TensorFlow Object Detection | Realtime Object Detection with TensorFlow | TensorFlow Python |Edureka

This Edureka video will provide you with a detailed and comprehensive knowledge of TensorFlow Object detection and how it works. It will also provide you with the details on how to use Tensorflow to detect objects in deep learning method. Below are the topics covered in this tutorial:

1. What is Object Detection?
2. Industrial use of Object Detection
3. Object Detection Workflow
4. What is Tensorflow?
5. Object Detection using Tensorflow - Demo
6. Live Object Detection using Tensorflow- Demo

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(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)

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How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

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About the Course

Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

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Who should go for this course?

The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. Information Architects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

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Why Learn Deep Learning With TensorFlow?

TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

[edurekaIN]

Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

[codingphilosophy]

How can we build our own backbone model to embed in this tensorflow object detection api?
And which files we have to update for this purpose?
Let me allow to explain my question, let suppose I introduce my backbone model "A".
In which files I have to give link of "A"? and
Where I have to place "A.py" in object detection api?

[kanikachaudhary]

Great tutorial . amazing .Please share the code

[abhikumar]

Thanks for making such videos it helps us a lot Could you please send me the source code

[satyasuryakala]

superb video... its vey useful for me right now
can you please send the entire code of object detection in live streaming video?

[shubhityagi]

its very helpfull video for please provided source code and data set.

[sushilraverkar]

Very nice ...got to learn too many things in a short time. Thank you
Can I have source code ?

[cassie_fn]

Great tutorial searching for so long....finally found a video that is actually fruitful ...Thankyou so much for your time and i please get the dataset and the source code?...could love to try it!

[saurav]

in realtime object detection module, you said to copy the same code and suggested few changes, but even after changes realtime time code is completely different from the one you show after changes

[istiaqahmed]

Amazing video..It gave me a clear idea about object detection..Can you give me the Source code and dataset? I want to implement this my own.

[yashverma]

Nice tutorial.
Thank you so much....!!
Can you please share the complete source code

[sayanighosh]

This is a great tutorial for begginers . Thanks a lot @Edureka .. Can we get the source code and other links . It would be great ! 😇

[mochikhlasg]

ery useful tutorial with a good theoretical explanation. Can I have the code, please? Thanks.

[emila]

thank you for this great video! please can you send me the source code and data set?

[penguin]

that's amazing !! could you send me the source code please

[Promptcomputing]

Really nice explaination. Can you please share the source code?

[SreeLekshmiS-ulkr]

Great tutorial...Can you please share the source code?

[herbeysoft]

Thanks for the tutorial. Really lovely!!! Please can you help with the code???

[sandeshk]

Great Tutorial, could you please share the source code and data set.

[sekoukaba]

hello thank for the video can you help me to get the code and data set