Object Detection with Tensorflow | Fast Track Course | ML

Object Detection with Tensorflow | Fast Track Course | ML | Udemy
English | Size: 1.30 GB
Genre: eLearning

What you’ll learn
Introduction to Roadmap of the Course
Tensorflow CPU installation
Tensorflow GPU Installation (Optional)
Tensorflow Garden Repository Installation
Dataset Preparation & Labelling tool
Convert labelled dataset to the TF-Record (Binary Format)
Training the model (custom dataset)
Model Export with Frozen Inference Graph and Deployment on Webserver
Deployment of trained model on Webcam, Android and Video Feed

Machine learning is an exponential technology from IBM Watson, SIRI, self driving car which are taking on the world and artificial neural network is heart of it. Object Detection is an essential technique for artificial visualization in Robotics, AI Sensory tools and many more. But the problem is Object detection with your own dataset is simply not taught at university.

So where do you learn them?

Well, online training is good option, and that’s why we had a look at some of the Tensorflow object detection courses available online right now and what we found was that you all have solid background in computer science or mathematics to understand what’s going on and none of the course was robust in structure.

That’s why we developed fully practical oriented course where non technical person can also train the tensorflow model. With your own dataset, This course will help you to detect your own object from the surroundings.

The first thing I have focused on is a robust structure navigating a complex topic with training your own dataset. This course won’t required any coding background. All types of students can learn this course. Anyone can change their existing domain with computer vision.

Welcome to Custom Object Detection course with Tensorflow!

The course is broken down into practical sections like,

1. Tensorflow introduction to latest framework 2021

2. Tensorflow CPU installation with anaconda

2a. Tensorflow GPU installation with NVIDIA Toolkit and CUDNN library (optional)

3. Dataset preparation using Kaggle’s dataset or custom dataset

4. Image annotation to perform faster RCNN algorithm

5. Conversion to TFRECORD for input pipeline

6. Training your own model with tensor board visualization

7. Deployment of your trained model on Android application, web application and Realtime application

Who this course is for:
Students who are curious about Data science, Computer Vision, Machine Learning, AI and Deep Learning.
Those who wants to start the career with Data science, Computer Vision, Machine Learning, AI and Deep Learning.
Those who feel AI is the future in Medical, Agricultural and Engineering Field.
Student starts this course and ends with 100% sure Machine Learning Model.