Видео

How to resolve it? It is still zoomed in😅

LLAMA_v2_Init_experiments_2023-10-02 23:45:53.mp4 -- github.com/RohitDhankar/Machine-Learning-with-Python_ML_Py/issues/125

Thanks to the LEAD DEVELOPERS AT LAVIS and HUGGINGFACE DIFFUSERS - SAYAK PAUL -- www.linkedin.com/in/sayak-paul/ AYUSH MANGAL -- github.com/ayushtues , www.linkedin.com/in/ayushtues/ Am just running their code on a local system -- just trying to learn -- thanks

SEARCH IN GITHUB for more code snippets results.append(conv_layers[i](results[-1]) github.com/search?q=results.append%28conv_layers%5Bi%5D%28results%5B-1%5D%29&type=code

Pytorch_resnet50_Visualize_CONV_layer_Activation_Feature_Maps github.com/RohitDhankar/Machine-Learning-with-Python_ML_Py/blob/master/src/basic_foo/a_test.py

pytorch_CycleGAN_and_pix2pix_Test Testing the INFERENCE code from this REPO -- github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/scripts/download_cyclegan_model.sh#L3

@4:30-- detectron2 , COCO API explained , pycocotools , training data set , validation set , categories of the COCO Dataset

@3:30--detectron2--model_zoo -- explained

@13:00-- detectron2 -- LAUNCH Method explained -- from detectron2.engine import default_argument_parser, default_setup, default_writers, launch #

@30:00-- detectron2 -- config explained -- self.get_config.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"))

@31:00-- detectron2 -- config explained

@14:00 -- Initial experiments with Yolov4

github.com/RohitDhankar/test_lightly_1/issues/1

github.com/RohitDhankar/test_lightly_1/tree/alt_ftr_extract/output_dir/knn_plots_1/faces_1/_06_04_2023_23

Active Learning - Image Processing - Feature Extraction -Resnet50- Tensorflow-KERAS--Image Similarity , kNN github.com/RohitDhankar/test_lightly_1/tree/alt_ftr_extract/output_dir/knn_plots_1/faces_1/_06_04_2023_23

Lightly_Active_Learning - Feature Extraction Alternative Approach github.com/RohitDhankar/test_lightly_1/issues/1

@3:01 -- Early experiments with Tensorflow and Keras -- Tensorflow-forPoets--retrain.py

@1:11 -- Reading in multiple Raster TIFF Files with - rastervision and RasterIO

@8:50 -- Input Layers of an ANN Defined

@12:51 -- HaarCascade Classifiers for -- face and Eye Detection with OpenCV

@6:30-- Binary Masks of Images - the RLE Format for Bio Cells

1:20 - Suggestions for CPU and GPU Memory Swaps -- for GPU memory optimization issues

16:30 -- transforms.Compose -- Pytorch Transforming and augmenting images . transforms.ToTensor(),transforms.Normalize -- using the Imagenet STD DEVIATION and MEAN

13:00 - Freezing Layers , preventing back propagation -- torch.no_grad()

6:05 -- Neural Network Architecture - CNN Layers Explained

RasterVision -- continuing with init experiments github.com/RohitDhankar/raster-vision/blob/master/code/init_test_script.py

github.com/RohitDhankar/raster-vision Raster-VISION github.com/RohitDhankar/raster-vision Raster-VISION ruclips.net/video/vkR8yF4y6cw/видео.html

KAGGLE SARTORIUS COCO RLE Video 1 github.com/RohitDhankar/kaggle_sartorius-cell-instance-segmentation/pull/7

github.com/RohitDhankar/OpenPCDet/blob/master/init_INSTALL_ERRORS_1.md

Original Code and all CREDITS -->> github.com/open-mmlab/OpenPCDet

github.com/RohitDhankar/detectron2_coco_panoptic_seg

Detectron2 - Panoptic Segmentation ## Database : "The Penn-Fudan-Pedestrian Database" github.com/RohitDhankar/Obj_Detect_Detectron2

Detectron2_COCO_Data_5 YOU_TUBE -- ruclips.net/video/NRred6RjWsU/видео.html GITHUB -- github.com/RohitDhankar/Obj_Detect_Detectron2

github.com/RohitDhankar/Obj_Detect_Detectron2

github.com/RohitDhankar/Obj_Detect_Detectron2

can u pls show how to use with open cv

github.com/RohitDhankar/Obj_Detect_Detectron2/tree/dev_rohit

github.com/RohitDhankar/obj_det/tree/dev_rohit/_taco_1

Can you please share the code? Or github link

Nice material. Looking forward to many more analytics videos.

Hi, is there any way where you can show the code or a part of it or show how you did it?

sir can i have source code for descriptive stats pls???

Can you please share the code to Study

Querying MongoDB - MongoCompass and MongoShell # Querying MongoDB - MongoCompass and MongoShell for an initial level data exploration task Am using the sample datasets provided by MongoATLAS This demo video is without a Voice Over as there is construction going on next door , they are very noisy :) My contacts - email-1 - dhankar.rohit@gmail.com email-2 - rohit.dhankar@strategic-leadership-llc-india.com LinkedIn - www.linkedin.com/in/rohitdhankar/ GitHub - github.com/RohitDhankar ### TASK -2 --- Split the collections into smaller collections . Split the existing collection on Review dates --- to create TEST Collections on which we can then practise our JOINS db.listingsAndReviews.count() ISODate("2019-02-07T05:00:00Z"), ISODate("2019-02-08T05:00:00Z"), ISODate("2019-02-09T05:00:00Z"), ISODate("2019-02-10T05:00:00Z"), ISODate("2019-02-11T05:00:00Z"), ISODate("2019-02-13T05:00:00Z"), #2019-02-08T05:00:00Z db.listingsAndReviews.find({ 'first_review': {$eq:ISODate("2019-02-07T05:00:00Z")}}) db.listingsAndReviews.find({ 'first_review': {$gt:ISODate("2019-02-07T05:00:00Z")}}).count() # 72 db.listingsAndReviews.find({ 'first_review': {$gte:ISODate("2019-02-07T05:00:00Z")}}).count() # 77 2016-01-03T05:00:00.000Z db.listingsAndReviews.find({ 'first_review': {$lte:ISODate("2016-01-03T05:00:00.000Z")}}).count() # 1112 db.listingsAndReviews.find({ 'first_review': {$gte:ISODate("2016-01-03T05:00:00.000Z")}}).count() #3072 {'first_review':{$gte:ISODate("2016-01-03T05:00:00.000Z")}} db.listingsAndReviews_backup.find( {first_review: {$gte: ISODate('2016-01-03T05:00:00.000Z')}} ) #3072 db.listingsAndReviews.find({ 'first_review': {$gte:ISODate("2016-01-03T05:00:00.000Z")}}).count() db.newCol_1.find({ 'first_review': {$gte:ISODate("2018-01-01T05:00:00.000Z")}}).count() use sample_airbnb show collections Compass {'first_review':{$gte:ISODate("2016-01-03T05:00:00.000Z")}} ISODate("2019-02-14T05:00:00Z"), ISODate("2019-02-15T05:00:00Z"), ISODate("2019-02-16T05:00:00Z"), ISODate("2019-02-17T05:00:00Z"), ISODate("2019-02-18T05:00:00Z"), ISODate("2019-02-19T05:00:00Z"), ISODate("2019-02-20T05:00:00Z"), ISODate("2019-02-22T05:00:00Z"), ISODate db.getSiblingDB("sample_airbnb").listingsAndReviews.aggregate([ {$group :{_id : "$first_review", num_occurence_of_attr_written_to_NewColl: { $push: "$first_review" }}}, {$out : "test_reviews_collection" } ]) use sample_airbnb show collections db.listingsAndReviews.count() db.listingsAndReviews. db.listingsAndReviews.renameCollection('listingsAndReviews_backup') db.new_collection.count() new_collection_backup db.new_collection_backup.count() db.new_collection_backup.findOne() db.new_collection.findOne() db.new_test_reviews_collection.count() db.new_test_reviews_collection.findOne() db.new_test_reviews_collection.drop() db.new_collection.renameCollection('new_collection_backup') db.new_collection_backup.updateMany( {}, { $rename: { "listingsAndReviews": "newName_listingsAndReviews" } } ) ### Convert Dtype # ascertain Dtypes db.test_reviews_collection.aggregate([{ "$project": { "key_DType": { "$type": "$first_review" } } } ]) db.listingsAndReviews.aggregate([{ "$project": { "key_DType": { "$type": "$first_review" } } } ]) ## here above the Dtype is a DATE .. whereas earlier with - distinct - we saw == ISODate("2019-02-22T05:00:00Z") { "_id": { "$date": "2016-11-27T05:00:00.000Z" }, "num_occurence_of_attr_written_to_NewColl": [{ "$date": "2016-11-27T05:00:00.000Z" }, { "$date": "2016-11-27T05:00:00.000Z" }, { "$date": "2016-11-27T05:00:00.000Z" }, { "$date": "2016-11-27T05:00:00.000Z" }] } ### Rename a Key db.listingsAndReviews.updateMany( {}, { $rename: { "first_review": "first_review_entry" } } ) ### within a .py module - Slice and .count() or .count_documents() db.listingsAndReviews.find({'name':{$gte:ISODate("2016-01-03T05:00:00.000Z")}}).count() {'first_review':{$gte:ISODate("2016-01-03T05:00:00.000Z")}} query_counts = {'summary': {'$regex': duplex}} query_counts1 = {'first_review': {'$gt': '2016-01-03T05:00:00.000Z'}, 'name': {'$regex': duplex }} query_counts2 = {'first_review': {'$gt': '2016-01-03T05:00:00.000Z', '$lt': '2016-01-07T07:00:00.000Z'}, 'summary': {'$regex': duplex}} query_Count = db.listingsAndReviews.count_documents(query_counts) query_Count_gt = db.listingsAndReviews.count_documents(query_counts1) query_Count_gt_lt = db.listingsAndReviews.count_documents(query_counts2) { "_id" : "10066928", "key_DType" : "missing" } { "_id" : "10057447", "key_DType" : "missing" } { "_id" : "10059244", "key_DType" : "missing" } { "_id" : "10066928", "key_DType" : "missing" } { "_id" : "10069642", "key_DType" : "missing" } { "_id" : "10030955", "key_DType" : "missing" } .findOne db.listingsAndReviews_backup.findOne() db.listingsAndReviews_backup.find({ '_id': {$eq:"10030955"}},{first_review:1}) db.listingsAndReviews_backup.find({ '_id': {$eq:"10069642"}},{first_review:1}) ### So it would seem that the KEY == first_review --- is missing ? Checking within Compass --- Yes that’s the reason we get no legible return from Distinct ? ### CONFIRMED that the KEY == first_review --- is missing ?

@7:30 - Live WebScraping AMAZON India - Python Code in the Jupyter Notebook

@4:50 - Live WebScraping AMAZON India - Python Code in the Jupyter Notebook

@12:00 - Price fluctuation of Products on SNAPDEAL

@6:00 -- SSL certbot Nginx Configuration

@3:01 - Nginx config file

@22:00 - Need to beyond 68 Facial Landmarks - require atleast 128