Useful Online Content

• 1. 1. ML cornel university

     2. svm_cornell

     3. svm light

     4. ML-Caltech

     5. ML_Oxford

     6. MATCONVET

     7. ML

     8. DEEP LEARNING FOR VISUAL QA

     9. ANDREJ KARPATHY DATA

     10. COLAH'S BLOG

     11. OXFORD CNN

     12. DENMARL UNIV. SIMPLE IMPLE CNN

     13. STANFORD CNN

     14. CNN DATA

     15. TENSERFLOW

     16. TYPE 2 FUZZY

     17. CMU-OLI

     18. GENERAL-DM

     19. CNN_Code

     20. Deep for non image dataset

     21. Google_on_Deep

     22. ML Conf

     23. Clustering

     24. Conferences Rank

     25. http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-3/www/ml.html

     26. To install pkgs in python

     27. Python_web Browser

     28. Google search parsing

     29. Deep Learning

     30. Financial_data

     31. ML

     32. Karpathy RNN

     33. Deep Learning

     34. Videos on ANN

     35. Deep Learning MIT Book

     36. Reinforcement learning

     37. ZEN of Gradient Descent

     38. http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/

     39. https://blog.heuritech.com/2016/01/20/attention-mechanism/

     40. https://www.cs.princeton.edu/~blei/papers/Blei2012.pdf - Topic Modelling (NLP)

     41. https://trainingthemachine.wordpress.com/2014/04/11/sentiment-analysis-using-stanford-corenlp-recursive-deep-learning-models/

     42. Probabilistic Graphical Model Lectures Series

     43. LDA

     44. MCMC

     45. By Lecun, Lectures by LeCun

     46. Deep Mind Publications

     47. CNN_Time

     48. To write a Paper

     49. Nice articles

     50. Sentiment Analysis of Social Media Texts

     51. Word embedding Visual Demo

     52. Back Propagation

     53. Courses

     54. IEEE format

     55. Notes on GA

     56. Comparison: Caffe, Torch, Theano, Tensor_flow, Keras, Lasagne

     57. Time series analysis

     58. Tool for Linear programming / Quadratic programming

     59. A Survey of Selected Papers on Deep Learning at ICML 2016

     60. Types of Regression

     61. Regression_animated

     62. Statistics on regression

     63. Turing_scrapbook

     64. Assumptions on Regression

     65. Matplotlib

     66. Regression Dataset

     67. NIPS-2016

     68. http://shoutfuture.blogspot.in/2016/12/machine-learning-algorithms-for-beginners.html

     69. https://github.com/ZuzooVn/machine-learning-for-software-engineers

     70. https://xyclade.github.io/MachineLearning/#using-unsupervised-learning-to-merge-features-pca

     71. http://www.cs.cmu.edu/~ninamf/courses/601sp15/prev.shtml

     72. DL_updated

     73. GAN (Generative Adversial Network)

     74. NIPS_2016 (Summary)

     75. GAN

     76. Hypothesis_Testing

     77. LSTM

     78. LSTM_TimeSeries

     79. Dataset_DataMarket

     80. Dataset:TimeSeries

     81. Multiple Regression

     82. Time Series_Data_Classification

     83. z score of companies

     84. sentiment Doc2Vec

     85. Modern Sentiments

     86. pre trained word2vec google

     87. Lexicons

     88. Download stock Market historical Data

     89. Bombay Stock Exchanage

     90. GAN

     91. WEVI Online

     92. EEG lift grasp

     93. hadoop setup

     94. Map functions

     95. sparkitLearn

     96. from sklearn import metrics

     97. To find out which NVIDIA GPU I have--->  update your PCI ID database with: sudo update-pciids And use the following command in your terminal: lspci -nn | grep '\[03'  You will see the model name of your graphic card. If it's ambiguous, you could search the PCI ID (something like [10de:11bc]) on the Internet for the corrent model name.

     98. watch -n 5 free -m To watch free memory

     99. Atlas plot

     100. Map_Python_Tutorial

     101. RBF

     102. To use Algorithm in Latex

     103. Remove Background

     104. RBF

     105. Atlas_xlsx_manually

     106. Embed Clocks in e-mail

     107. Bias-Variance Dilemma

     108. R2 Score

     109. 3D Plot

     110. KL Divergence

     111. Latex Table Generator

     112. Nice Blog (ML)

     113.  Py Torch Stock Prediction

     114. Gephi for python plot

     115. Words2avoid in Research Paper

     116. donts of research writing

     117. Conferences_Journal

     118. Gov Datasets1

     119. LibSVM Datasets2

     120. OnGithub Dataset3

     121. 1001Dataset4

     122. Delve Dataset5

     123. Blog on Regression

     124. Language Identification

     125. To handle non-English languages in Python

     126. Color Code for plot

     127. for statistics

     128. Python Plot

     129. Emotion Dictionary Text Mining

     130. Emotion Blog

     131. $$ \mathscr R $$

     132. Symbolic Regressor

     133. ML

     134. Lagarange Multiplier

     135. Memory analysis of Python

     136. Kernels

     137. Datasets

     138. latex math symbol

     139. cuda linear algebra

     140. Cv2

     141. DT Mam's Thesis

     142. Scholarships

     143. Variance Estimation Over Sliding Window

     144. A novel approach for speeding up RBF based Interpolation Methods

     145. fast interpolation and approximation of Scattered RBF

     146. time series Deep Learning

     147. https://stackoverflow.com/questions/33759623/tensorflow-how-to-save-restore-a-model

     148. From tensorflow.python.client import device_lib          Print(device_lib.list_local_devices()) Python without Sudo Mobilenet V2: PyTorch CIFAR-1 Google AI blog: Federated Learning :Fine tuning through Torch (Surgery): https://discuss.pytorch.org/t/how-to-perform-finetuning-in-pytorch/419,  Codecogs for equations in latex, 

     149. Gradient Descent Optimization

     150. PyTorch Tutorial

     151. How to do Training

     152. plot graphs

     153. Scaling neural machine translation to bigger data sets with faster training and inference

     154. MNET2_Prototxt

     155. Model Description Flops

     156. Fine Tuning and Transfer Learning

     157. Latex acronym and glossary

     158. Depthwise separable convolution

     159. ReLu as an activation function

     160. Deep models explanation

     161. Deep Models Explaination_2

     162. https://developer.download.nvidia.com/compute/cuda/10.0/Prod/docs/sidebar/CUDA_Quick_Start_Guide.pdf

     163. gedit ~/.bashrc

     164. ESWA Rank

     165. http://archive.ics.uci.edu/ml/index.php (data for ML and DS)

     166. Command to save terminal output to the text file

“SomeCommand 2>&1 | tee SomeFile.txt”

• 1. 167. https://api.whatsapp.com/send?phone=918090042017#

     168. KKT Condisions

     169. Elsevier Chat

     170. Mobilenet-v2

     171. quantization resnet

     172. S. Keshav on how to read a paper http://ccr.sigcomm.org/online/files/p83-keshavA.pdf

          Simon Peyton-Jones talk on writing a great research paper: https://www.youtube.com/watch?v=g3dkRsTqdDA

         Simon P-J on how to give a research talk: https://www.youtube.com/playlist?list=PL_GZMuq8H3NaR-8LjxAEiNJ4cUVC1vxn6

         Simon Says:  http://research.microsoft.com/en-us/um/people/simonpj/papers/giving-a-talk/giving-a-talk.htm

         Parberry on presenting a theoretical CS paper: https://larc.unt.edu/ian/pubs/speaker.pdf

         Parberry on writing referee reports: http://www.cs.utexas.edu/~dahlin/professional/parbery-referee.pdf

        Atul Gawande's The Checklist Manifesto: http://atulgawande.com/book/the-checklist-manifesto/

• 1. 173. https://www.policybazaar.com/income-tax/tax-saving-investments/

     174. Import inspect Import numpy inspect.getfile(numpy)

     175. tensorflow checkpoint to .pb conversion

     176. TF Graph trimming

     177. TF Surgery

     178.  Problem of finding wrapt while installing tensorflow pip install wrapt --upgrade --ignore-installed pip install tensorflow

     179. Masked Image Dataset

     180. mask dataset

     181. Add node in graph

     182. Inference with .pb                   Next

     183. Editing the graph

     184. Add two subgraphs

     185. Editting Graph

     186. RL Content

     187. Change IP Python

     188. Git Commands and steps

     189. Quantum Computing

     190. Gradients with back propagation

     191. More on hooks Pytorch gradients 

     192. Pytorch Gradients computational gradients

     193. Caltech-101 Dataloader

     194. CV Maker Password sumitsingh

     195. GPU Programming

     196. cuda device side error

     197. ROC AUC Explanation

     198. RNN

     199. Derivative of sigmoid function

     200. Logistic Regression

     201. Zero shot Learning

     202. https://towardsdatascience.com/batch-normalization-in-3-levels-of-understanding-14c2da90a338

     203. Multitask learning

     204. segmentation task

     205. Pre-trained Mobilenet-v2 on Pytorch with Imagenet data

     206. ✅

     207. Image Dataset

     208. Setting up VDI

     209. Course on Quantum

     210. Argument Parser in Python