Friday, June 29, 2018

mnist test



/Users/hqin/github/Courses/tensorflow-without-a-phd/tensorflow-mnist-tutorial


under tensorflow virtual enviroment,
python3  mnist_1.0_softmax.py


tensorflow install applejac

Ref: https://www.tensorflow.org/install/install_mac


applejack:~ hqin$ sudo easy_install pip
Password:
Searching for pip
Best match: pip 9.0.1
Adding pip 9.0.1 to easy-install.pth file
Installing pip script to /anaconda3/bin
Installing pip3 script to /anaconda3/bin
Installing pip3.6 script to /anaconda3/bin

Using /anaconda3/lib/python3.6/site-packages
Processing dependencies for pip
Finished processing dependencies for pip
applejack:~ hqin$ 





applejack:~ hqin$  pip install --upgrade virtualenv 
Collecting virtualenv
  Downloading https://files.pythonhosted.org/packages/b6/30/96a02b2287098b23b875bc8c2f58071c35d2efe84f747b64d523721dc2b5/virtualenv-16.0.0-py2.py3-none-any.whl (1.9MB)
    100% |████████████████████████████████| 1.9MB 764kB/s 
Installing collected packages: virtualenv
Successfully installed virtualenv-16.0.0
You are using pip version 9.0.1, however version 10.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
applejack:~ hqin$ 



applejack:~ hqin$ virtualenv --system-site-packages -p python3 tensorflow

Running virtualenv with interpreter /anaconda3/bin/python3
Using base prefix '/anaconda3'
New python executable in /Users/hqin/python3/bin/python3
Also creating executable in /Users/hqin/python3/bin/python
Installing setuptools, pip, wheel...done.

applejack:~ hqin$ 





applejack:~ hqin$ cd python3/
applejack:python3 hqin$ ls
bin lib
include pip-selfcheck.json
applejack:python3 hqin$ ls bin/activate
bin/activate
applejack:python3 hqin$ 
applejack:python3 hqin$ pwd
/Users/hqin/python3
applejack:python3 hqin$  source ./bin/activate 
(python3) applejack:python3 hqin$ 
(python3) applejack:python3 hqin$ 


(python3) applejack:python3 hqin$ 
(python3) applejack:python3 hqin$ easy_install -U pip
Searching for pip
Reading https://pypi.org/simple/pip/
Downloading https://files.pythonhosted.org/packages/0f/74/ecd13431bcc456ed390b44c8a6e917c1820365cbebcb6a8974d1cd045ab4/pip-10.0.1-py2.py3-none-any.whl#sha256=717cdffb2833be8409433a93746744b59505f42146e8d37de6c62b430e25d6d7
Best match: pip 10.0.1
Processing pip-10.0.1-py2.py3-none-any.whl
Installing pip-10.0.1-py2.py3-none-any.whl to /Users/hqin/python3/lib/python3.6/site-packages
writing requirements to /Users/hqin/python3/lib/python3.6/site-packages/pip-10.0.1-py3.6.egg/EGG-INFO/requires.txt
Adding pip 10.0.1 to easy-install.pth file
Installing pip script to /Users/hqin/python3/bin
Installing pip3 script to /Users/hqin/python3/bin
Installing pip3.6 script to /Users/hqin/python3/bin

Installed /Users/hqin/python3/lib/python3.6/site-packages/pip-10.0.1-py3.6.egg
Processing dependencies for pip

Finished processing dependencies for pip

(python3) applejack:python3 hqin$ pip3 install --upgrade tensorflow
Collecting tensorflow
  Downloading https://files.pythonhosted.org/packages/03/ad/d732a5d9d50bfcd8aeb6e4a266065a8868829388e4e2b529ff689f1fc923/tensorflow-1.8.0-cp36-cp36m-macosx_10_11_x86_64.whl (46.5MB)
    100% |████████████████████████████████| 46.5MB 886kB/s 
Collecting tensorboard<1.9.0,>=1.8.0 (from tensorflow)
  Downloading https://files.pythonhosted.org/packages/59/a6/0ae6092b7542cfedba6b2a1c9b8dceaf278238c39484f3ba03b03f07803c/tensorboard-1.8.0-py3-none-any.whl (3.1MB)
    100% |████████████████████████████████| 3.1MB 7.8MB/s 
Collecting gast>=0.2.0 (from tensorflow)
  Downloading https://files.pythonhosted.org/packages/5c/78/ff794fcae2ce8aa6323e789d1f8b3b7765f601e7702726f430e814822b96/gast-0.2.0.tar.gz
Requirement not upgraded as not directly required: wheel>=0.26 in ./lib/python3.6/site-packages (from tensorflow) (0.31.1)
Collecting protobuf>=3.4.0 (from tensorflow)
  Downloading https://files.pythonhosted.org/packages/6d/7e/51c91b28cb8446ebd7231d375a2025bca4c59d15ddc0cf2dd0867b400cd7/protobuf-3.6.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (1.2MB)
    100% |████████████████████████████████| 1.2MB 8.0MB/s 
Collecting termcolor>=1.1.0 (from tensorflow)
  Downloading https://files.pythonhosted.org/packages/8a/48/a76be51647d0eb9f10e2a4511bf3ffb8cc1e6b14e9e4fab46173aa79f981/termcolor-1.1.0.tar.gz
Collecting astor>=0.6.0 (from tensorflow)
  Downloading https://files.pythonhosted.org/packages/b2/91/cc9805f1ff7b49f620136b3a7ca26f6a1be2ed424606804b0fbcf499f712/astor-0.6.2-py2.py3-none-any.whl
Collecting absl-py>=0.1.6 (from tensorflow)
  Downloading https://files.pythonhosted.org/packages/57/8d/6664518f9b6ced0aa41cf50b989740909261d4c212557400c48e5cda0804/absl-py-0.2.2.tar.gz (82kB)
    100% |████████████████████████████████| 92kB 14.1MB/s 
Collecting grpcio>=1.8.6 (from tensorflow)
  Downloading https://files.pythonhosted.org/packages/7e/a9/daef33ec8a83eb1c34498f947d4447d5728d91c7374e4b78465c7c14e99a/grpcio-1.13.0-cp36-cp36m-macosx_10_7_intel.whl (1.9MB)
    100% |████████████████████████████████| 1.9MB 6.0MB/s 
Requirement not upgraded as not directly required: numpy>=1.13.3 in /anaconda3/lib/python3.6/site-packages (from tensorflow) (1.14.0)
Requirement not upgraded as not directly required: six>=1.10.0 in /anaconda3/lib/python3.6/site-packages (from tensorflow) (1.11.0)
Collecting bleach==1.5.0 (from tensorboard<1.9.0,>=1.8.0->tensorflow)
  Downloading https://files.pythonhosted.org/packages/33/70/86c5fec937ea4964184d4d6c4f0b9551564f821e1c3575907639036d9b90/bleach-1.5.0-py2.py3-none-any.whl
Collecting html5lib==0.9999999 (from tensorboard<1.9.0,>=1.8.0->tensorflow)
  Downloading https://files.pythonhosted.org/packages/ae/ae/bcb60402c60932b32dfaf19bb53870b29eda2cd17551ba5639219fb5ebf9/html5lib-0.9999999.tar.gz (889kB)
    100% |████████████████████████████████| 890kB 10.6MB/s 
Requirement not upgraded as not directly required: werkzeug>=0.11.10 in /anaconda3/lib/python3.6/site-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow) (0.14.1)
Collecting markdown>=2.6.8 (from tensorboard<1.9.0,>=1.8.0->tensorflow)
  Downloading https://files.pythonhosted.org/packages/6d/7d/488b90f470b96531a3f5788cf12a93332f543dbab13c423a5e7ce96a0493/Markdown-2.6.11-py2.py3-none-any.whl (78kB)
    100% |████████████████████████████████| 81kB 13.3MB/s 
Requirement not upgraded as not directly required: setuptools in ./lib/python3.6/site-packages (from protobuf>=3.4.0->tensorflow) (39.2.0)
Building wheels for collected packages: gast, termcolor, absl-py, html5lib
  Running setup.py bdist_wheel for gast ... done
  Stored in directory: /Users/hqin/Library/Caches/pip/wheels/9a/1f/0e/3cde98113222b853e98fc0a8e9924480a3e25f1b4008cedb4f
  Running setup.py bdist_wheel for termcolor ... done
  Stored in directory: /Users/hqin/Library/Caches/pip/wheels/7c/06/54/bc84598ba1daf8f970247f550b175aaaee85f68b4b0c5ab2c6
  Running setup.py bdist_wheel for absl-py ... done
  Stored in directory: /Users/hqin/Library/Caches/pip/wheels/a0/f8/e9/1933dbb3447ea6ef57062fd5461cb118deb8c2ed074e8344bf
  Running setup.py bdist_wheel for html5lib ... done
  Stored in directory: /Users/hqin/Library/Caches/pip/wheels/50/ae/f9/d2b189788efcf61d1ee0e36045476735c838898eef1cad6e29
Successfully built gast termcolor absl-py html5lib
Installing collected packages: html5lib, bleach, protobuf, markdown, tensorboard, gast, termcolor, astor, absl-py, grpcio, tensorflow
  Found existing installation: html5lib 1.0.1
    Not uninstalling html5lib at /anaconda3/lib/python3.6/site-packages, outside environment /Users/hqin/python3
    Can't uninstall 'html5lib'. No files were found to uninstall.
  Found existing installation: bleach 2.1.2
    Not uninstalling bleach at /anaconda3/lib/python3.6/site-packages, outside environment /Users/hqin/python3
    Can't uninstall 'bleach'. No files were found to uninstall.
Successfully installed absl-py-0.2.2 astor-0.6.2 bleach-1.5.0 gast-0.2.0 grpcio-1.13.0 html5lib-0.9999999 markdown-2.6.11 protobuf-3.6.0 tensorboard-1.8.0 tensorflow-1.8.0 termcolor-1.1.0

(python3) applejack:python3 hqin$ 

rm * in python3. Reinstall to ~tensorlfow

applejack:~ hqin$ virtualenv --system-site-packages -p python3 tensorflow
Running virtualenv with interpreter /anaconda3/bin/python3
Using base prefix '/anaconda3'
New python executable in /Users/hqin/tensorflow/bin/python3
Also creating executable in /Users/hqin/tensorflow/bin/python
Installing setuptools, pip, wheel...

done.

applejack:tensorflow hqin$ pwd
/Users/hqin/tensorflow
applejack:tensorflow hqin$ ll
total 8
drwxr-xr-x  16 hqin  staff   544B Jun 29 21:17 bin
drwxr-xr-x   3 hqin  staff   102B Jun 29 21:13 include
drwxr-xr-x   3 hqin  staff   102B Jun 29 21:13 lib
-rw-r--r--   1 hqin  staff    61B Jun 29 21:13 pip-selfcheck.json
applejack:tensorflow hqin$ source ./bin/activate
(tensorflow) applejack:tensorflow hqin$ 


(tensorflow) applejack:tensorflow hqin$  easy_install -U pip
Searching for pip
Reading https://pypi.org/simple/pip/
Downloading https://files.pythonhosted.org/packages/0f/74/ecd13431bcc456ed390b44c8a6e917c1820365cbebcb6a8974d1cd045ab4/pip-10.0.1-py2.py3-none-any.whl#sha256=717cdffb2833be8409433a93746744b59505f42146e8d37de6c62b430e25d6d7
Best match: pip 10.0.1
Processing pip-10.0.1-py2.py3-none-any.whl
Installing pip-10.0.1-py2.py3-none-any.whl to /Users/hqin/tensorflow/lib/python3.6/site-packages
writing requirements to /Users/hqin/tensorflow/lib/python3.6/site-packages/pip-10.0.1-py3.6.egg/EGG-INFO/requires.txt
Adding pip 10.0.1 to easy-install.pth file
Installing pip script to /Users/hqin/tensorflow/bin
Installing pip3 script to /Users/hqin/tensorflow/bin
Installing pip3.6 script to /Users/hqin/tensorflow/bin

Installed /Users/hqin/tensorflow/lib/python3.6/site-packages/pip-10.0.1-py3.6.egg
Processing dependencies for pip
Finished processing dependencies for pip



(tensorflow) applejack:tensorflow hqin$ 
(tensorflow) applejack:tensorflow hqin$ pip3 install --upgrade tensorflow 
Collecting tensorflow
  Using cached https://files.pythonhosted.org/packages/03/ad/d732a5d9d50bfcd8aeb6e4a266065a8868829388e4e2b529ff689f1fc923/tensorflow-1.8.0-cp36-cp36m-macosx_10_11_x86_64.whl
Requirement not upgraded as not directly required: wheel>=0.26 in ./lib/python3.6/site-packages (from tensorflow) (0.31.1)
Collecting protobuf>=3.4.0 (from tensorflow)
  Using cached https://files.pythonhosted.org/packages/6d/7e/51c91b28cb8446ebd7231d375a2025bca4c59d15ddc0cf2dd0867b400cd7/protobuf-3.6.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Requirement not upgraded as not directly required: six>=1.10.0 in /anaconda3/lib/python3.6/site-packages (from tensorflow) (1.11.0)
Collecting termcolor>=1.1.0 (from tensorflow)
Collecting gast>=0.2.0 (from tensorflow)
Collecting absl-py>=0.1.6 (from tensorflow)
Collecting astor>=0.6.0 (from tensorflow)
  Using cached https://files.pythonhosted.org/packages/b2/91/cc9805f1ff7b49f620136b3a7ca26f6a1be2ed424606804b0fbcf499f712/astor-0.6.2-py2.py3-none-any.whl
Collecting tensorboard<1.9.0,>=1.8.0 (from tensorflow)
  Using cached https://files.pythonhosted.org/packages/59/a6/0ae6092b7542cfedba6b2a1c9b8dceaf278238c39484f3ba03b03f07803c/tensorboard-1.8.0-py3-none-any.whl
Collecting grpcio>=1.8.6 (from tensorflow)
  Using cached https://files.pythonhosted.org/packages/7e/a9/daef33ec8a83eb1c34498f947d4447d5728d91c7374e4b78465c7c14e99a/grpcio-1.13.0-cp36-cp36m-macosx_10_7_intel.whl
Requirement not upgraded as not directly required: numpy>=1.13.3 in /anaconda3/lib/python3.6/site-packages (from tensorflow) (1.14.0)
Requirement not upgraded as not directly required: setuptools in ./lib/python3.6/site-packages (from protobuf>=3.4.0->tensorflow) (39.2.0)
Requirement not upgraded as not directly required: werkzeug>=0.11.10 in /anaconda3/lib/python3.6/site-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow) (0.14.1)
Collecting markdown>=2.6.8 (from tensorboard<1.9.0,>=1.8.0->tensorflow)
  Using cached https://files.pythonhosted.org/packages/6d/7d/488b90f470b96531a3f5788cf12a93332f543dbab13c423a5e7ce96a0493/Markdown-2.6.11-py2.py3-none-any.whl
Collecting html5lib==0.9999999 (from tensorboard<1.9.0,>=1.8.0->tensorflow)
Collecting bleach==1.5.0 (from tensorboard<1.9.0,>=1.8.0->tensorflow)
  Using cached https://files.pythonhosted.org/packages/33/70/86c5fec937ea4964184d4d6c4f0b9551564f821e1c3575907639036d9b90/bleach-1.5.0-py2.py3-none-any.whl
Installing collected packages: protobuf, termcolor, gast, absl-py, astor, markdown, html5lib, bleach, tensorboard, grpcio, tensorflow
  Found existing installation: html5lib 1.0.1
    Not uninstalling html5lib at /anaconda3/lib/python3.6/site-packages, outside environment /Users/hqin/tensorflow
    Can't uninstall 'html5lib'. No files were found to uninstall.
  Found existing installation: bleach 2.1.2
    Not uninstalling bleach at /anaconda3/lib/python3.6/site-packages, outside environment /Users/hqin/tensorflow
    Can't uninstall 'bleach'. No files were found to uninstall.
Successfully installed absl-py-0.2.2 astor-0.6.2 bleach-1.5.0 gast-0.2.0 grpcio-1.13.0 html5lib-0.9999999 markdown-2.6.11 protobuf-3.6.0 tensorboard-1.8.0 tensorflow-1.8.0 termcolor-1.1.0
(tensorflow) applejack:tensorflow hqin$ 


(tensorflow) applejack:imagenet hqin$ which python3
/Users/hqin/tensorflow/bin/python3
(tensorflow) applejack:imagenet hqin$ python3 classify_image.py 
/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
>> Downloading inception-2015-12-05.tgz 100.0%
Successfully downloaded inception-2015-12-05.tgz 88931400 bytes.
2018-06-29 21:22:49.100997: W tensorflow/core/framework/op_def_util.cc:346] Op BatchNormWithGlobalNormalization is deprecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization().
2018-06-29 21:22:49.315593: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca (score = 0.89107)
indri, indris, Indri indri, Indri brevicaudatus (score = 0.00779)
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens (score = 0.00296)
custard apple (score = 0.00147)
earthstar (score = 0.00117)

Tested imagenet example, Passed.

Thursday, June 28, 2018

mediation test in R


https://www.rdocumentation.org/packages/bda/versions/10.1.9/topics/mediation.test




The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.
By Baron, Reuben M.,Kenny, David A.
Journal of Personality and Social Psychology, Vol 51(6), Dec 1986, 1173-1182
http://psycnet.apa.org/buy/1987-13085-001


https://www.youtube.com/watch?v=F6lZ-Lj_W4I


SGD GO download



Could you look into GO slim mapping files? 
go_slim_mapping.tab

Wednesday, June 27, 2018

reading notes, deep learning in biomedical image analysis


A Survey on Deep Learning in Medical Image Analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sa ́nchez
Diagnostic Image Analysis Group Radboud University Medical Center Nijmegen, The Netherlands 

https://www.sciencedirect.com/science/article/pii/S1361841517301135?via%3Dihub


"Currently, the most popular models are trained end- to-end in a supervised fashion, greatly simplifying the training process. The most popular architectures are convolutional neural networks (CNNs) and recur- rent neural networks (RNNs). CNNs are currently most widely used in (medical) image analysis, although RNNs are gaining popularity. "


The second key difference between CNNs and MLPs, is the typical incorporation of pooling layers in CNNs, where pixel values of neighborhoods are aggregated using a permutation invariant function, typically the max or mean operation. This induces a certain amount of translation invariance and again reduces the amount of parameters in the network. At the end of the convo- lutional stream of the network, fully-connected layers (i.e. regular neural network layers) are usually added, where weights are no longer shared. Similar to MLPs, a distribution over classes is generated by feeding the activations in the final layer through a softmax function and the network is trained using maximum likelihood.


https://en.wikipedia.org/wiki/Softmax_function
In mathematics, the softmax function, or normalized exponential function,[1]:198 is a generalization of the logistic function that "squashes" a K-dimensional vector  of arbitrary real values to a K-dimensional vector  of real values, where each entry is in the range (0, 1], and all the entries add up to 1.

Adjacency matrix in Yuan for directed graphs

Adjacency matrix in Yuan exact controllability paper use column_node -> row_node, which is a mirror of the conventional row_node -> column_node.  These mirrored adjacency matrix for direct graphs do not change controllability analysis using the matrix based method, it seems to me.

Monday, June 25, 2018

Fall 2018 schedule




Couse merge request


UTC, UTCLearn Support

9:51 AM (1 hour ago)
to FACULTY@RAVEN.UTC.EDU Unsubscribe
Instructors:

Fall 2018 courses have been created, and are now available to you in UTC Learn.  Students will be added to courses one (1) week before the first day of classes (8/13/2018).  If you would like to merge any of your courses, please complete the course merge request form located at:


If you need additional assistance, please contact the Walker Center for Teaching & Learning (utclearn@utc.edu or 423-425-4188).

_________________________
UTC Learn Administrator
Walker Center for Teaching & Learning
utclearn@utc.edu

Fall 2018
CPSC2100 CRN 45179 software design and development

CPSC4999 CRN48778 computational genomics
CPSC4999 CRN 50405 computational genomics
BIOL4999  CRN48263 computational genomics
CPSC5910R CRN48779 computational genomics
For merge request:
 48778  ,  50405 ,  48263 ,  48779 

Sunday, June 24, 2018

nano sim card


Nano SIM is both smaller and approximately 15% thinner than the earlier Micro SIM(3FF) standard as well as the Mini SIM (2FF) cards that were ubiquitous for many years and people commonly refer to simply as SIM cards.Apr 9, 2018









  


Nano SIM is the fourth version, or the "fourth form factor" (4FF) of the SIM standard and measures a mere 12.3 mm by 8.8 mm by 0.67 mm, but still holds the same amount of data as earlier SIM cards.Apr 9, 2018



controllability and mirror effect of adjacency matrix


using the matrix eigen value genometric multiplicity, the adjacentcy matric matrix can be mirrored, and its control nodes should not be changed, based on my intuitive understanding.

insert figures into Rmd, example


---
title: "sandbox"
author:
date: "June, 2018"
output: html_document
---

```{r}
library("pracma")
library("Smisc")
```

#Run these matrices from Yuan2013
#ToDo in the next version
#next build a data structure to run more networks systematically.

#plug in these matrices into matrix A variable above
#then find n_D

![YuanFig1](figures/fig1.yuan.png)


```{r fig1A_a}
A_a<-matrix(c(0,1,1,1,1,1,1,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,1,0),nrow=6,ncol=6)

A_a;

eig<-eigen(A_a);
eig;

#functionND(A_a)


```


```{r fig1A_b}
A_b<-matrix(c(1,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,1,1,0,0,0,0,1),nrow=6,ncol=6)

A_c<-matrix(c(0,1,1,1,1,1,1,0,1,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,1,1,1,1,0,1,1,1,1,1,1,0),nrow=6,ncol=6)

```

#nd_names = c( 'network','network_size','eigen-values','eigen-vectors','nd')
#nd= data.frame(matrix(, nrow=3, ncol=length(nd_names)) #set up a skeleton table

#names(nd) = c( "network","network_size","eigen-values","eigen-vectors","nd")

```{r}

#result<-data.frame(network_name=c("A_a","A_b","A_c"),
#            network_size=c(length(A_a[1,]),length(A_b[1,]),length(A_c[1,])),
#            driver_nodes=c(functionND(A_a),functionND(A_b),functionND(A_c)));

#result;       
```

Wednesday, June 20, 2018

folder with large file size on applejack

Folders with large file size


applejack:MobileSync hqin$ pwd
/Users/hqin/Library/Application Support/Google
Google File stream temporary uploading file

/Users/hqin/Library/Application Support/MobileSync
iPad syn storage.

Monday, June 18, 2018

CRISPR in budding yeast

Cut
Fully functional CRISPR/Cas enzymes will introduce a double-strand break (DSB) at a specific location based on a gRNA-defined target sequence. DSBs are preferentially repaired in the cell by non-homologous end joining (NHEJ), a mechanism which frequently causes insertions or deletions (indels) in the DNA. Indels often lead to frameshifts, creating loss of function alleles.
To introduce specific genomic changes, researchers use ssDNA or dsDNA repair templates with homology to the DNA flanking the DSB and a specific edit close to the gRNA PAM site. When a repair template is present, the cell may repair a DSB using homology-directed repair (HDR) instead of NHEJ. In most experimental systems, HDR occurs at a much lower efficiency than NHEJ.
https://www.addgene.org/crispr/yeast/

There different types of CRIPSR/Cas kits: cut, based editing, nick, activate, and interference.

https://benchling.com/pub/ellis-crispr-tools

edX video files

Video files can be downloaded from EdX YouTube videos