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TensorFlow Course with Hands-on Training

Equip Yourself with Most In-Demand Library TensorFlow

  • 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
Group Discount


Big data and data science are the careers of the future. Powered by big data, Deep Learning has made business more viable across healthcare, genomics, cybersecurity, e-commerce, agriculture, and other sectors and this is the right time to invest in a career in Deep Learning. Deep-learning networks are distinguished from ordinary neural networks by having more hidden layers, or so-called more depth.

Deep learning engineers who are experts in libraries such as TensorFlow are in much demand for their ability to implement deep learning for numerical computation of mathematical expressions, using data flow graphs. Developed in the Google labs, TensorFlow is one of the best libraries to implement advanced techniques in deep learning. This workshop will help you understand TensorFlow and explore deep neural networks and layers of data abstraction. Through this hands-on workshop, you will gain real-world contextualization through deep learning problems concerning research and application.

What You Will Learn

  • We recommend applicants to have knowledge of programming (preferably in Python).
  • Familiarity with statistics, algebra, probability, and exposure to data science is preferred.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

  • Those interested in computer vision, image restoration, text classification etc.
  • Those working with large datasets who want to simplify the effective analysis
  • Software or Data Engineers interested in learning about Deep Learning

KnowledgeHut Experience

Instructor-led Interactive Classroom Experience

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises, and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Our support team will guide and assist you whenever you require help.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.


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.

Topics Covered:

  • A soft introduction to ML
  • Artificial neural networks
  • ML versus DL
  • DL neural network architectures
  • Available DL frameworks

Learning Objectives:  

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

Topics Covered:

  • TensorFlow computational graph
  • TensorFlow code structure
  • TensorFlow data model
  • Visualizing computations through TensorBoard
  • Linear regression and beyond


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

Learning Objectives:  

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

Topics Covered:

  • Implementing a feedforward network
  • Implementing a multilayer perceptron
  • Tuning hyperparameters and advanced FFNN


Implement a layered Neural Network using TensorFlow 

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

Topics Covered:

  • Main concepts in CNN
  • CNN in action
  • Fine tuning implementation
  • Case Study on CNN

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

Learning Objectives:  

  • Learn how an autoencoder works and implement the same. 
  • Learn to improve the robustness of autoencoder

Topics Covered:

  • How does an autoencoder work?
  • How to implement an autoencoder
  • Improving autoencoder robustness
  • Building denoising autoencoders
  • Convolutional autoencoders

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

Topics Covered:

  • Working principles of RNNs
  • RNNs and the gradient vanishing-exploding problem
  • LSTM networks
  • Implementing an RNN
  • Case Study on RNN


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

Learning Objectives:  

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

Topics Covered:

  • GPU computing
  • The TensorFlow GPU setup
  • Distributed computing
  • The distributed TensorFlow setup


Hands-on in setting up a TensorFlow GPU

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

Topics Covered:

  • Recommendation systems
  • User-Based Collaborative Filtering
  • Item-Based Collaborative Filtering
  • FM-based recommendation systems
  • Case Study on Recommender Systems


You do not need a market research team to know what your customers are 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 nearly $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



Project related to this course

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


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

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Jin Shi

Director at Timber creek Asset Management from Toronto, Canada

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.

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Richard Dsouza

Business Analyst at Valtech from Bangalore, India

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

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Wily Salim

Services Project Engineer at Lendlease from Sydney, Australia

The training was very interactive and engaging with the attendees.

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Anish Maidh

Senior Project Manager at Telstra from Melbourne, Australia


The Course

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. 

Finance Related

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% reduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for a 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

The Remote Experience

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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