- Welcome
- Introduction to TensorFlow
- TensorFlow 2.x and Eager Execution
- Introduction to Deep Learning
- Deep Neural Networks
Building Deep Learning Models with TensorFlow
Embrace real-world problems on Deep Learning with ease using the TensorFlow library by studying Building Deep Learning ...Read more
Intermediate
Online
7 Weeks
Quick Facts
particular | details | |||
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
In Building Deep Learning Models with TensorFlow course, learners will obtain a hands-on experience with the TensorFlow library as a strong mechanism for the application of Deep Learning to real-world issues.
The course efficiently combines Machine Learning and Data Science to impart an all-rounder learning on building Deep Learning models. As a part of the IBM AI Engineering Professional Certificate, students will grasp all fundamentals of Machine learning and Deep Learning, including the use of Python.
Hands-on projects will provide the course takers with essential Data Science skills and unsupervised learning. Candidates will also build, deploy and train their Deep Learning models and adopt various features of the TensorFlow library in them. Since the majority of data in the world is unstructured and unlabelled, shallow neural networks find it difficult to capture structures in data comprising sound, images and texts.
However, deep networks are capable of performing this function and hence this course has been created to provide an Intermediate Level of knowledge of how to go about it. IBM is a major player in the industry by providing a broad portfolio to its trainees for software development, predictive analytics, and systems management. Its expertise in technology and R&D is incorporated into the course curriculum.
The highlights
- Seven-day free trial
- Approx. 7 hours of online training
- IBM instructors
- Professional Certificate and badge from IBM
- Self-scheduled learning
- Free audit of course
- Intermediate level course
Program offerings
- Hands-on project
- Self paced learning
- Online mode
- Video modules
Course and certificate fees
Building Deep Learning Models with TensorFlow Fee Structure:
Description | Amount |
Fee for 1 month | Rs. 1,676/- |
Fee for 3 months | Rs. 3,369/- |
Fee for 6 months | Rs. 5,029/- |
certificate availability
Yes
certificate providing authority
Coursera
Who it is for
The multifarious course of Building Deep Learning Models with TensorFlow covers a lot of domains including Deep Learning, Machine Learning, and Data Science. As a result, it proves helpful to individuals from different inter-related domains, including-
- Data scientists
- Machine Learning enthusiasts
- Software Engineers
- AI engineers
Eligibility criteria
Certification Qualifying Details
Since Building Deep Learning Models with TensorFlow course is a part of IBM AI Engineering Professional Certificate, students pursuing the paid version of the course will get a dual benefit of a Professional shareable certificate from Coursera as well as a Digital Badge from IBM which holds immense professional significance. Though there are no specific certificate qualification requirements, certification will only come up on the “My Accomplishments” tab on the dashboard after candidates have completed all the learnings in toto and after the grading of their assignments by their peers and the staff.
What you will learn
Candidates pursuing Building Deep Learning Models with the TensorFlow course will acquire appreciable learning of the current domain along with professional takeaways of the adjoining concepts. From video lectures to assignments, each aspect of the curriculum is designed to grant conceptual insights in the following way-
- Describe the use of TensorFlow in regression, curve fitting, minimization of error functions and classification.
- Explain foundational TensorFlow concepts such as the operations, main functions, and the execution pipelines.
- Apply TensorFlow for backpropagation to tune the biases and weights along with the training of Neural Networks.
- Understand different types of Deep Architectures like Recurrent Networks, Convolutional Networks, and Autoencoders.
The syllabus
Module 1: Introduction
Videos
Readings
- Syllabus
- Labs in This Course
Practice Exercise
- Deep Neural Networks and TensorFlow
Module 2: Supervised Learning Models
Videos
- Introduction to Convolutional Neural Networks (CNNs)
- Convolutional Neural Networks (CNNs) for Classification
- Convolutional Neural Networks (CNNs) Architecture
Practice Exercise
- Convolutional Neural Networks
Module 3: Supervised Learning Models (Cont'd)
Videos
- The Sequential Problem
- Recurrent Neural Networks (RNNs)
- The Long Short Term Memory (LSTM) Model
- Language Modelling
Practice Exercise
- Recurrent Neural Networks
Module 4: Unsupervised Deep Learning Models
Videos
- Introduction to Restricted Boltzmann Machines
- Restricted Boltzmann Machines (RBMs)
Practice Exercise
- Restricted Boltzmann Machines
Module 5: Unsupervised Deep Learning Models (Cont'd) and scaling
Videos
- Introduction to Autoencoders
- Autoencoders
Readings
- Scaling of neural networks
Practice Exercise
- Autoencoders
Admission details
In order to enrol themselves in Building Deep Learning Models with TensorFlow course, candidates have to register on Coursera and follow a simple procedure as stated below-
Step 1: Visit the course page. https://www.coursera.org/learn/building-deep-learning-models-with-tensorflow
Step 2: Look for the “Enroll for Free” tab at the top which would lead to a prompt stating the conditions of a free trial along with the option to audit for the free course.
Step 3: If the learner chooses the option for a free trial, he/she will have to pay the registration fee to avail of the course.
Step 4: Where else if he/she wishes to audit the course, he/she will get immediate access to the course material and dashboard.
Step 5: Candidates will have to select the option of “Start Learning” to start their course.
Scholarship Details
After getting an idea of the course structure through the seven-day free trial, learners willing to get Professional Certification can opt for financial aid on Coursera if they are unable to arrange for the fee. To be considered in the selection process, candidates have to fill in an application form.
Applicants should log in through their Coursera account through the login options displayed on the screen and click on the option of “Financial Aid Available”. After a thorough reading of the terms, learners will have to select “Continue to the Application”. Soon after, they would be able to see the webpage application form. The deadline for submission of an application is fifteen days, that is, learners ought to submit their application prior to the deadline for its consideration.
Candidates have to first make two declarations promising to abide by their intention to take up the course entirely and to enter their actual personal details. After clicking on “Continue,” they will have to put in the requested information.
Some basic details to be entered by the candidates include-
- Their annual income
- Their educational Background
- The minimum amount that they can afford
- Reasons for requesting the aid
- Specification regarding utilization of the learning in their career
- Their current employment status
How it helps
The primary benefit of Building Deep Learning Models with TensorFlow course is the delivery of lectures and course content by professionals from IBM, which has long established itself as a training platform equipping trainees with requisite skills. Besides this, Professional Certification from Coursera with an IBM badge will help boost up the professional experience of the candidates and will also showcase their proficiency in Artificial Intelligence engineering.
Labs are a great learning experience carrying a professional outlook. They not only reinforce concepts but also illustrate Tensorflow coding to run the Deep Learning models to aid in industrial implementation. In order to understand the codes, learners will have to break and analyse them line by line which is a good exercise.
The course proves itself to be an excellent one to get started with the TensorFlow library as this would help one in solving problems under Data Science and Machine Learning. The course carries an easy-to-follow pace for the adaptation of a hard-core Python package. Apart from getting an overview of practicalities like DNN, the easy math methods and detailed notebooks keep the learners hooked and give them a second layer of knowledge along with practical examples.
Instructors
FAQs
Are there some restrictions on accessing the course material for certain countries?
Coursera prohibits countries like Crimea, Iran, Cuba, Sudan, North Korea, and Syria to access all or certain content as per the U.S. export control regulations.
What is the Professional Certification in this course all about?
This Professional Certificate:
- Confirms the successful completion of the programme
- Is recognised by other organisations or schools.
- Is issued by the institution that developed the program, not Coursera
What are the payment methods which are not available to Indian students?
Learners in India cannot make payments on Coursera through the following methods-
- eWallet (PayTM)
- NetBanking
- Local debit cards
- Local credit cards
How can candidates utilise a promo code?
Promo code can be used from the “My Purchases” page, or directly from the payment screen during the payment. It may also be applied automatically if the candidate has it.
How can candidates add deadlines for their courses?
- They will have to log in via the web or Coursera app
- Select the Settings menu
- Choose “Calendar Sync”
- Lastly, they may connect their calendar by the displayed instructions.
Will a candidate be eligible for the certificate if he completes the course?
Yes, candidates will receive the certification after they complete the entire programme successfully and pay for the certification.
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