TensorFlow Course
Rated 4.5/5 based on 84 customer reviews

TensorFlow Course

Learn to build machine learning models with Tensor Flow

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Modes of Delivery

Online Classroom

Collaborative, enriching virtual sessions, led by world class instructors at time slots to suit your convenience.


Our classroom training provides you the opportunity to interact with instructors and benefit from face-to-face instruction.

Team/Corporate Training

Our Corporate training is carefully structured to help executives keep ahead of rapidly evolving business environments.
Group Discount: 10.00% for 2 people 15.00% for 3 to 4 people 20.00% for 5 and above people

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Module 1- Getting started with TensorFlow

Learning Objectives:  Learn the basic concepts of Machine Learning (ML) and Deep Learning (DL). We will start with a brief introduction to ML. Then we will move to DL, which is a branch of ML based on a set of algorithms that attempt to model high-level abstractions in data.


  • A soft introduction to ML
  • Artificial neural networks
  • ML versus DL
  • DL neural network architectures
  • Available DL frameworks
Hands-on: No hands-on

Module 2- Overview of TensorFlow

Learning Objectives:  Write Python code using Deep Learning framework - TensorFlow to visualize computations through Tensorboards


  • TensorFlow computational graph
  • TensorFlow code structure
  • TensorFlow data model
  • Visualizing computations through TensorBoard
  • Linear regression and beyond
Hands-on: Write Python code using Deep Learning framework - TensorFlow to visualize computations through Tensorboards

Module 3- Feed-forward neural networks with TensorFlow

Learning Objectives:  Learn to implement a layered neural network also learn about hyperparameter tuning and dropout optimization in an FFNN


  • Implementing a feed forward network
  • Implementing a multilayer perceptron
  • Tuning hyperparameters and advanced FFNN
Hands-on: Implement a layered Neural Network using TensorFlow

Module 4- Convolutional Neural Network (CNN)

Learning Objectives: Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on objects that appear in them. Use these networks to learn data compression and image denoising
Real Life Case Study on CNN using TensorFLow


  • Main concepts in CNN
  • CNN in action
  • Fine tuning implementation
  • Case Study on CNN
Hands-on:  Handwriting digit recognition using CNN with TensorFlow. This project will help build a model using Convolutional Neural Network to recognize handwriting
Design and train  convolutional neural network models to classify images using TensorFlow & Keras

Module 5- Optimizing TensorFlow Autoencoders

Learning Objectives:  Learn how an autoencoder works and implement the same. Learn to improve the robustness of autoencoder


  • How does an autoencoder work?
  • How to implement an autoencoder
  • Improving autoencoder robustness
  • Building denoising autoencoders
  • Convolutional autoencoders
Hands-on : No Hands-on

Module 6- Recurrent Neural Networks

Learning Objectives: Build your own recurrent networks and long short-term memory networks with Keras and TensorFlow; perform sentiment analysis and generate new text
Real Life Case Study on RNN using TensorFLow


  • Working principles of RNNs
  • RNNs and the gradient vanishing-exploding problem
  • LSTM networks
  • Implementing an RNN
  • Case Study on RNN
Hands-on: Implement RNN using Keras
A time series  is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Using Long-Short-Term-Memory (LSTM) build a time series model to forecast the future values

Module 7- Heterogeneous and Distributed Computing

Learning Objectives:  Explore the fundamental topic on TensorFlow considering the possibility of executing TensorFlow models on GPU cards and distributed systems


  • GPU computing
  • The TensorFlow GPU setup
  • Distributed computing
  • The distributed TensorFlow setup
Hands-on: Hands-on on setting up a TensorFlow GPU

Module 8- Recommendation Systems Using Factorization Machines

Learning Objectives:  Learn about the theoretical background of recommendation systems, such as matrix factorization
Learn what is UBCF and how is it used in Recommender Engines. Covers concepts like cold-start problems
Learn what is IBCF and how is it used in Recommender Engines
Learn how to use Factorization Machines (FMs) and improved versions of them to develop more robust recommendation systems
Real Life Case Study with Recommender Systems


  • Recommendation systems
  • User-Based Collaborative Filtering
  • Item-Based Collaborative Filtering
  • FM-based recommendation systems
  • Case Study on Recommender Systems
Hands-on:  You do not need a market research team to know what your customers is willing to buy. And Netflix is a big example. Netflix successfully used recommender system to recommend movies to its viewers. And Netflix estimated, that its recommendation engine is worth a yearly $1billion. 

An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build Recommender System for a Retail Chain to recommend the right products to its users

Covers Artificial Neural Networks, Convolutional Neural Networks, Long Short Term Memory

Key Features

28 hours of Instructor led Training
Comprehensive Hands-on with Python
Covers Artificial Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks using TensorFlow
Gain knowledge in Computer Vision applications
Covers Reinforcement Learning

Our Students See All

Attended a 2 day weekend course by Knowledgehut for the CSM certification. The instructor was very knowledgeable and engaging. Excellent experience.

Attended workshop in April 2018

The CSPO Training was awesome and great. The trainer Anderson made all the concepts look so easy and simple. Using his past experience as examples to explain various scenarios was a plus. Moreover, it was an active session with a lot of participant involvement which not only made it interactive but interesting as well. Would definitely recommend this Training.

Attended workshop in July 2018

Great course. An interesting and interactive session to better understand how to succeed in formulating a business case and how to present it effectively.

Attended workshop in May 2018

The training was very interactive and engaging with the attendees.

Attended workshop in June 2018
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Jin Shi

Director at Timber creek Asset Management from Toronto, Canada
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Richard Dsouza

Business Analyst at Valtech from Bangalore, India
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Wily Salim

Services Project Engineer at Lendlease from Sydney, Australia
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Anish Maidh

Senior Project Manager at Telstra from Melbourne, Australia

Frequently Asked Questions

Deep learning is among the hottest career trends in the market right now. According to the average salary for "deep learning" ranges from approximately $72,172 per year for Research Scientist to $146,075 per year for Computer Vision Engineer.
This course will help you master TensorFlow, one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs.

You will gain the following practical skills from this course:

  • Get to know about TensorFlow Framework
  • Learn about Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks using TensorFlow
  • Be proficient in using TensorFlow
  • Get an understanding of Computer Vision applications using TensorFlow

By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as data analysts.

There are no restrictions but participants would benefit if they have Python programming knowledge and familiarity with Data Science.

Yes, KnowledgeHut offers this training online.

On successful completion of the Tensorflow course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Data Science experts who have years of industry experience.

Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details:

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our
Refund Policy.

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

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