From d7ec888d687725d47b8789578f0bd96876b475b4 Mon Sep 17 00:00:00 2001 From: navanchauhan Date: Sun, 7 Aug 2022 22:54:35 -0400 Subject: rebuild --- docs/posts/2010-01-24-experiments.html | 9 +- .../2019-05-05-Custom-Snowboard-Anemone-Theme.html | 9 +- .../2019-12-04-Google-Teachable-Machines.html | 9 +- .../2019-12-08-Image-Classifier-Tensorflow.html | 9 +- docs/posts/2019-12-08-Splitting-Zips.html | 9 +- .../2019-12-10-TensorFlow-Model-Prediction.html | 9 +- ...019-12-16-TensorFlow-Polynomial-Regression.html | 259 +++++++-------- docs/posts/2019-12-22-Fake-News-Detector.html | 11 +- .../2020-01-14-Converting-between-PIL-NumPy.html | 9 +- ...-01-15-Setting-up-Kaggle-to-use-with-Colab.html | 9 +- ...20-01-16-Image-Classifier-Using-Turicreate.html | 105 ++++--- ...onnect-To-Bluetooth-Devices-Linux-Terminal.html | 9 +- docs/posts/2020-03-03-Playing-With-Android-TV.html | 9 +- docs/posts/2020-03-08-Making-Vaporwave-Track.html | 9 +- ...20-04-13-Fixing-X11-Error-AmberTools-macOS.html | 9 +- .../2020-05-31-compiling-open-babel-on-ios.html | 9 +- ...r-Docking-Workflow-AutoDock-Vina-and-PyMOL.html | 9 +- .../2020-06-02-Compiling-AutoDock-Vina-on-iOS.html | 9 +- docs/posts/2020-07-01-Install-rdkit-colab.html | 141 +++++---- .../2020-08-01-Natural-Feature-Tracking-ARJS.html | 47 +-- docs/posts/2020-10-11-macOS-Virtual-Cam-OBS.html | 9 +- docs/posts/2020-11-17-Lets-Encrypt-DuckDns.html | 9 +- docs/posts/2020-12-1-HTML-JS-RSS-Feed.html | 349 +++++++++++---------- docs/posts/2021-06-25-Blog2Twitter-P1.html | 9 +- .../2021-06-25-NFC-Music-Cards-Basic-iOS.html | 9 +- ...2021-06-26-Cheminformatics-On-The-Web-2021.html | 9 +- ...21-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html | 37 ++- .../2022-05-21-Similar-Movies-Recommender.html | 12 +- docs/posts/2022-08-05-Why-You-No-Host.html | 9 +- docs/posts/hello-world.html | 9 +- 30 files changed, 656 insertions(+), 503 deletions(-) (limited to 'docs/posts') diff --git a/docs/posts/2010-01-24-experiments.html b/docs/posts/2010-01-24-experiments.html index c2caa33..27f9d6d 100644 --- a/docs/posts/2010-01-24-experiments.html +++ b/docs/posts/2010-01-24-experiments.html @@ -47,9 +47,14 @@ -
+ +
+ +
+ diff --git a/docs/posts/2019-05-05-Custom-Snowboard-Anemone-Theme.html b/docs/posts/2019-05-05-Custom-Snowboard-Anemone-Theme.html index 5f4300c..570e1e4 100644 --- a/docs/posts/2019-05-05-Custom-Snowboard-Anemone-Theme.html +++ b/docs/posts/2019-05-05-Custom-Snowboard-Anemone-Theme.html @@ -459,9 +459,14 @@ Section: Themes

You can share this with your friends :+1:

-
+ +
+ +
+ diff --git a/docs/posts/2019-12-04-Google-Teachable-Machines.html b/docs/posts/2019-12-04-Google-Teachable-Machines.html index f92786e..0349eae 100644 --- a/docs/posts/2019-12-04-Google-Teachable-Machines.html +++ b/docs/posts/2019-12-04-Google-Teachable-Machines.html @@ -91,9 +91,14 @@

https://luminous-opinion.glitch.me

-
+ +
+ +
+ diff --git a/docs/posts/2019-12-08-Image-Classifier-Tensorflow.html b/docs/posts/2019-12-08-Image-Classifier-Tensorflow.html index b83190d..ac305ac 100644 --- a/docs/posts/2019-12-08-Image-Classifier-Tensorflow.html +++ b/docs/posts/2019-12-08-Image-Classifier-Tensorflow.html @@ -201,9 +201,14 @@ X_train = X_train/255.0

Link to Colab Notebook

-
+ +
+ +
+ diff --git a/docs/posts/2019-12-08-Splitting-Zips.html b/docs/posts/2019-12-08-Splitting-Zips.html index 72a9176..ed9ecff 100644 --- a/docs/posts/2019-12-08-Splitting-Zips.html +++ b/docs/posts/2019-12-08-Splitting-Zips.html @@ -64,9 +64,14 @@ 
zip -F oodlesofnoodles.zip --out merged.zip
-
+ +
+ +
+ diff --git a/docs/posts/2019-12-10-TensorFlow-Model-Prediction.html b/docs/posts/2019-12-10-TensorFlow-Model-Prediction.html index 795878b..7187fe8 100644 --- a/docs/posts/2019-12-10-TensorFlow-Model-Prediction.html +++ b/docs/posts/2019-12-10-TensorFlow-Model-Prediction.html @@ -87,9 +87,14 @@

Infected

-
+ +
+ +
+ diff --git a/docs/posts/2019-12-16-TensorFlow-Polynomial-Regression.html b/docs/posts/2019-12-16-TensorFlow-Polynomial-Regression.html index a469dd7..7bfe8d4 100644 --- a/docs/posts/2019-12-16-TensorFlow-Polynomial-Regression.html +++ b/docs/posts/2019-12-16-TensorFlow-Polynomial-Regression.html @@ -220,31 +220,31 @@ values using the X values. We then plot it to compare the actual data and predic print(training_cost, coefficient1, constant) -
Epoch 1000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 2000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 3000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 4000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 5000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 6000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 7000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 8000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 9000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 10000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 11000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 12000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 13000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 14000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 15000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 16000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 17000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 18000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 19000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 20000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 21000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 22000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 23000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 24000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 25000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+
Epoch 1000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 2000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 3000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 4000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 5000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 6000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 7000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 8000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 9000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 10000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 11000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 12000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 13000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 14000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 15000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 16000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 17000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 18000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 19000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 20000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 21000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 22000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 23000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 24000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 25000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
 88999125000.0 180396.42 -478869.12
 

 
@@ -279,31 +279,31 @@ values using the X values. We then plot it to compare the actual data and predic
 print(training_cost, coefficient1, coefficient2, constant)
-
Epoch 1000 : Training Cost: 52571360000.0  a,b,c: 1002.4456 1097.0197 1276.6921
-Epoch 2000 : Training Cost: 37798890000.0  a,b,c: 1952.4263 2130.2825 2469.7756
-Epoch 3000 : Training Cost: 26751185000.0  a,b,c: 2839.5825 3081.6118 3554.351
-Epoch 4000 : Training Cost: 19020106000.0  a,b,c: 3644.56 3922.9563 4486.3135
-Epoch 5000 : Training Cost: 14060446000.0  a,b,c: 4345.042 4621.4233 5212.693
-Epoch 6000 : Training Cost: 11201084000.0  a,b,c: 4921.1855 5148.1504 5689.0713
-Epoch 7000 : Training Cost: 9732740000.0  a,b,c: 5364.764 5493.0156 5906.754
-Epoch 8000 : Training Cost: 9050918000.0  a,b,c: 5685.4067 5673.182 5902.0728
-Epoch 9000 : Training Cost: 8750394000.0  a,b,c: 5906.9814 5724.8906 5734.746
-Epoch 10000 : Training Cost: 8613128000.0  a,b,c: 6057.3677 5687.3364 5461.167
-Epoch 11000 : Training Cost: 8540034600.0  a,b,c: 6160.547 5592.3022 5122.8633
-Epoch 12000 : Training Cost: 8490983000.0  a,b,c: 6233.9175 5462.025 4747.111
-Epoch 13000 : Training Cost: 8450816500.0  a,b,c: 6289.048 5310.7583 4350.6997
-Epoch 14000 : Training Cost: 8414082000.0  a,b,c: 6333.199 5147.394 3943.9294
-Epoch 15000 : Training Cost: 8378841600.0  a,b,c: 6370.7944 4977.1704 3532.476
-Epoch 16000 : Training Cost: 8344471000.0  a,b,c: 6404.468 4803.542 3120.2087
-Epoch 17000 : Training Cost: 8310785500.0  a,b,c: 6435.365 4628.1523 2709.1445
-Epoch 18000 : Training Cost: 8277482000.0  a,b,c: 6465.5493 4451.833 2300.2783
-Epoch 19000 : Training Cost: 8244650000.0  a,b,c: 6494.609 4274.826 1894.3738
-Epoch 20000 : Training Cost: 8212349000.0  a,b,c: 6522.8247 4098.1733 1491.9915
-Epoch 21000 : Training Cost: 8180598300.0  a,b,c: 6550.6567 3922.7405 1093.3868
-Epoch 22000 : Training Cost: 8149257700.0  a,b,c: 6578.489 3747.8362 698.53357
-Epoch 23000 : Training Cost: 8118325000.0  a,b,c: 6606.1973 3573.2742 307.3541
-Epoch 24000 : Training Cost: 8088001000.0  a,b,c: 6632.96 3399.878 -79.89219
-Epoch 25000 : Training Cost: 8058094600.0  a,b,c: 6659.793 3227.2517 -463.03156
+
Epoch 1000 : Training Cost: 52571360000.0  a,b,c: 1002.4456 1097.0197 1276.6921
+Epoch 2000 : Training Cost: 37798890000.0  a,b,c: 1952.4263 2130.2825 2469.7756
+Epoch 3000 : Training Cost: 26751185000.0  a,b,c: 2839.5825 3081.6118 3554.351
+Epoch 4000 : Training Cost: 19020106000.0  a,b,c: 3644.56 3922.9563 4486.3135
+Epoch 5000 : Training Cost: 14060446000.0  a,b,c: 4345.042 4621.4233 5212.693
+Epoch 6000 : Training Cost: 11201084000.0  a,b,c: 4921.1855 5148.1504 5689.0713
+Epoch 7000 : Training Cost: 9732740000.0  a,b,c: 5364.764 5493.0156 5906.754
+Epoch 8000 : Training Cost: 9050918000.0  a,b,c: 5685.4067 5673.182 5902.0728
+Epoch 9000 : Training Cost: 8750394000.0  a,b,c: 5906.9814 5724.8906 5734.746
+Epoch 10000 : Training Cost: 8613128000.0  a,b,c: 6057.3677 5687.3364 5461.167
+Epoch 11000 : Training Cost: 8540034600.0  a,b,c: 6160.547 5592.3022 5122.8633
+Epoch 12000 : Training Cost: 8490983000.0  a,b,c: 6233.9175 5462.025 4747.111
+Epoch 13000 : Training Cost: 8450816500.0  a,b,c: 6289.048 5310.7583 4350.6997
+Epoch 14000 : Training Cost: 8414082000.0  a,b,c: 6333.199 5147.394 3943.9294
+Epoch 15000 : Training Cost: 8378841600.0  a,b,c: 6370.7944 4977.1704 3532.476
+Epoch 16000 : Training Cost: 8344471000.0  a,b,c: 6404.468 4803.542 3120.2087
+Epoch 17000 : Training Cost: 8310785500.0  a,b,c: 6435.365 4628.1523 2709.1445
+Epoch 18000 : Training Cost: 8277482000.0  a,b,c: 6465.5493 4451.833 2300.2783
+Epoch 19000 : Training Cost: 8244650000.0  a,b,c: 6494.609 4274.826 1894.3738
+Epoch 20000 : Training Cost: 8212349000.0  a,b,c: 6522.8247 4098.1733 1491.9915
+Epoch 21000 : Training Cost: 8180598300.0  a,b,c: 6550.6567 3922.7405 1093.3868
+Epoch 22000 : Training Cost: 8149257700.0  a,b,c: 6578.489 3747.8362 698.53357
+Epoch 23000 : Training Cost: 8118325000.0  a,b,c: 6606.1973 3573.2742 307.3541
+Epoch 24000 : Training Cost: 8088001000.0  a,b,c: 6632.96 3399.878 -79.89219
+Epoch 25000 : Training Cost: 8058094600.0  a,b,c: 6659.793 3227.2517 -463.03156
 8058094600.0 6659.793 3227.2517 -463.03156
 

 
@@ -339,31 +339,31 @@ values using the X values. We then plot it to compare the actual data and predic
 print(training_cost, coefficient1, coefficient2, coefficient3, constant)
-
Epoch 1000 : Training Cost: 4279814000.0  a,b,c,d: 670.1527 694.4212 751.4653 903.9527
-Epoch 2000 : Training Cost: 3770950400.0  a,b,c,d: 742.6414 666.3489 636.94525 859.2088
-Epoch 3000 : Training Cost: 3717708300.0  a,b,c,d: 756.2582 569.3339 448.105 748.23956
-Epoch 4000 : Training Cost: 3667464000.0  a,b,c,d: 769.4476 474.0318 265.5761 654.75525
-Epoch 5000 : Training Cost: 3620040700.0  a,b,c,d: 782.32324 380.54272 89.39888 578.5136
-Epoch 6000 : Training Cost: 3575265800.0  a,b,c,d: 794.8898 288.83356 -80.5215 519.13654
-Epoch 7000 : Training Cost: 3532972000.0  a,b,c,d: 807.1608 198.87044 -244.31102 476.2061
-Epoch 8000 : Training Cost: 3493009200.0  a,b,c,d: 819.13513 110.64169 -402.0677 449.3291
-Epoch 9000 : Training Cost: 3455228400.0  a,b,c,d: 830.80255 24.0964 -553.92804 438.0652
-Epoch 10000 : Training Cost: 3419475500.0  a,b,c,d: 842.21594 -60.797424 -700.0123 441.983
-Epoch 11000 : Training Cost: 3385625300.0  a,b,c,d: 853.3363 -144.08699 -840.467 460.6356
-Epoch 12000 : Training Cost: 3353544700.0  a,b,c,d: 864.19135 -225.8125 -975.4196 493.57703
-Epoch 13000 : Training Cost: 3323125000.0  a,b,c,d: 874.778 -305.98932 -1104.9867 540.39465
-Epoch 14000 : Training Cost: 3294257000.0  a,b,c,d: 885.1007 -384.63474 -1229.277 600.65607
-Epoch 15000 : Training Cost: 3266820000.0  a,b,c,d: 895.18823 -461.819 -1348.4417 673.9051
-Epoch 16000 : Training Cost: 3240736000.0  a,b,c,d: 905.0128 -537.541 -1462.6171 759.7118
-Epoch 17000 : Training Cost: 3215895000.0  a,b,c,d: 914.60065 -611.8676 -1571.9058 857.6638
-Epoch 18000 : Training Cost: 3192216800.0  a,b,c,d: 923.9603 -684.8093 -1676.4642 967.30475
-Epoch 19000 : Training Cost: 3169632300.0  a,b,c,d: 933.08594 -756.3582 -1776.4275 1088.2198
-Epoch 20000 : Training Cost: 3148046300.0  a,b,c,d: 941.9928 -826.6257 -1871.9355 1219.9702
-Epoch 21000 : Training Cost: 3127394800.0  a,b,c,d: 950.67896 -895.6205 -1963.0989 1362.1665
-Epoch 22000 : Training Cost: 3107608600.0  a,b,c,d: 959.1487 -963.38116 -2050.0586 1514.4026
-Epoch 23000 : Training Cost: 3088618200.0  a,b,c,d: 967.4355 -1029.9625 -2132.961 1676.2717
-Epoch 24000 : Training Cost: 3070361300.0  a,b,c,d: 975.52875 -1095.4292 -2211.854 1847.4485
-Epoch 25000 : Training Cost: 3052791300.0  a,b,c,d: 983.4346 -1159.7922 -2286.9412 2027.4857
+
Epoch 1000 : Training Cost: 4279814000.0  a,b,c,d: 670.1527 694.4212 751.4653 903.9527
+Epoch 2000 : Training Cost: 3770950400.0  a,b,c,d: 742.6414 666.3489 636.94525 859.2088
+Epoch 3000 : Training Cost: 3717708300.0  a,b,c,d: 756.2582 569.3339 448.105 748.23956
+Epoch 4000 : Training Cost: 3667464000.0  a,b,c,d: 769.4476 474.0318 265.5761 654.75525
+Epoch 5000 : Training Cost: 3620040700.0  a,b,c,d: 782.32324 380.54272 89.39888 578.5136
+Epoch 6000 : Training Cost: 3575265800.0  a,b,c,d: 794.8898 288.83356 -80.5215 519.13654
+Epoch 7000 : Training Cost: 3532972000.0  a,b,c,d: 807.1608 198.87044 -244.31102 476.2061
+Epoch 8000 : Training Cost: 3493009200.0  a,b,c,d: 819.13513 110.64169 -402.0677 449.3291
+Epoch 9000 : Training Cost: 3455228400.0  a,b,c,d: 830.80255 24.0964 -553.92804 438.0652
+Epoch 10000 : Training Cost: 3419475500.0  a,b,c,d: 842.21594 -60.797424 -700.0123 441.983
+Epoch 11000 : Training Cost: 3385625300.0  a,b,c,d: 853.3363 -144.08699 -840.467 460.6356
+Epoch 12000 : Training Cost: 3353544700.0  a,b,c,d: 864.19135 -225.8125 -975.4196 493.57703
+Epoch 13000 : Training Cost: 3323125000.0  a,b,c,d: 874.778 -305.98932 -1104.9867 540.39465
+Epoch 14000 : Training Cost: 3294257000.0  a,b,c,d: 885.1007 -384.63474 -1229.277 600.65607
+Epoch 15000 : Training Cost: 3266820000.0  a,b,c,d: 895.18823 -461.819 -1348.4417 673.9051
+Epoch 16000 : Training Cost: 3240736000.0  a,b,c,d: 905.0128 -537.541 -1462.6171 759.7118
+Epoch 17000 : Training Cost: 3215895000.0  a,b,c,d: 914.60065 -611.8676 -1571.9058 857.6638
+Epoch 18000 : Training Cost: 3192216800.0  a,b,c,d: 923.9603 -684.8093 -1676.4642 967.30475
+Epoch 19000 : Training Cost: 3169632300.0  a,b,c,d: 933.08594 -756.3582 -1776.4275 1088.2198
+Epoch 20000 : Training Cost: 3148046300.0  a,b,c,d: 941.9928 -826.6257 -1871.9355 1219.9702
+Epoch 21000 : Training Cost: 3127394800.0  a,b,c,d: 950.67896 -895.6205 -1963.0989 1362.1665
+Epoch 22000 : Training Cost: 3107608600.0  a,b,c,d: 959.1487 -963.38116 -2050.0586 1514.4026
+Epoch 23000 : Training Cost: 3088618200.0  a,b,c,d: 967.4355 -1029.9625 -2132.961 1676.2717
+Epoch 24000 : Training Cost: 3070361300.0  a,b,c,d: 975.52875 -1095.4292 -2211.854 1847.4485
+Epoch 25000 : Training Cost: 3052791300.0  a,b,c,d: 983.4346 -1159.7922 -2286.9412 2027.4857
 3052791300.0 983.4346 -1159.7922 -2286.9412 2027.4857
 

 
@@ -400,31 +400,31 @@ values using the X values. We then plot it to compare the actual data and predic
 print(training_cost, coefficient1, coefficient2, coefficient3, coefficient4, constant)
-
Epoch 1000 : Training Cost: 1902632600.0  a,b,c,d: 84.48304 52.210594 54.791424 142.51952 512.0343
-Epoch 2000 : Training Cost: 1854316200.0  a,b,c,d: 88.998955 13.073557 14.276088 223.55667 1056.4655
-Epoch 3000 : Training Cost: 1812812400.0  a,b,c,d: 92.9462 -22.331177 -15.262934 327.41858 1634.9054
-Epoch 4000 : Training Cost: 1775716000.0  a,b,c,d: 96.42522 -54.64535 -35.829437 449.5028 2239.1392
-Epoch 5000 : Training Cost: 1741494100.0  a,b,c,d: 99.524734 -84.43976 -49.181057 585.85876 2862.4915
-Epoch 6000 : Training Cost: 1709199600.0  a,b,c,d: 102.31984 -112.19895 -56.808075 733.1876 3499.6199
-Epoch 7000 : Training Cost: 1678261800.0  a,b,c,d: 104.87324 -138.32709 -59.9442 888.79626 4146.2944
-Epoch 8000 : Training Cost: 1648340600.0  a,b,c,d: 107.23536 -163.15173 -59.58964 1050.524 4798.979
-Epoch 9000 : Training Cost: 1619243400.0  a,b,c,d: 109.44742 -186.9409 -56.53944 1216.6432 5454.9463
-Epoch 10000 : Training Cost: 1590821900.0  a,b,c,d: 111.54233 -209.91287 -51.423084 1385.8513 6113.5137
-Epoch 11000 : Training Cost: 1563042200.0  a,b,c,d: 113.54405 -232.21953 -44.73371 1557.1084 6771.7046
-Epoch 12000 : Training Cost: 1535855600.0  a,b,c,d: 115.471565 -253.9838 -36.851135 1729.535 7429.069
-Epoch 13000 : Training Cost: 1509255300.0  a,b,c,d: 117.33939 -275.29697 -28.0714 1902.5308 8083.9634
-Epoch 14000 : Training Cost: 1483227000.0  a,b,c,d: 119.1605 -296.2472 -18.618649 2075.6094 8735.381
-Epoch 15000 : Training Cost: 1457726700.0  a,b,c,d: 120.94584 -316.915 -8.650095 2248.3247 9384.197
-Epoch 16000 : Training Cost: 1432777300.0  a,b,c,d: 122.69806 -337.30704 1.7027153 2420.5771 10028.871
-Epoch 17000 : Training Cost: 1408365000.0  a,b,c,d: 124.42179 -357.45245 12.33499 2592.2983 10669.157
-Epoch 18000 : Training Cost: 1384480000.0  a,b,c,d: 126.12332 -377.39734 23.168756 2763.0933 11305.027
-Epoch 19000 : Training Cost: 1361116800.0  a,b,c,d: 127.80568 -397.16415 34.160156 2933.0452 11935.669
-Epoch 20000 : Training Cost: 1338288100.0  a,b,c,d: 129.4674 -416.72803 45.259155 3101.7727 12561.179
-Epoch 21000 : Training Cost: 1315959700.0  a,b,c,d: 131.11403 -436.14285 56.4436 3269.3142 13182.058
-Epoch 22000 : Training Cost: 1294164700.0  a,b,c,d: 132.74377 -455.3779 67.6757 3435.3833 13796.807
-Epoch 23000 : Training Cost: 1272863600.0  a,b,c,d: 134.35779 -474.45316 78.96117 3600.264 14406.58
-Epoch 24000 : Training Cost: 1252052600.0  a,b,c,d: 135.9583 -493.38254 90.268616 3764.0078 15010.481
-Epoch 25000 : Training Cost: 1231713700.0  a,b,c,d: 137.54753 -512.1876 101.59372 3926.4897 15609.368
+
Epoch 1000 : Training Cost: 1902632600.0  a,b,c,d: 84.48304 52.210594 54.791424 142.51952 512.0343
+Epoch 2000 : Training Cost: 1854316200.0  a,b,c,d: 88.998955 13.073557 14.276088 223.55667 1056.4655
+Epoch 3000 : Training Cost: 1812812400.0  a,b,c,d: 92.9462 -22.331177 -15.262934 327.41858 1634.9054
+Epoch 4000 : Training Cost: 1775716000.0  a,b,c,d: 96.42522 -54.64535 -35.829437 449.5028 2239.1392
+Epoch 5000 : Training Cost: 1741494100.0  a,b,c,d: 99.524734 -84.43976 -49.181057 585.85876 2862.4915
+Epoch 6000 : Training Cost: 1709199600.0  a,b,c,d: 102.31984 -112.19895 -56.808075 733.1876 3499.6199
+Epoch 7000 : Training Cost: 1678261800.0  a,b,c,d: 104.87324 -138.32709 -59.9442 888.79626 4146.2944
+Epoch 8000 : Training Cost: 1648340600.0  a,b,c,d: 107.23536 -163.15173 -59.58964 1050.524 4798.979
+Epoch 9000 : Training Cost: 1619243400.0  a,b,c,d: 109.44742 -186.9409 -56.53944 1216.6432 5454.9463
+Epoch 10000 : Training Cost: 1590821900.0  a,b,c,d: 111.54233 -209.91287 -51.423084 1385.8513 6113.5137
+Epoch 11000 : Training Cost: 1563042200.0  a,b,c,d: 113.54405 -232.21953 -44.73371 1557.1084 6771.7046
+Epoch 12000 : Training Cost: 1535855600.0  a,b,c,d: 115.471565 -253.9838 -36.851135 1729.535 7429.069
+Epoch 13000 : Training Cost: 1509255300.0  a,b,c,d: 117.33939 -275.29697 -28.0714 1902.5308 8083.9634
+Epoch 14000 : Training Cost: 1483227000.0  a,b,c,d: 119.1605 -296.2472 -18.618649 2075.6094 8735.381
+Epoch 15000 : Training Cost: 1457726700.0  a,b,c,d: 120.94584 -316.915 -8.650095 2248.3247 9384.197
+Epoch 16000 : Training Cost: 1432777300.0  a,b,c,d: 122.69806 -337.30704 1.7027153 2420.5771 10028.871
+Epoch 17000 : Training Cost: 1408365000.0  a,b,c,d: 124.42179 -357.45245 12.33499 2592.2983 10669.157
+Epoch 18000 : Training Cost: 1384480000.0  a,b,c,d: 126.12332 -377.39734 23.168756 2763.0933 11305.027
+Epoch 19000 : Training Cost: 1361116800.0  a,b,c,d: 127.80568 -397.16415 34.160156 2933.0452 11935.669
+Epoch 20000 : Training Cost: 1338288100.0  a,b,c,d: 129.4674 -416.72803 45.259155 3101.7727 12561.179
+Epoch 21000 : Training Cost: 1315959700.0  a,b,c,d: 131.11403 -436.14285 56.4436 3269.3142 13182.058
+Epoch 22000 : Training Cost: 1294164700.0  a,b,c,d: 132.74377 -455.3779 67.6757 3435.3833 13796.807
+Epoch 23000 : Training Cost: 1272863600.0  a,b,c,d: 134.35779 -474.45316 78.96117 3600.264 14406.58
+Epoch 24000 : Training Cost: 1252052600.0  a,b,c,d: 135.9583 -493.38254 90.268616 3764.0078 15010.481
+Epoch 25000 : Training Cost: 1231713700.0  a,b,c,d: 137.54753 -512.1876 101.59372 3926.4897 15609.368
 1231713700.0 137.54753 -512.1876 101.59372 3926.4897 15609.368
 

 
@@ -460,31 +460,31 @@ values using the X values. We then plot it to compare the actual data and predic
         constant = sess.run(f)
-
Epoch 1000 : Training Cost: 1409200100.0  a,b,c,d,e,f: 7.949472 7.46219 55.626034 184.29028 484.00223 1024.0083
-Epoch 2000 : Training Cost: 1306882400.0  a,b,c,d,e,f: 8.732181 -4.0085897 73.25298 315.90103 904.08887 2004.9749
-Epoch 3000 : Training Cost: 1212606000.0  a,b,c,d,e,f: 9.732249 -16.90125 86.28379 437.06552 1305.055 2966.2188
-Epoch 4000 : Training Cost: 1123640400.0  a,b,c,d,e,f: 10.74851 -29.82692 98.59997 555.331 1698.4631 3917.9155
-Epoch 5000 : Training Cost: 1039694300.0  a,b,c,d,e,f: 11.75426 -42.598194 110.698326 671.64355 2085.5513 4860.8535
-Epoch 6000 : Training Cost: 960663550.0  a,b,c,d,e,f: 12.745439 -55.18337 122.644936 786.00214 2466.1638 5794.3735
-Epoch 7000 : Training Cost: 886438340.0  a,b,c,d,e,f: 13.721028 -67.57168 134.43822 898.3691 2839.9958 6717.659
-Epoch 8000 : Training Cost: 816913100.0  a,b,c,d,e,f: 14.679965 -79.75113 146.07385 1008.66895 3206.6692 7629.812
-Epoch 9000 : Training Cost: 751971500.0  a,b,c,d,e,f: 15.62181 -91.71608 157.55713 1116.7715 3565.8323 8529.976
-Epoch 10000 : Training Cost: 691508740.0  a,b,c,d,e,f: 16.545347 -103.4531 168.88321 1222.6348 3916.9785 9416.236
-Epoch 11000 : Training Cost: 635382000.0  a,b,c,d,e,f: 17.450052 -114.954254 180.03932 1326.1565 4259.842 10287.99
-Epoch 12000 : Training Cost: 583477250.0  a,b,c,d,e,f: 18.334944 -126.20821 191.02948 1427.2095 4593.8 11143.449
-Epoch 13000 : Training Cost: 535640400.0  a,b,c,d,e,f: 19.198917 -137.20206 201.84718 1525.6926 4918.5327 11981.633
-Epoch 14000 : Training Cost: 491722240.0  a,b,c,d,e,f: 20.041153 -147.92719 212.49709 1621.5496 5233.627 12800.468
-Epoch 15000 : Training Cost: 451559520.0  a,b,c,d,e,f: 20.860966 -158.37456 222.97133 1714.7141 5538.676 13598.337
-Epoch 16000 : Training Cost: 414988960.0  a,b,c,d,e,f: 21.657421 -168.53406 233.27422 1805.0874 5833.1978 14373.658
-Epoch 17000 : Training Cost: 381837920.0  a,b,c,d,e,f: 22.429693 -178.39536 243.39914 1892.5883 6116.847 15124.394
-Epoch 18000 : Training Cost: 351931300.0  a,b,c,d,e,f: 23.176882 -187.94789 253.3445 1977.137 6389.117 15848.417
-Epoch 19000 : Training Cost: 325074400.0  a,b,c,d,e,f: 23.898485 -197.18741 263.12512 2058.6716 6649.8037 16543.95
-Epoch 20000 : Training Cost: 301073570.0  a,b,c,d,e,f: 24.593851 -206.10497 272.72385 2137.1797 6898.544 17209.367
-Epoch 21000 : Training Cost: 279727000.0  a,b,c,d,e,f: 25.262104 -214.69217 282.14642 2212.6372 7135.217 17842.854
-Epoch 22000 : Training Cost: 260845550.0  a,b,c,d,e,f: 25.903376 -222.94969 291.4003 2284.9844 7359.4644 18442.408
-Epoch 23000 : Training Cost: 244218030.0  a,b,c,d,e,f: 26.517094 -230.8697 300.45532 2354.3003 7571.261 19007.49
-Epoch 24000 : Training Cost: 229660080.0  a,b,c,d,e,f: 27.102589 -238.44817 309.35342 2420.4185 7770.5728 19536.19
-Epoch 25000 : Training Cost: 216972400.0  a,b,c,d,e,f: 27.660324 -245.69016 318.10062 2483.3608 7957.354 20027.707
+
Epoch 1000 : Training Cost: 1409200100.0  a,b,c,d,e,f: 7.949472 7.46219 55.626034 184.29028 484.00223 1024.0083
+Epoch 2000 : Training Cost: 1306882400.0  a,b,c,d,e,f: 8.732181 -4.0085897 73.25298 315.90103 904.08887 2004.9749
+Epoch 3000 : Training Cost: 1212606000.0  a,b,c,d,e,f: 9.732249 -16.90125 86.28379 437.06552 1305.055 2966.2188
+Epoch 4000 : Training Cost: 1123640400.0  a,b,c,d,e,f: 10.74851 -29.82692 98.59997 555.331 1698.4631 3917.9155
+Epoch 5000 : Training Cost: 1039694300.0  a,b,c,d,e,f: 11.75426 -42.598194 110.698326 671.64355 2085.5513 4860.8535
+Epoch 6000 : Training Cost: 960663550.0  a,b,c,d,e,f: 12.745439 -55.18337 122.644936 786.00214 2466.1638 5794.3735
+Epoch 7000 : Training Cost: 886438340.0  a,b,c,d,e,f: 13.721028 -67.57168 134.43822 898.3691 2839.9958 6717.659
+Epoch 8000 : Training Cost: 816913100.0  a,b,c,d,e,f: 14.679965 -79.75113 146.07385 1008.66895 3206.6692 7629.812
+Epoch 9000 : Training Cost: 751971500.0  a,b,c,d,e,f: 15.62181 -91.71608 157.55713 1116.7715 3565.8323 8529.976
+Epoch 10000 : Training Cost: 691508740.0  a,b,c,d,e,f: 16.545347 -103.4531 168.88321 1222.6348 3916.9785 9416.236
+Epoch 11000 : Training Cost: 635382000.0  a,b,c,d,e,f: 17.450052 -114.954254 180.03932 1326.1565 4259.842 10287.99
+Epoch 12000 : Training Cost: 583477250.0  a,b,c,d,e,f: 18.334944 -126.20821 191.02948 1427.2095 4593.8 11143.449
+Epoch 13000 : Training Cost: 535640400.0  a,b,c,d,e,f: 19.198917 -137.20206 201.84718 1525.6926 4918.5327 11981.633
+Epoch 14000 : Training Cost: 491722240.0  a,b,c,d,e,f: 20.041153 -147.92719 212.49709 1621.5496 5233.627 12800.468
+Epoch 15000 : Training Cost: 451559520.0  a,b,c,d,e,f: 20.860966 -158.37456 222.97133 1714.7141 5538.676 13598.337
+Epoch 16000 : Training Cost: 414988960.0  a,b,c,d,e,f: 21.657421 -168.53406 233.27422 1805.0874 5833.1978 14373.658
+Epoch 17000 : Training Cost: 381837920.0  a,b,c,d,e,f: 22.429693 -178.39536 243.39914 1892.5883 6116.847 15124.394
+Epoch 18000 : Training Cost: 351931300.0  a,b,c,d,e,f: 23.176882 -187.94789 253.3445 1977.137 6389.117 15848.417
+Epoch 19000 : Training Cost: 325074400.0  a,b,c,d,e,f: 23.898485 -197.18741 263.12512 2058.6716 6649.8037 16543.95
+Epoch 20000 : Training Cost: 301073570.0  a,b,c,d,e,f: 24.593851 -206.10497 272.72385 2137.1797 6898.544 17209.367
+Epoch 21000 : Training Cost: 279727000.0  a,b,c,d,e,f: 25.262104 -214.69217 282.14642 2212.6372 7135.217 17842.854
+Epoch 22000 : Training Cost: 260845550.0  a,b,c,d,e,f: 25.903376 -222.94969 291.4003 2284.9844 7359.4644 18442.408
+Epoch 23000 : Training Cost: 244218030.0  a,b,c,d,e,f: 26.517094 -230.8697 300.45532 2354.3003 7571.261 19007.49
+Epoch 24000 : Training Cost: 229660080.0  a,b,c,d,e,f: 27.102589 -238.44817 309.35342 2420.4185 7770.5728 19536.19
+Epoch 25000 : Training Cost: 216972400.0  a,b,c,d,e,f: 27.660324 -245.69016 318.10062 2483.3608 7957.354 20027.707
 216972400.0 27.660324 -245.69016 318.10062 2483.3608 7957.354 20027.707
 

 
@@ -521,9 +521,14 @@ values using the X values. We then plot it to compare the actual data and predic
 
 
Basically if you train your machine learning model on a small dataset for a really large number of epochs, the model will learn all the deformities/noise in the data and will actually think that it is a normal part. Therefore when it will see some new data, it will discard that new data as noise and will impact the accuracy of the model in a negative manner

 
-	

+	
+	

+	    
+	

+	
 
 
 
diff --git a/docs/posts/2019-12-22-Fake-News-Detector.html b/docs/posts/2019-12-22-Fake-News-Detector.html
index a5158e3..46297b0 100644
--- a/docs/posts/2019-12-22-Fake-News-Detector.html
+++ b/docs/posts/2019-12-22-Fake-News-Detector.html
@@ -69,7 +69,7 @@ a CUDA compatible MXNet package.

 
 
Downloading the Dataset

 
-
!wget -q "https://github.com/joolsa/fake_real_news_dataset/raw/master/fake_or_real_news.csv.zip"
+
!wget -q "https://github.com/joolsa/fake_real_news_dataset/raw/master/fake_or_real_news.csv.zip"
 !unzip fake_or_real_news.csv.zip
 

 
@@ -273,9 +273,14 @@ DescriptionThe bag-of-words model is a simplifying representation used in NLP, i
 }
 

 
-	

+	
+	

+	    
+	

+	
 
 
 
diff --git a/docs/posts/2020-01-14-Converting-between-PIL-NumPy.html b/docs/posts/2020-01-14-Converting-between-PIL-NumPy.html
index e8b802f..293da91 100644
--- a/docs/posts/2020-01-14-Converting-between-PIL-NumPy.html
+++ b/docs/posts/2020-01-14-Converting-between-PIL-NumPy.html
@@ -63,9 +63,14 @@
     img.save(destination, "JPEG", quality=80, optimize=True, progressive=True)
-
+ +
+ +
+ diff --git a/docs/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab.html b/docs/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab.html index 5675502..9a7faef 100644 --- a/docs/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab.html +++ b/docs/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab.html @@ -83,9 +83,14 @@

Voila! You can now download Kaggle datasets

-
+ +
+ +
+ diff --git a/docs/posts/2020-01-16-Image-Classifier-Using-Turicreate.html b/docs/posts/2020-01-16-Image-Classifier-Using-Turicreate.html index a9f60ea..4235b29 100644 --- a/docs/posts/2020-01-16-Image-Classifier-Using-Turicreate.html +++ b/docs/posts/2020-01-16-Image-Classifier-Using-Turicreate.html @@ -160,39 +160,39 @@

\

-
+-------------------------+------------------------+
-|           path          |         image          |
-+-------------------------+------------------------+
-|  ./train/default/1.jpg  | Height: 224 Width: 224 |
-|  ./train/default/10.jpg | Height: 224 Width: 224 |
-| ./train/default/100.jpg | Height: 224 Width: 224 |
-| ./train/default/101.jpg | Height: 224 Width: 224 |
-| ./train/default/102.jpg | Height: 224 Width: 224 |
-| ./train/default/103.jpg | Height: 224 Width: 224 |
-| ./train/default/104.jpg | Height: 224 Width: 224 |
-| ./train/default/105.jpg | Height: 224 Width: 224 |
-| ./train/default/106.jpg | Height: 224 Width: 224 |
-| ./train/default/107.jpg | Height: 224 Width: 224 |
-+-------------------------+------------------------+
+
+-------------------------+------------------------+
+|           path          |         image          |
++-------------------------+------------------------+
+|  ./train/default/1.jpg  | Height: 224 Width: 224 |
+|  ./train/default/10.jpg | Height: 224 Width: 224 |
+| ./train/default/100.jpg | Height: 224 Width: 224 |
+| ./train/default/101.jpg | Height: 224 Width: 224 |
+| ./train/default/102.jpg | Height: 224 Width: 224 |
+| ./train/default/103.jpg | Height: 224 Width: 224 |
+| ./train/default/104.jpg | Height: 224 Width: 224 |
+| ./train/default/105.jpg | Height: 224 Width: 224 |
+| ./train/default/106.jpg | Height: 224 Width: 224 |
+| ./train/default/107.jpg | Height: 224 Width: 224 |
++-------------------------+------------------------+
 [2028 rows x 2 columns]
-Note: Only the head of the SFrame is printed.
+Note: Only the head of the SFrame is printed.
 You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
 +-------------------------+------------------------+---------+
-|           path          |         image          |  label  |
-+-------------------------+------------------------+---------+
-|  ./train/default/1.jpg  | Height: 224 Width: 224 | default |
-|  ./train/default/10.jpg | Height: 224 Width: 224 | default |
-| ./train/default/100.jpg | Height: 224 Width: 224 | default |
-| ./train/default/101.jpg | Height: 224 Width: 224 | default |
-| ./train/default/102.jpg | Height: 224 Width: 224 | default |
-| ./train/default/103.jpg | Height: 224 Width: 224 | default |
-| ./train/default/104.jpg | Height: 224 Width: 224 | default |
-| ./train/default/105.jpg | Height: 224 Width: 224 | default |
-| ./train/default/106.jpg | Height: 224 Width: 224 | default |
-| ./train/default/107.jpg | Height: 224 Width: 224 | default |
-+-------------------------+------------------------+---------+
+|           path          |         image          |  label  |
++-------------------------+------------------------+---------+
+|  ./train/default/1.jpg  | Height: 224 Width: 224 | default |
+|  ./train/default/10.jpg | Height: 224 Width: 224 | default |
+| ./train/default/100.jpg | Height: 224 Width: 224 | default |
+| ./train/default/101.jpg | Height: 224 Width: 224 | default |
+| ./train/default/102.jpg | Height: 224 Width: 224 | default |
+| ./train/default/103.jpg | Height: 224 Width: 224 | default |
+| ./train/default/104.jpg | Height: 224 Width: 224 | default |
+| ./train/default/105.jpg | Height: 224 Width: 224 | default |
+| ./train/default/106.jpg | Height: 224 Width: 224 | default |
+| ./train/default/107.jpg | Height: 224 Width: 224 | default |
++-------------------------+------------------------+---------+
 [2028 rows x 3 columns]
-Note: Only the head of the SFrame is printed.
+Note: Only the head of the SFrame is printed.
 You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
 

 
@@ -252,28 +252,28 @@
 Completed 1536/1633
 Completed 1600/1633
 Completed 1633/1633
-PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
-          You can set ``validation_set=None`` to disable validation tracking.
-
-Logistic regression:
---------------------------------------------------------
-Number of examples          : 1551
-Number of classes           : 3
-Number of feature columns   : 1
-Number of unpacked features : 2048
-Number of coefficients      : 4098
+PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
+          You can set ``validation_set=None`` to disable validation tracking.
+
+Logistic regression:
+--------------------------------------------------------
+Number of examples          : 1551
+Number of classes           : 3
+Number of feature columns   : 1
+Number of unpacked features : 2048
+Number of coefficients      : 4098
 Starting L-BFGS
 --------------------------------------------------------
 +-----------+----------+-----------+--------------+-------------------+---------------------+
-| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
-+-----------+----------+-----------+--------------+-------------------+---------------------+
-| 0         | 6        | 0.018611  | 0.891830     | 0.553836          | 0.560976            |
-| 1         | 10       | 0.390832  | 1.622383     | 0.744681          | 0.792683            |
-| 2         | 11       | 0.488541  | 1.943987     | 0.733075          | 0.804878            |
-| 3         | 14       | 2.442703  | 2.512545     | 0.727917          | 0.841463            |
-| 4         | 15       | 2.442703  | 2.826964     | 0.861380          | 0.853659            |
-| 9         | 28       | 2.340435  | 5.492035     | 0.941328          | 0.975610            |
-+-----------+----------+-----------+--------------+-------------------+---------------------+
+| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
++-----------+----------+-----------+--------------+-------------------+---------------------+
+| 0         | 6        | 0.018611  | 0.891830     | 0.553836          | 0.560976            |
+| 1         | 10       | 0.390832  | 1.622383     | 0.744681          | 0.792683            |
+| 2         | 11       | 0.488541  | 1.943987     | 0.733075          | 0.804878            |
+| 3         | 14       | 2.442703  | 2.512545     | 0.727917          | 0.841463            |
+| 4         | 15       | 2.442703  | 2.826964     | 0.861380          | 0.853659            |
+| 9         | 28       | 2.340435  | 5.492035     | 0.941328          | 0.975610            |
++-----------+----------+-----------+--------------+-------------------+---------------------+
 Performing feature extraction on resized images...
 Completed  64/395
 Completed 128/395
@@ -287,9 +287,14 @@
 
 
We just got an accuracy of 94% on Training Data and 97% on Validation Data!

 
-	

+	
+	

+	    
+	

+	
 
 
 
diff --git a/docs/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal.html b/docs/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal.html
index f915e48..55329f5 100644
--- a/docs/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal.html
+++ b/docs/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal.html
@@ -63,9 +63,14 @@
 
 
To Exit out of bluetoothctl anytime, just type exit 

 
-	

+	
+	

+	    
+	

+	
 
 
 
diff --git a/docs/posts/2020-03-03-Playing-With-Android-TV.html b/docs/posts/2020-03-03-Playing-With-Android-TV.html
index 1e854f9..f077896 100644
--- a/docs/posts/2020-03-03-Playing-With-Android-TV.html
+++ b/docs/posts/2020-03-03-Playing-With-Android-TV.html
@@ -114,9 +114,14 @@
 
  • adb uninstall com.company.yourpackagename
    • 
 
 
-	
    
+	
+	
    
+	    
+	
    
+	
 
 
 
diff --git a/docs/posts/2020-03-08-Making-Vaporwave-Track.html b/docs/posts/2020-03-08-Making-Vaporwave-Track.html
index 01b39d2..401a4b3 100644
--- a/docs/posts/2020-03-08-Making-Vaporwave-Track.html
+++ b/docs/posts/2020-03-08-Making-Vaporwave-Track.html
@@ -73,9 +73,14 @@
 
 
    The fact that there are steps on producing Vaporwave, this gave me the idea that Vaporwave can actually be made using programming, stay tuned for when I publish the program which I am working on ( Generating A E S T H E T I C artwork and remixes)
    
 
-	
    
+	
+	
    
+	    
+	
    
+	
 
 
 
diff --git a/docs/posts/2020-04-13-Fixing-X11-Error-AmberTools-macOS.html b/docs/posts/2020-04-13-Fixing-X11-Error-AmberTools-macOS.html
index 89203c2..3a26cf9 100644
--- a/docs/posts/2020-04-13-Fixing-X11-Error-AmberTools-macOS.html
+++ b/docs/posts/2020-04-13-Fixing-X11-Error-AmberTools-macOS.html
@@ -72,9 +72,14 @@ Configure failed due to the errors above!
 
 
    If you do not have XQuartz installed, you need to run brew cask install xquartz
    
 
-	
    
+	
+	
    
+	    
+	
    
+	
 
 
 
diff --git a/docs/posts/2020-05-31-compiling-open-babel-on-ios.html b/docs/posts/2020-05-31-compiling-open-babel-on-ios.html
index a631c17..0d2dd2f 100644
--- a/docs/posts/2020-05-31-compiling-open-babel-on-ios.html
+++ b/docs/posts/2020-05-31-compiling-open-babel-on-ios.html
@@ -158,9 +158,14 @@ export BABEL_LIBDIR="/usr/lib/openbabel/3.1.0"
 
 
    Edit 1: Added Screenshots, had to replicate the errors.
    
 
-	
    
+	
+	
    
+	    
+	
    
+	
 
 
 
diff --git a/docs/posts/2020-06-01-Speeding-Up-Molecular-Docking-Workflow-AutoDock-Vina-and-PyMOL.html b/docs/posts/2020-06-01-Speeding-Up-Molecular-Docking-Workflow-AutoDock-Vina-and-PyMOL.html
index ad72e96..5542120 100644
--- a/docs/posts/2020-06-01-Speeding-Up-Molecular-Docking-Workflow-AutoDock-Vina-and-PyMOL.html
+++ b/docs/posts/2020-06-01-Speeding-Up-Molecular-Docking-Workflow-AutoDock-Vina-and-PyMOL.html
@@ -87,9 +87,14 @@ alias pbpaste='xclip -selection clipboard -o'
 
 
    This is just the docking command for AutoDock Vina. In the next part I will tell how to use PyMOL and a plugin to directly generate the coordinates in Vina format --center_x -9.7 --center_y 11.4 --center_z 68.9 --size_x 19.3 --size_y 29.9 --size_z 21.3 without needing to type them manually.
    
 
-	
    
+	
+	
    
+	    
+	
    
+	
 
 
 
diff --git a/docs/posts/2020-06-02-Compiling-AutoDock-Vina-on-iOS.html b/docs/posts/2020-06-02-Compiling-AutoDock-Vina-on-iOS.html
index 63bd2d2..5adc193 100644
--- a/docs/posts/2020-06-02-Compiling-AutoDock-Vina-on-iOS.html
+++ b/docs/posts/2020-06-02-Compiling-AutoDock-Vina-on-iOS.html
@@ -124,9 +124,14 @@ return path(str, boost::filesystem::native);
 
 
    The package is available on my repository and only depends on boost. ( Both, Vina and Vina-Split are part of the package)
    
 
-	
    
+	
+	
    
+	    
+	
    
+	
 
 
 
diff --git a/docs/posts/2020-07-01-Install-rdkit-colab.html b/docs/posts/2020-07-01-Install-rdkit-colab.html
index bf1da97..56e2f21 100644
--- a/docs/posts/2020-07-01-Install-rdkit-colab.html
+++ b/docs/posts/2020-07-01-Install-rdkit-colab.html
@@ -76,76 +76,83 @@
         rdkit_version=None,
         add_python_path=True,
         force=False):
-    """install rdkit from miniconda
-    ```
-    import rdkit_installer
-    rdkit_installer.install()
-    ```
-    """
-
-    python_path = os.path.join(
-        conda_path,
-        "lib",
-        "python{0}.{1}".format(*sys.version_info),
-        "site-packages",
-    )
-
-    if add_python_path and python_path not in sys.path:
-        logger.info("add {} to PYTHONPATH".format(python_path))
-        sys.path.append(python_path)
-
-    if os.path.isdir(os.path.join(python_path, "rdkit")):
-        logger.info("rdkit is already installed")
-        if not force:
-            return
-
-        logger.info("force re-install")
-
-    url = url_base + file_name
-    python_version = "{0}.{1}.{2}".format(*sys.version_info)
-
-    logger.info("python version: {}".format(python_version))
-
-    if os.path.isdir(conda_path):
-        logger.warning("remove current miniconda")
-        shutil.rmtree(conda_path)
-    elif os.path.isfile(conda_path):
-        logger.warning("remove {}".format(conda_path))
-        os.remove(conda_path)
-
-    logger.info('fetching installer from {}'.format(url))
-    res = requests.get(url, stream=True)
-    res.raise_for_status()
-    with open(file_name, 'wb') as f:
-        for chunk in res.iter_content(chunk_size):
-            f.write(chunk)
-    logger.info('done')
-
-    logger.info('installing miniconda to {}'.format(conda_path))
-    subprocess.check_call(["bash", file_name, "-b", "-p", conda_path])
-    logger.info('done')
-
-    logger.info("installing rdkit")
-    subprocess.check_call([
-        os.path.join(conda_path, "bin", "conda"),
-        "install",
-        "--yes",
-        "-c", "rdkit",
-        "python=={}".format(python_version),
-        "rdkit" if rdkit_version is None else "rdkit=={}".format(rdkit_version)])
-    logger.info("done")
-
-    import rdkit
-    logger.info("rdkit-{} installation finished!".format(rdkit.__version__))
-
-
-if __name__ == "__main__":
-    install()
+    """install rdkit from miniconda
+
    -
    +
    import rdkit_installer
+rdkit_installer.install()
+```
+"""
+
+python_path = os.path.join(
+    conda_path,
+    "lib",
+    "python{0}.{1}".format(*sys.version_info),
+    "site-packages",
+)
+
+if add_python_path and python_path not in sys.path:
+    logger.info("add {} to PYTHONPATH".format(python_path))
+    sys.path.append(python_path)
+
+if os.path.isdir(os.path.join(python_path, "rdkit")):
+    logger.info("rdkit is already installed")
+    if not force:
+        return
+
+    logger.info("force re-install")
+
+url = url_base + file_name
+python_version = "{0}.{1}.{2}".format(*sys.version_info)
+
+logger.info("python version: {}".format(python_version))
+
+if os.path.isdir(conda_path):
+    logger.warning("remove current miniconda")
+    shutil.rmtree(conda_path)
+elif os.path.isfile(conda_path):
+    logger.warning("remove {}".format(conda_path))
+    os.remove(conda_path)
+
+logger.info('fetching installer from {}'.format(url))
+res = requests.get(url, stream=True)
+res.raise_for_status()
+with open(file_name, 'wb') as f:
+    for chunk in res.iter_content(chunk_size):
+        f.write(chunk)
+logger.info('done')
+
+logger.info('installing miniconda to {}'.format(conda_path))
+subprocess.check_call(["bash", file_name, "-b", "-p", conda_path])
+logger.info('done')
+
+logger.info("installing rdkit")
+subprocess.check_call([
+    os.path.join(conda_path, "bin", "conda"),
+    "install",
+    "--yes",
+    "-c", "rdkit",
+    "python=={}".format(python_version),
+    "rdkit" if rdkit_version is None else "rdkit=={}".format(rdkit_version)])
+logger.info("done")
+
+import rdkit
+logger.info("rdkit-{} installation finished!".format(rdkit.__version__))
+
    + +

    if name == "main": + install() +```

    + + +
    + +
    + diff --git a/docs/posts/2020-08-01-Natural-Feature-Tracking-ARJS.html b/docs/posts/2020-08-01-Natural-Feature-Tracking-ARJS.html index 711b3ea..6b28206 100644 --- a/docs/posts/2020-08-01-Natural-Feature-Tracking-ARJS.html +++ b/docs/posts/2020-08-01-Natural-Feature-Tracking-ARJS.html @@ -187,25 +187,25 @@ me.fset me.fset3 me.iset 
    <script src="https://cdn.jsdelivr.net/gh/aframevr/aframe@1c2407b26c61958baa93967b5412487cd94b290b/dist/aframe-master.min.js"></script>
 <script src="https://raw.githack.com/AR-js-org/AR.js/master/aframe/build/aframe-ar-nft.js"></script>
 
-<style>
-  .arjs-loader {
-    height: 100%;
-    width: 100%;
-    position: absolute;
-    top: 0;
-    left: 0;
-    background-color: rgba(0, 0, 0, 0.8);
-    z-index: 9999;
-    display: flex;
-    justify-content: center;
-    align-items: center;
-  }
-
-  .arjs-loader div {
-    text-align: center;
-    font-size: 1.25em;
-    color: white;
-  }
+<style>
+  .arjs-loader {
+    height: 100%;
+    width: 100%;
+    position: absolute;
+    top: 0;
+    left: 0;
+    background-color: rgba(0, 0, 0, 0.8);
+    z-index: 9999;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+  }
+
+  .arjs-loader div {
+    text-align: center;
+    font-size: 1.25em;
+    color: white;
+  }
 </style>
 
 <body style="margin : 0px; overflow: hidden;">
@@ -318,9 +318,14 @@ Serving HTTP on 0.0.0.0 port 8000 ...
 
 
    
 
-	
    
+	
+	
    
+	    
+	
    
+	
 
 
 
diff --git a/docs/posts/2020-10-11-macOS-Virtual-Cam-OBS.html b/docs/posts/2020-10-11-macOS-Virtual-Cam-OBS.html
index 7a663e8..06951dc 100644
--- a/docs/posts/2020-10-11-macOS-Virtual-Cam-OBS.html
+++ b/docs/posts/2020-10-11-macOS-Virtual-Cam-OBS.html
@@ -149,9 +149,14 @@ new Dics({
 });
 
 
-	
    
+	
+	
    
+	    
+	
    
+	
 
 
 
diff --git a/docs/posts/2020-11-17-Lets-Encrypt-DuckDns.html b/docs/posts/2020-11-17-Lets-Encrypt-DuckDns.html
index b58d0fc..f8e7b6c 100644
--- a/docs/posts/2020-11-17-Lets-Encrypt-DuckDns.html
+++ b/docs/posts/2020-11-17-Lets-Encrypt-DuckDns.html
@@ -109,9 +109,14 @@ navanspi.duckdns.org.    60    IN    TXT
    + +
    + +
    + diff --git a/docs/posts/2020-12-1-HTML-JS-RSS-Feed.html b/docs/posts/2020-12-1-HTML-JS-RSS-Feed.html index 2f6f70c..4fdb015 100644 --- a/docs/posts/2020-12-1-HTML-JS-RSS-Feed.html +++ b/docs/posts/2020-12-1-HTML-JS-RSS-Feed.html @@ -66,185 +66,190 @@ </main> <script src="https://gitcdn.xyz/repo/rbren/rss-parser/master/dist/rss-parser.js"></script> -<script> - -const feeds = { - "BuzzFeed - India": { - "link":"https://www.buzzfeed.com/in.xml", - "summary":true - }, - "New Yorker": { - "link":"http://www.newyorker.com/feed/news", - }, - "Vox":{ - "link":"https://www.vox.com/rss/index.xml", - "limit": 3 - }, - "r/Jokes":{ - "link":"https://reddit.com/r/Jokes/hot/.rss?sort=hot", - "ignore": ["repost","discord"] - } -} - -const config_extra = { -"Responsive-Images": true, -"direct-link": false, -"show-date":false, -"left-column":false, -"defaults": { - "limit": 5, - "summary": true -} -} - -const CORS_PROXY = "https://cors-anywhere.herokuapp.com/" - -var contents_title = document.createElement("h2") -contents_title.textContent = "Contents" -contents_title.classList.add("pb-1") -document.getElementById("contents").appendChild(contents_title) - -async function myfunc(key){ - - var count_lim = feeds[key]["limit"] - var count_lim = (count_lim === undefined) ? config_extra["defaults"]["limit"] : count_lim - - var show_summary = feeds[key]["summary"] - var show_summary = (show_summary === undefined) ? config_extra["defaults"]["summary"] : show_summary - - var ignore_tags = feeds[key]["ignore"] - var ignore_tags = (ignore_tags === undefined) ? [] : ignore_tags - - var contents = document.createElement("a") - contents.href = "#" + key - contents.classList.add("list-group-item","list-group-item-action") - contents.textContent = key - document.getElementById("contents").appendChild(contents) - var feed_div = document.createElement("div") - feed_div.id = key - feed_div.setAttribute("id", key); - var title = document.createElement("h2"); - title.textContent = "From " + key; - title.classList.add("pb-1") - feed_div.appendChild(title) - document.getElementById("feed").appendChild(feed_div) - var parser = new RSSParser(); - var countPosts = 0 - parser.parseURL(CORS_PROXY + feeds[key]["link"], function(err, feed) { - if (err) throw err; - feed.items.forEach(function(entry) { - if (countPosts < count_lim) { - - var skip = false - for(var i = 0; i < ignore_tags.length; i++) { - if (entry.title.includes(ignore_tags[i])){ - var skip = true - } else if (entry.content.includes(ignore_tags[i])){ - var skip = true - } - } - - if (!skip) { - - var node = document.createElement("div"); - node.classList.add("card","mb-3"); - var row = document.createElement("div") - row.classList.add("row","no-gutters") - - if (config_extra["left-column"]){ - var left_col = document.createElement("div") - left_col.classList.add("col-md-2") - var left_col_body = document.createElement("div") - left_col_body.classList.add("card-body") - } - - var right_col = document.createElement("div") - if (config_extra["left-column"]){ - right_col.classList.add("col-md-10") - } - var node_title = document.createElement("h5") - - node_title.classList.add("card-header") - node_title.innerHTML = entry.title - - node_body = document.createElement("div") - node_body.classList.add("card-body") - - node_content = document.createElement("p") - - if (show_summary){ - node_content.innerHTML = entry.content - } - node_content.classList.add("card-text") - - if (config_extra["direct-link"]){ - node_link = document.createElement("p") - node_link.classList.add("card-text") - node_link.innerHTML = "<b>Link:</b> <a href='" + entry.link +"'>Direct Link</a>" - if (config_extra["left-column"]){ - left_col_body.appendChild(node_link) - } else { - node_content.appendChild(node_link) - } - } - - if (config_extra["show-date"]){ - node_date = document.createElement("p") - node_date.classList.add("card-text") - node_date.innerHTML = "<p><b>Date: </b>" + entry.pubDate + "</p>" - if (config_extra["left-column"]){ - left_col_body.appendChild(node_date) - } else { - node_content.appendChild(node_date) - - } - } - - node.appendChild(node_title) - - node_body.appendChild(node_content) - - right_col.appendChild(node_body) - - if (config_extra["left-column"]){ - left_col.appendChild(left_col_body) - row.appendChild(left_col) - } - - row.appendChild(right_col) - - node.appendChild(row) - - document.getElementById(key).appendChild(node) - countPosts+=1 - } - } - }) - - if (config_extra["Responsive-Images"]){ - var inputs = document.getElementsByTagName('img') - for(var i = 0; i < inputs.length; i++) { - inputs[i].classList.add("img-fluid") - } - } - - }) - - return true -} -(async () => { -for(var key in feeds) { - let result = await myfunc(key); -}})(); +<script> + +const feeds = { + "BuzzFeed - India": { + "link":"https://www.buzzfeed.com/in.xml", + "summary":true + }, + "New Yorker": { + "link":"http://www.newyorker.com/feed/news", + }, + "Vox":{ + "link":"https://www.vox.com/rss/index.xml", + "limit": 3 + }, + "r/Jokes":{ + "link":"https://reddit.com/r/Jokes/hot/.rss?sort=hot", + "ignore": ["repost","discord"] + } +} + +const config_extra = { +"Responsive-Images": true, +"direct-link": false, +"show-date":false, +"left-column":false, +"defaults": { + "limit": 5, + "summary": true +} +} + +const CORS_PROXY = "https://cors-anywhere.herokuapp.com/" + +var contents_title = document.createElement("h2") +contents_title.textContent = "Contents" +contents_title.classList.add("pb-1") +document.getElementById("contents").appendChild(contents_title) + +async function myfunc(key){ + + var count_lim = feeds[key]["limit"] + var count_lim = (count_lim === undefined) ? config_extra["defaults"]["limit"] : count_lim + + var show_summary = feeds[key]["summary"] + var show_summary = (show_summary === undefined) ? config_extra["defaults"]["summary"] : show_summary + + var ignore_tags = feeds[key]["ignore"] + var ignore_tags = (ignore_tags === undefined) ? [] : ignore_tags + + var contents = document.createElement("a") + contents.href = "#" + key + contents.classList.add("list-group-item","list-group-item-action") + contents.textContent = key + document.getElementById("contents").appendChild(contents) + var feed_div = document.createElement("div") + feed_div.id = key + feed_div.setAttribute("id", key); + var title = document.createElement("h2"); + title.textContent = "From " + key; + title.classList.add("pb-1") + feed_div.appendChild(title) + document.getElementById("feed").appendChild(feed_div) + var parser = new RSSParser(); + var countPosts = 0 + parser.parseURL(CORS_PROXY + feeds[key]["link"], function(err, feed) { + if (err) throw err; + feed.items.forEach(function(entry) { + if (countPosts < count_lim) { + + var skip = false + for(var i = 0; i < ignore_tags.length; i++) { + if (entry.title.includes(ignore_tags[i])){ + var skip = true + } else if (entry.content.includes(ignore_tags[i])){ + var skip = true + } + } + + if (!skip) { + + var node = document.createElement("div"); + node.classList.add("card","mb-3"); + var row = document.createElement("div") + row.classList.add("row","no-gutters") + + if (config_extra["left-column"]){ + var left_col = document.createElement("div") + left_col.classList.add("col-md-2") + var left_col_body = document.createElement("div") + left_col_body.classList.add("card-body") + } + + var right_col = document.createElement("div") + if (config_extra["left-column"]){ + right_col.classList.add("col-md-10") + } + var node_title = document.createElement("h5") + + node_title.classList.add("card-header") + node_title.innerHTML = entry.title + + node_body = document.createElement("div") + node_body.classList.add("card-body") + + node_content = document.createElement("p") + + if (show_summary){ + node_content.innerHTML = entry.content + } + node_content.classList.add("card-text") + + if (config_extra["direct-link"]){ + node_link = document.createElement("p") + node_link.classList.add("card-text") + node_link.innerHTML = "<b>Link:</b> <a href='" + entry.link +"'>Direct Link</a>" + if (config_extra["left-column"]){ + left_col_body.appendChild(node_link) + } else { + node_content.appendChild(node_link) + } + } + + if (config_extra["show-date"]){ + node_date = document.createElement("p") + node_date.classList.add("card-text") + node_date.innerHTML = "<p><b>Date: </b>" + entry.pubDate + "</p>" + if (config_extra["left-column"]){ + left_col_body.appendChild(node_date) + } else { + node_content.appendChild(node_date) + + } + } + + node.appendChild(node_title) + + node_body.appendChild(node_content) + + right_col.appendChild(node_body) + + if (config_extra["left-column"]){ + left_col.appendChild(left_col_body) + row.appendChild(left_col) + } + + row.appendChild(right_col) + + node.appendChild(row) + + document.getElementById(key).appendChild(node) + countPosts+=1 + } + } + }) + + if (config_extra["Responsive-Images"]){ + var inputs = document.getElementsByTagName('img') + for(var i = 0; i < inputs.length; i++) { + inputs[i].classList.add("img-fluid") + } + } + + }) + + return true +} +(async () => { +for(var key in feeds) { + let result = await myfunc(key); +}})(); </script> <noscript>Uh Oh! Your browser does not support JavaScript or JavaScript is currently disabled. Please enable JavaScript or switch to a different browser.</noscript> </body></html>     -
    + +
    + +
    + diff --git a/docs/posts/2021-06-25-Blog2Twitter-P1.html b/docs/posts/2021-06-25-Blog2Twitter-P1.html index 6d92c75..ada9666 100644 --- a/docs/posts/2021-06-25-Blog2Twitter-P1.html +++ b/docs/posts/2021-06-25-Blog2Twitter-P1.html @@ -136,9 +136,14 @@ I am not handling lists or images right now.

    For the next part, I will try to append the code as well. I actually added the code to this post after running the program.

    -
    + +
    + +
    + diff --git a/docs/posts/2021-06-25-NFC-Music-Cards-Basic-iOS.html b/docs/posts/2021-06-25-NFC-Music-Cards-Basic-iOS.html index d2d5e13..017df87 100644 --- a/docs/posts/2021-06-25-NFC-Music-Cards-Basic-iOS.html +++ b/docs/posts/2021-06-25-NFC-Music-Cards-Basic-iOS.html @@ -70,9 +70,14 @@ So, I did not have to ensure this could work with any device. I settled with usi -
    + +
    + +
    + diff --git a/docs/posts/2021-06-26-Cheminformatics-On-The-Web-2021.html b/docs/posts/2021-06-26-Cheminformatics-On-The-Web-2021.html index 1743268..b001d2f 100644 --- a/docs/posts/2021-06-26-Cheminformatics-On-The-Web-2021.html +++ b/docs/posts/2021-06-26-Cheminformatics-On-The-Web-2021.html @@ -123,9 +123,14 @@ Hopefully, this encourages you to explore the world of cheminformatics on the we

    Getting Started with RDKit-JS

    -
    + +
    + +
    + diff --git a/docs/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html b/docs/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html index 0eea323..0b307fd 100644 --- a/docs/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html +++ b/docs/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html @@ -89,20 +89,20 @@ I created a sample JSON with only 3 examples (I know, very less, but works for a

    Screenshot of Sample Dataset

    -
    [
-    {
-        "tokens": ["Tell","me","about","the","drug","Aspirin","."],
-        "labels": ["NONE","NONE","NONE","NONE","NONE","COMPOUND","NONE"]
-    },
-    {
-        "tokens": ["Please","tell","me","information","about","the","compound","salicylic","acid","."],
-        "labels": ["NONE","NONE","NONE","NONE","NONE","NONE","NONE","COMPOUND","COMPOUND","NONE"]
-    },
-    {
-        "tokens": ["Information","about","the","compound","Ibuprofen","please","."],
-        "labels": ["NONE","NONE","NONE","NONE","COMPOUND","NONE","NONE"]
-    }
-]
+
    [
+    {
+        "tokens": ["Tell","me","about","the","drug","Aspirin","."],
+        "labels": ["NONE","NONE","NONE","NONE","NONE","COMPOUND","NONE"]
+    },
+    {
+        "tokens": ["Please","tell","me","information","about","the","compound","salicylic","acid","."],
+        "labels": ["NONE","NONE","NONE","NONE","NONE","NONE","NONE","COMPOUND","COMPOUND","NONE"]
+    },
+    {
+        "tokens": ["Information","about","the","compound","Ibuprofen","please","."],
+        "labels": ["NONE","NONE","NONE","NONE","COMPOUND","NONE","NONE"]
+    }
+]
 
    
 
 
    Screenshot of Create ML Text Classifier
    
@@ -208,9 +208,14 @@ Otherwise, it calls the custom action.
    
 
 
    If I ever release a part-2, it will either be about implementing this in Tensorflow.JS or an iOS app using SwiftUI ;)
    
 
-	
    
+	
+	
    
+	    
+	
    
+	
 
 
 
diff --git a/docs/posts/2022-05-21-Similar-Movies-Recommender.html b/docs/posts/2022-05-21-Similar-Movies-Recommender.html
index 2e2fb6b..5d2d6fe 100644
--- a/docs/posts/2022-05-21-Similar-Movies-Recommender.html
+++ b/docs/posts/2022-05-21-Similar-Movies-Recommender.html
@@ -364,7 +364,8 @@ It is possible that this additional step of mapping could be avoided by storing
 
 
    Output:
    
 
-
    [55786, 18374, 299592, 662622, 6054, 227458, 139687, 303950, 70000, 129307, 70823, 5766, 23950, 137696, 655723, 32842, 413269, 145994, 197990, 373832]
+
    55786
+[55786, 18374, 299592, 662622, 6054, 227458, 139687, 303950, 70000, 129307, 70823, 5766, 23950, 137696, 655723, 32842, 413269, 145994, 197990, 373832]
 Now You See Me (2013): An FBI agent and an Interpol detective track a team of illusionists who pull off bank heists during their performances and reward their audiences with the money.
 Trapped (1949): U.S. Treasury Department agents go after a ring of counterfeiters.
 Brute Sanity (2018): An FBI-trained neuropsychologist teams up with a thief to find a reality-altering device while her insane ex-boss unleashes bizarre traps to stop her.
@@ -434,9 +435,14 @@ Spies (2015): A secret agent must perform a heist without time on his side
 
  • Filter based on popularity: The data already exists in the indexed database
    • 
 
 
-	
    
+	
+	
    
+	    
+	
    
+	
 
 
 
diff --git a/docs/posts/2022-08-05-Why-You-No-Host.html b/docs/posts/2022-08-05-Why-You-No-Host.html
index 8521fda..f325c3a 100644
--- a/docs/posts/2022-08-05-Why-You-No-Host.html
+++ b/docs/posts/2022-08-05-Why-You-No-Host.html
@@ -220,9 +220,14 @@
 
 
    Highly context dependent. I run two YunoHost servers in two different locations. One of the ISP has actually blacklisted the residential IP address range and does not let me change my reverseDNS, which means all my outgoing emails are marked as spam. On the other hand, the other ISP gave a clean static IP and the server managed for a small business is not at all problematic for emailing. YMMV but at least you know you have an option.
    
 
-	
    
+	
+	
    
+	    
+	
    
+	
 
 
 
diff --git a/docs/posts/hello-world.html b/docs/posts/hello-world.html
index 126a388..54a3e50 100644
--- a/docs/posts/hello-world.html
+++ b/docs/posts/hello-world.html
@@ -47,9 +47,14 @@
 
 
    Just re-did the entire website using Publish (Publish by John Sundell). So, a new hello world post :) 
    
 
-	
    
+	
+	
    
+	    
+	
    
+	
 
 
 
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