Quick Contact
Python Tutorial
- What is Python?
- How to Install Python?
- Python Variables and Operators
- Python Loops
- Python Functions
- Python Files
- Python Errors and Exceptions
- Python Packages
- Python Classes and Objects
- Python Strings
- PostgreSQL Data Types
- Python Generators and Decorators
- Python Dictionary
- Python Date and Time
- Python List and Tuples
- Python Multithreading and Synchronization
- Python Modules
- What is Python bytecode?
- Python Regular Expressions
Python Panda Tutorial
- Python Pandas Tutorial
- Python Pandas Features
- Advantages and Disadvantages of Python Pandas
- Pandas Library In Python
- Pandas Series To Frame
- Python Dataframeaggregate and Assign
- Pandas Dataframe Describe
- Pandas Dataframe Mean
- Pandas Hist
- Pandas Dataframe Sum
- How to convert Pandas DataFrame to Numpy array
- Pandas Concatenation
Python Selenium
- Selenium Basics
- Selenium with Python Introduction and Installation
- Navigating links using get method Selenium Python
- Locating Single Elements in Selenium Python
- Locating Multiple elements in Selenium Python
Python Flask Tutorial
Python Django
- How to Install Django and Set Up a Virtual Environment in 6 Steps
- Django MTV Architecture
- Django Models
- Django Views
- Django Templates
- Django Template Language
- Django Project Layout
- Django Admin Interface
- Django Database
- Django URLs and URLConf
- Django Redirects
- Django Cookies and Cookies Handling
- Django Caching
- Types of Caching in Django
- Django Sessions
- Django Forms Handling & Validation
- Django Exceptions & Error-handling
- Django Forms Validation
- Django Redirects
- Django Admin Interface
- Django Bootstrap
- Ajax in Django
- Django Migrations and Database Connectivity
- Django Web Hosting and IDE
- Django Admin Customization
- What is CRUD?
- Django ORM
- Django Request-Response Cycle
- Django ORM
- Making a basic_api with DRF
- Django Logging
- Django Applications
- Difference between Flask vs Django
- Difference between Django vs PHP
Numpy
- Numpy Introduction
- NumPy– Environment Setup
- NumPy - Data Types
- NumPy–Functions
- NumPy Histogram
- numPy.where
- numpy.sort
- NumPyfloor
- Matrix in NumPy
- NumPy Arrays
- NumPy Array Functions
- Matrix Multiplication in NumPy
- NumPy Matrix Transpose
- NumPy Array Append
- NumPy empty array
- NumPy Linear Algebra
- numpy.diff()
- numpy.unique()
- numpy.dot()
- numpy.mean()
- Numpy.argsort()
- numpy.pad()
- NumPyvstack
- NumPy sum
- NumPy Normal Distribution
- NumPylogspace()
- NumPy correlation
- Why we learn and use Numpy?
Tensorflow
- Introduction To Tensorflow
- INTRODUCTION TO DEEP LEARNING
- EXPLAIN NEURAL NETWORK?
- CONVOLUTIONAL AND RECURRENT NEURAL NETWORK
- INTRODUCTION TO TENSORFLOW
- INSTALLATION OF TENSORFLOW
- TENSORBOARD VISUALIZATION
- Linear regression in tensorflow
- Word Embedding
- Difference between CNN And RNN
- Explain Keras
- Program elements in tensorflow
- Recurrent Neural Network
- Tensorflow Object Detection
- EXPLAIN MULTILAYER PERCEPTRON
- GRADIENT DESCENT OPTIMIZATION
Interview Questions & Answers
Tensorflow Object Detection
– creating environment
How to run 40 object detection models using tensorflow these models include famous ssd mobile net and faster rcnn.
So you should follow some steps with example as-:
– Create new environment with python 3.8 in order to avoid mismatch of environments versions.
– Now open the prompt and create the environment as-:
Conda create –n myenv python 3.8
Press enter and environment will be created.
Now clear the screen and activate the environment as take the created environment as “tfod”.
>Activate tfod
– Now install tensorflow 2.2.0 and press enter.
– Once its install verify it as-:
>python
– Now import tensorflow
>import tensorflow as tf
– Now install cuda as mentioned after importing tensorflow.
>tf_version_
Press enter
– Now let’s start first step
Install cuda toolkit 10.1 or it depends on your gpu that which version will work.
After downloading this, just open exe file and start the installation process. Once you start with installation process go to explorer – c drive – program file and nvidia toolkit where entire installation is done.
– Now go for Cudnn libraries, download it
After downloading this, you got the downloaded file, open it. After this you got dll file, which you will copy and paste in bin. Copy the entire files from bin into your drive.
– After this go to env variable and setup the path as-:
And then press OK.
– After all setup is done, go to the command prompt.
As here you can also check cuda file in bin as-:
– To check in command prompt, you should go for “jupyter notebook”
Go to anaconda navigator.
Install jupyter notebook.
– In jupyter you should add some code and execute as-:
Now set up is done.
Next you have to do is installing tensorflow object detection API
– Open the anaconda prompt do same step for activating model.
Install git-:
– After installing or downloading, go to command prompt as-:
You can also download entire repository as follows-:
In command prompt install the “git” by entering the path copied from above image as-:
Installation process is running as-:
After installing, last step in this is – Protobuf installation and compilation-:
This protobuf is just like a kind of library that will actually consider your models and your training parameters. You can open the above link and go to further process by scrolling down.
– So select protoc-3.14.0-win64.zip file and download by clicking it. Then go to downloads and copy it in E-drive. Extract the file. Copy the entire files and go to C drive >program files and make a folder “google protobuf”. Inside the folder, paste the files. These files help to configure the model and train the parameters and that is the setup with respect to protobuff compile installation and compilation.
– Take the specific path of files located and go to environment variable and open the path folder and paste the path as-:
– Next do the compilation
– Open the anaconda prompt, and do the compiling with entire library. Now for compiling, go to tensorflow directory,
Copy the entire path, and go to command prompt, activate as usual and paste the url as-:
Now in order to compile use the command as-:
Now above command is the command which is used for compiling the entire protograph library. Now copy the command and paste in prompt. To verify that it is working or not press enters.
– If you see it will execute then it means that the library has got compiled properly.
– You can go to your folder and see the changes in the folder, no changes are there that means the execution or compilation has happened perfectly.