plate number & Plate State recognition ANPR using OCR Engine
🧠 Microsocpy Medical Healthcare Segmenting Nuclei in Microscopy Images
🧠 Language_Detection-in-OCR_Image_AI
🧠 License-Plate-Detection_Image_Video_AI-as-a-Service
🧠 LicensePlateDetector-deployment-flask-master
🧠 Face_Emotion_Detection_AI_Model
🧠 Language_Detection-in-OCR_Image_AI
🧠 NLP - Bidirectional_lstm_IMDB movie review sentiment classification
🧠 class activation heatmap for an image classification model
🧠 A simple DCGAN Model
🧠 Neural Style Transfer
🧠 Timeseries Anomaly Detection using DeepNN
🧠 ASR Speech Model
🧠 Neural Image Captioning
🧠 Bio Ontology Knowledge-Graph
🧠 Video Action Recognition AI
🧠 AI_powered_PII-Protection-and-Anonymization_Image&Text_Service
🧠 Azure 'Serverless' Pipeline for Video_AI with Video Transcription and Key_Topic detection
🧠 Video-AI-ServingPipeline_Object_Anomaly_Topic_Audio_Emotion_Detection

Additional references to DL,RL,DRL,NLP,ML,OCR

OpenAI: Building the Infrastructure that Powers the Future of AI

During KubeCon 2017, Vicki Cheung and Jonas Schneider delivered a keynote explaining how OpenAI manage to handle training at very large scale with Kubernetes, it is worth listening to:

Please refer to Additional references for learning and career transitioning to the world of 'DL,RL,DRL,NLP,ML,OCR'

Contact info

Feel free to contact me to discuss any issues, questions, or comments.

My contact info can be found on my GitHub page.

License

I, The DeepHiveMind, am providing code and resources in this repository to you under custom Copyright & license (Copyright 2019 DeepHiveMind & Creative Commons Legal Code CC0 1.0 Universal). Please Refer to the Copyright 2019 DeepHiveMind License for further details as to this. Thanks!