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Deep Learning and Neural Networks with Computer Vision

Surprised to see how Facebook, Instagram automatically detects the face or an object? Become a deep learning expert and build your own neural network and start detecting images, voices etc.

  1. 4.5
  2. (535 ratings)
  • 900+ Learners
  • English

Deep Learning and Neural Networks with Computer Vision

  1. $572
    • deep learning specialization
    • deep learning & neural network course
    • DataTrained, deep neural network
    • neural networks and deep learning
    900+ Learners
Features
  • 15+ Hours on demand Video
  • Projects Included
  • Certificate of completion
  • Taught by Industry Pros

What you'll learn

  • Understand the intuition behind multiple Neural Networks
  • Understand the intuition behind Convolutional Neural Networks
  • Understand the concepts behind Auto Encoders
  • Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications
  • Train test sets, analyse variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow

Deep Learning and Neural Networks Course Syllabus

Best-in-class content by leading faculty and industry leaders in the form of cases, videos and assignments, projects, & live sessions.

neural networks and deep learning
 deep neural network
  • This program has been created specifically for students who are interested in machine learning, AI, and/or deep learning, and who have a basic working knowledge of Python programming. Outside of that Python expectation, it's a very beginner-friendly program. Any students in college who want to start a career in Data Science, any data analysts who want to level up in Deep Learning, Students who have at least high school knowledge in math and who want to start learning Deep Learning can do this course
 neural networks and deep learning

The Deep Learning A-Z: Artificial Neural Networks is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology

In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Autoencoders, Object Detection, and learn concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

deep neural network

Get industry Recognize certificate of completion in Deep Learning and Neural Networks with Computer Vision form DataTrained eduction which can we varified online through out the world, shared in certification section on your linked profile, downloaded, priented, shared and mentined on your resume.

deep learning & neural network course DataTrained
deep neural network Abhay
  1. 4.5

I really enjoyed this course, it is easy to follow and have many resources to read about this argument without fall in over-complicated mathematics. Good Work

microsoft power bi certification training course Raj
  1. 4.5

This course is amazing in terms of it's friendliness to beginners. The instructor explains the intuitions and codings for the ANN, CNN and RNN very well for a beginner with basic knowledge of Python to understand.

Instructors

Learn from India’s leading Management faculty and Industry leaders

CHIDRI PRAJWAL -M.tech (computer science)

Shreya Gupta

AI Researcher Latitude+Lumiere

I started as my work as a Data Science Intern at iNeuron where I learned Computer Vision for Medical Imaging and made project in the same. Now I am working as Research assistant at Engineers4Exploration (UCSD), where I developed models that helped solve pressing environmental issues.

Dr. Deepika Sharma - Training Head, DataTrained

Dr. Deepika Sharma

Training Head, DataTrained

Dr. Deepika Sharma has been associated with academics /corporate education for more than 14 years. She has a deep passion in the field of Artificial Intelligence, Data Science, and Machine Learning.

Shankargouda Tegginmani - Data Scientist, Accenture

Shankargouda Tegginmani

Data Scientist, Accenture

Shankar is a data Scientist with 14 Years of Experience. His current employment is with Accenture and has experience in telecom, healthcare, finance and banking products.

Why Take this Course

  • The hottest new frontier in the universe of AI and machine learning is in deep learning and neural networks. This learning path is your entryway into the tools, concepts, and finer points of computer vision, natural language processing, and more.
  • If you are just starting out into Deep Learning, then you will find this course extremely useful
deep learning specialization

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Global Certification

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Frequently Ask Questions

Deep learning which is also known as deep structure learning. It is a part of machine learning methodologies based on artificial neural networks and representation learning. It can be either supervised learning, semi supervised or unsupervised learning.

Deep learning has several architectures such as :-

  • Deep neural networks
  • Deep belief networks
  • Deep reinforcement learning
  • Recurrent neural networks
  • Convolutional neural networks

These architectures are used in fields such as machine translation, medical image analysis, bio informatics, speech recognition, drug design and natural language processing. Deep learning is a vital part of data science that includes predictive modeling and statistics.

It is valuable for data scientists who are entrusted with collection, analysation and interpretation of huge amounts of data. Deep learning makes it possible to process this faster and easier. It is a category of machine learning algorithm which utilizes numerous layers to progressively bring out higher level characteristics from raw input.

There are tons of courses available in the market whether offline or online in the name of deep learning but most of them are not worth it. Organizations search for a skilled candidate who is certified from a reputed organization. DataTrained presents you with the best online course available for Deep Learning and Neural Network with computer vision.

DataTrained is India’s number 1 Ed Tech startup and we have already transformed thousands of careers. In our course, we have designed such that you can learn everything from scratch. Step by step you would learn from beginner to advanced level.

Our tutors and instructors will guide you through every stage of learning. Here are the salient features of our deep learning and neural network with computer vision course:-

  • 15+ hours of on demand video content
  • Several projects included
  • Best in class faculty in whole India
  • Certificate of completion from DataTrained
  • Student support system and placement assistance

Computer vision is a multidisciplinary science field which focuses on how computer systems can reach a higher level of learning from digital media such as images and videos. It basically learns and automates work which the human visual system does.

According to a report by Forbes, computer vision is being utilized in various industries like energy, utilities, automotive, and manufacturing, and this market is expected to grow to USD 48.6 Billion by 2022.

In simple terms, neural networks are the computer systems which are created for machine learning to mimic a human brain which is a natural neural network. They analyze data, search for patterns to develop logical rules so as to process information for the identification of things.

In the market space, only that candidate would find success who is certified from a reputed organization. DataTrained, which is India’s number 1 Ed Tech startup, presents you with the best online course available for Deep Learning and Neural Networks with computer vision.

Our course is offered at an unbeatable price of ₹ 19999. Our course is comparatively cheaper and very affordable as we keep in our mind that every class of students would have the desire to join our course. We at DataTrained believe in quality education at affordable prices.

This course would teach students every topic in detail with respect to deep learning and neural networks with computer vision. Our students can raise any number of questions and doubts and it will be resolved within a short period of time. Upon successful completion of course our team will be guiding and assisting students for placements as well. We also train our students to crack interviews with the help of our industry experts.

Deep learning and neural networks have been used interchangeably many times but there is a difference between both of them. Let us first understand what a neural network is. AI has advanced a lot in last few years thanks to rapid development in technologies.

But we are still very far away from truly intelligent machines. It means machines which have the power to behave like human beings. Machines that can make decisions like human beings and reason things based on their learning from data that is being provided.

The human brain is made up of several neurons which are connected to each other. Artificial Neural Networks or ANNs in short look for simulating such networks and make computers mimic like inter connected brain cells. Different segments of neurons or the human brain are responsible for processing distinct information and they are in a hierarchical arrangement.

Neural networks are used in deep learning for solving complicated issues which require analytical calculations. These calculations are similar to the calculations that are performed in a human brain. Let's look at the most common uses of neural networks in deep learning:-

  • Classification: Neural networks are used to implicitly analyze data parameters and label them into different categories. For example, for a bank customer based on his or her age, credit history, and solvency parameters a neural network analyses whether to loan him or her the money.
  • Recognition: It is the most used application of neural networks currently. For example, many organizations use a face recognition system in their premises so that only authorized company personnel can enter the building.
  • Prediction: Neural networks algorithms have the capability to make predictions as well. For example, based on different current and past parameters of stock it can predict whether its prices will rise or fall.

Deep learning is a subset of machine learning. The data that is represented in machine learning uses structured data whereas in deep learning it uses neural networks or artificial neural networks (ANNs). Machine learning is the next step of Artificial Intelligence whereas deep learning is the next stage to machine learning, it is basically how a machine can learn.

Machine learning consists of thousands of data points whereas deep learning consists of millions of data points. Outputs for machine learning are in numerical value whereas for deep learning they are either numerical or free form elements.

Machine learning utilizes several classes of algorithms to predict the future from datasets whereas deep learning utilizes neural networks to analyze data characteristics and relationships.

Natural Language Processing or in short NLP is a part of Artificial Intelligence specialization which looks for understanding and illustrating the cognitive systems which grant to understanding and producing human languages.

It utilizes advanced methodologies which are taken from artificial intelligence and computer science to facilitate computers in understanding, interpreting, and manipulating human languages.

Computer vision is a part of artificial intelligence or AI domain which facilitates machines, computers, and systems to make out meaningful data from digital media such as images, videos, and other visual sources. Computer vision requires a lot of data for processing meaningful information from different visual sources.

Yes, today deep learning is used very extensively for computer vision. In fact, there are various benefits of utilizing Convolutional Neural Networks or in short CNNs for computer vision. Convolutional neural networks provide a multi layered architecture which allows NNs to focus on the most applicable characteristics in the image.

  • Artificial Intelligence: Artificial Intelligence as it sounds is the ability of computer systems or devices to mimic a human brain. It means, the ability of computers to think, work, and function as human beings.
    Although we are still very far away from the true Artificial intelligence depicting models. An example of an AI model is “Sophia” which is the most advanced artificial intelligence tech we have today. Sophia even has full citizenship of Saudi Arabia!
  • Machine learning: Machine learning is a part of Artificial Intelligence. It is the study of algorithms which have the capability to improve automatically by repeated actions and utilization of data. ML programs execute several different tasks without exactly being coded to do so. It involves computers learning from the data available to carry out objectives.
  • Deep Learning: Deep learning is also known as deep structure learning. It is a part of machine learning methodologies based on artificial neural networks and representation learning. It can be either supervised learning, semi supervised, or unsupervised learning.