README.md

Machine-Learning-with-Python -

This repository will contain both code and additional links to code / reading material refrences for Machine-Learning-with-Python - Machine-Learning-with-Python

Table of Contents

PyTorch

• 1 PyTorch-SimpleNet

• 1.1-PyTorch-Layers-Conv2d-Architecture

• 1.2-PyTorch-transforms.Compose

• 1.3-PyTorch-freezingLayers-torch.no_grad()

• 1.4-PyTorch-Initial-ObjectDetection-YouTube

• 1.4a-PyTorch-Initial-ObjectDetection-Git

• 1.5-PyTorch--SimpleNet-Airplane-Classifier

• 1.5a-PyTorch_normalization_setting_from_Imagenet_Explained

• 1.5b-PyTorch_normalization_SOUMITH_CHINTALA_reply_for_transform.Normalize

• SOUMITH_CHINTALA

• [2] Diffusers - Text to Image

• 2.1-Create-own-pipeline

• [3] detectron2 from Facebook AI Research- for Object Detection

• 3.1-detectron2-FAIR-Panoptic-Segmentation-YouTube

• 3.1a-detectron2-FAIR-Panoptic-Segmentation-GitRepo

• (https://github.com/RohitDhankar/detectron2_coco_panoptic_seg/blob/main/output_dir/__11_04_2022_23_50__/_dir_img_gt_bbox_/_11_04_2022_23_50_/000000000139_0.50_.png)

• 3.2-detectron2-faster_rcnn_R_50_FPN_3x

• 3.3-detectron2-config_explained -self.get_config.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"))

• 3.3a-detectron2-LAUNCH_explained LAUNCH-detectron2

• 3.3b-detectron2-model_zoo_explained

• 3.3c-detectron2 , COCO API explained , pycocotools , training data set , validation set , categories of the COCO Dataset

• [pyCOCO-get-annotation-ID's]

• annotation_ids = coco_obj.getAnnIds(imgIds=ls_image_ids_bikes[iter_image], catIds=[2])(https://github.com/RohitDhankar/Obj_Detect_Detectron2/blob/950503a6f64783d8d54e50af49e3e9888a14b428/src/det2_1.py#L101C5-L101C91)

• 3.4-detectron2 from FAIR

• 3.5-kaggle_sartorius-cell-instance-segmentation-GIT-Repo 3.2-kaggle_sartorius-YOuTube-Explainer

• 4-OpenPCDet_Initial_Demo_MAYAVI_KITTI_LIDAR

• 4.1-OpenPCDet_Git-Experiments_KITTI_LIDAR

[TODO--List--detectron2]

• 1-detectron2-DensePose-CSE-Continuous_Surface_Embeddings
• [1-detectron2-DensePose]

[neural-nets-basics]

• [1] Neural Networks - mostly CNN for Image tasks

• CNN-Layers_architecture_Explained-GITRepo

• 1-Book-1-Practical-Deep-Learning(Anirudh-Koul)

• 1.1-Book-1-->Image_Similarity_Search_18March22-YouTube

• 1.1a-Book-1-->Image_Similarity_Search_18March22-GitRepo

• 1.1b-Book-1-->Image-Feature-Extraction-GitRepo

• 1.1c-Book-1-->Image-Feature-Extraction_for_ACTIVE_Learning_LIGHTLY-YouTube -2-Book-2-->PyTorch-SimpleNet-AirplaneClassifier

#

-TRAIN----torch.cuda.memory_allocated---> 0.02 GB
Epoch:10,TRAIN_Loss:0.00,VAL_Loss:0.00, Accuracy = 1.00
---labels[prediction]----
 not_plane
---labels[prediction]----
 not_plane

Active Learning

• [1] Active Learning - mostly Lightly
• testing Lightly
• 1.1-Lightly Active Learning -YouTube
• 1.2-Lightly Active Learning-SimSiam -YouTube
• 1.3-Lightly Active Learning- -YouTube
• 1.4-FeatureExtraction-Resnet50

Object Detection -- Yolov4

• [1] Yolov4
• 1.1-Initial-Experiments-Yolov4
• 1.2-Yolov4-Git(DEPRECATED-theAIGuysCode)

LLM Usage -- LangChain , ScikitLLM

• [1] Using Large Language Models - LangChain , ScikitLLM
• [TODO] -ScikitLLM- https://github.com/iryna-kondr/scikit-llm
• [TODO]
• [TODO]

Classic Legacy NLP -- NLTK

• [FaceBook-WebHooks-ChatBot]
• [TODO]
• [TODO]

GEO-GIS-DeepLearning-for-Geography

• [1] raster-vision
• raster-vision
• 1.1-raster-vision
• 1.2-QGIS-SpaceNet
• 1.2a-QGIS-SpaceNet
• 1.3-QGIS-LAStools_LIDAR_1
• 1.3-QGIS-LAStools_LIDAR

OpenCV - OpenCV Projects

• [1] OpenCV Projects
• 1.1-MediaPipe_OpenCV_Hand_Pose_Tracking
• 1.2-Credit Card Digit Detection
• 1.2a-Credit Card Digit Detection
• 1.3-OpenCV_mask_blue - cv2.bitwise_and)
• 1.4-OpenCV_SIFT_ORB_KeyPoints_Detection)

Generic Core Python

• [1] Python-3 basics , data transforms etc.

• PySpark

• pandas

• [2] Download and Preprocess various kinds of Data

• data_aggregations

• [3] Visualizations of Data for EDA and others

• 3.1-EDA-Exploratory-Data-Analysis

• 3.2-Bokeh-plots-for-Tukeys-Five-Point-Summary

• 3.3-Bokeh-Box-plots-for-Tukeys-Five-Point-Summary

• [3.4-statsmodels] --> TODO (https://github.com/RohitDhankar/time_series/blob/main/ts_1.ipynb)

• [3.5-matplotlib-TimeSeries-Plots] --> TODO (https://github.com/RohitDhankar/time_series/blob/main/ts_1.ipynb)

• 3.6-Levenshtein-distance

• DataTables-DigitalCognition

• Code-walkthrough-datatables.net-datatables.js

• PCA-->INTRO-->cPCA-ContrastivePrincipleComponentsAnalysis

Not Core Machine Learning [Full Stack Machine Learning]

• [1] Software Development - Data Focused App Development
• 1.1-ChatGPT-FlaskApp
• 1.1a-ChatGPT-FlaskApp-VariationAPI
• 1.2-Django-jQuery-Bokeh-Exploratory-Data-Analysis
• 1.2-uWSGI
• Django-wsgi - https://github.com/RohitDhankar/digitalCognition/blob/master/dc_dash_proj/wsgi.py
• 1.3-NGINX
• 1.3-Nginx_uWsgi_Django_Registration_Redux
• [2] Tornado - async framework
• [2.1-Tornado] (https://github.com/RohitDhankar/Machine-Learning-with-Python_ML_Py/tree/master/dev_tornado)