Tensorflow Rated 4.5/5 based on 8057 customer reviews

Tensorflow certification training

A comprehensive and interactive pathway for you to learn Tensorflow.

  • 28 hours of Instructor led Training
  • Comprehensive Hands-on with Tensorflow
  • Understand what drives neural networks and technologies like Computer Vision applications
  • Learn about Artificial Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks using TensorFlow

Why should you learn Tensorflow?

Deep learning, a branch of machine learning is the technology behind driverless car, voice assistants, robots, predictive analysis, sound recognition and several other features that have made our lives easier. Deep learning methods use neural networks that are sophisticated networks capable of discovering hidden patterns within unstructured data. And how are neural networks created? With the help of libraries such as Tensorflow that can create large scale, multi layered neural networks. Tensorflow is among the most popular libraries for deep learning and has found large scale use in creating solutions for classification, creation, prediction, perception and understanding problems through numerical computation of mathematical expressions, using data flow graphs.

Investments in Deep Learning have crossed billions of dollars and its market is expected to grow from $655 million in 2016 to $34.9 billion worldwide by 2025. This is a great time to invest in a career in Deep Learning by mastering Tensorflow. 

How do you get started with Tensorflow?

Zeolearn’s comprehensive course is a great way to get started on Tensorflow. Starting from the basics, our industry expert guides will guide you through the advanced concepts in a practical and experiential course. You will gain real-world contextualization through deep learning problems concerning research and application through concepts like CNN, Recurrent Neural Networks, Heterogeneous and distributed computing and much more. 

What you will learn:

Prerequisites

It is recommended that participants have knowledge of programming (preferably in Python), along with familiarity with statistics, algebra, and probability. A prior exposure to data science would be hugely beneficial. 

Who should Attend

Zeolearn Experience

Learn By Doing

Immersive Hands-on training with combination of theoretical learning, hands-on exercises, group discussions, assignments and intensive Q&A sessions.

Live & Interactive

Ask questions, get clarifications, and engage in discussions with instructors and other participants.

Mentored by Industry Experts

 Get Mentored by Industry practitioners having more than 10 years of experience

Reason based learning

Don’t gain just theoretical or practical knowledge. Understand the WHAT, WHY, and HOW of a subject. Simplify the subject matter and get in-depth comprehension.

Code Review by Professionals

Get reviews and timely feedback on your assignments and projects from professional developers.

Build Projects

We emphasize on learning the concepts through examples and help you in building a portfolio of projects through the course of training

Lifetime Enrolment

 Free lifetime enrolment into any of the upcoming batches to help you refresh the concepts

Curriculum designed by Experts

The Curriculum goes through multiple levels of design and preparation by the experts to keep the topics/modules relevant to everyday changes in technology.

Study even from remote locations

Learn to use collaborative mediums to share opinions and improve your coding skills with assistance from the instructors and other participants.

Curriculum

Learning Objectives:  

In this module, you will learn the basic concepts of Machine Learning (ML) and Deep Learning (DL). You will start off with a brief introduction to ML and then move on to DL, which is a branch of ML based on a set of algorithms that attempt to model high-level abstractions in data.

Topics:

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

Learning Objectives:  

This module will help you learn and write Python code using Deep Learning framework - TensorFlow. You will learn to use TensorFlow to visualize computations through Tensorboards.

Topics

  • 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

Learning Objectives:  

This module will teach you to implement a layered neural network. You will also learn about hyperparameter tuning and dropout optimization in an FFNN.

Topics

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

Hands-on:

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. Also understand how to use these networks to learn data compression and image denoising and learn about CNN using TensorFLow through a real Life Case Study. 

Topics

  • 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.

Learning Objectives:  

In this module you will learn how an autoencoder works and implement the same. You will also learn to improve the robustness of autoencoder.

Topics

  • 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. Learn RNN using TensorFLow with a real Life Case Study.

Topics

  • 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.

Learning Objectives:  

You will explore the fundamental topic on TensorFlow considering the possibility of executing TensorFlow models on GPU cards and distributed systems.

Topics

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

Hands-on:

Hands-on in setting up a TensorFlow GPU.

Learning Objectives:  

In this module you will learn about the theoretical background of recommendation systems, such as matrix factorization, about UBCF and how is it used in Recommender Engines. You will also learn concepts like cold-start problems, about IBCF and how it is used in Recommender Engines. The module covers the use of Factorization Machines (FMs) and improved versions of them to develop more robust recommendation systems. You will also study about Recommender Systems with a real Life Case Study.

Topics

  • 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 are willing to buy. Netflix is a big example, and has successfully used recommender system to recommend movies to its viewers. 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.

FAQs

The Course

After completing our course, you will know:

  • All about TensorFlow Framework
  • About Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks using TensorFlow
  • How to be proficient in using TensorFlow
  • About Computer Vision applications using TensorFlow

Data in today’s world is a business’s most useful commodity and running a business all depends on how well this data is made use of. Study and analysis of this data can lead to uncovering trends and patterns that lead to new innovation, customer preferences and solutions to any business challenges. Deep learning networks are used to uncover these trends in images, sound and text and provide an analyses or solutions to problems. Tensorflow is one of the most important libraries used in deep learning and helping machines perform and make human like decisions. This has huge relevance in the future world and a career in Tensorflow and deep learning can set you up for life. 

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

Tensorflow workshop experience

The workshops at Zeolearn are always interactive, immersive and intensive hands-on programs. There are 3 modes of Delivery and you can select based on the requirements - 


Online Classroom training: Learn from anywhere through the most preferred virtual live instructor led training with the help of hands-on training and interactive sessions   

One-to-One Training: You can enrol for one-to-one Tensorflow classroom training session with our expert trainer at a preferred time. With this mode, you can customize your curriculum to suit your learning needs. 

Team/Corporate Training: In this type of training, an Organization can nominate their entire team for online or classroom training. You can customize your curriculum to suit your learning needs and also get post-training expert’s support to implement Tensorflow concepts in the project.  

We follow the below mentioned procedure for all the training programs by dividing the complete workshop experience into 3 stages i.e Pre, Workshop and Post. This is a tried and tested approach using which we have been able to upskill thousands of engineers.

Pre-training 

Before the start of training program, we make sure that you are ready to understand the concepts from Day 1. Hence, as a process of preparation for the intensive workshop, we provide the following -

  • Reference articles/ Videos and e-books
  • 2-4 hrs of training on pre-requisites - to make you workshop ready
  • Pre-Workshop Assessments - to assess and benchmark 
  • Environment set-up docs 

During Training

The training is completely hands-on and you receive the below mentioned deliverables from Zeolearn team - 

  • PPT and Code Snippets used in the class
  • Learners Guide or E-book
  • Projects / Case Studies
  • Assessments / Lab exercises
  • Quizzes and Polls
  • Study Plans - To structure your learning.

Post Training

We don’t just impart skills but also make sure that you implement them in the project. And for that to happen, we are always in touch with you either through newsletters or webinars or next version trainings. Some of the post-training deliverables lined-up for you are - 

  • Project assistance with mentor
  • Course Recordings 
  • Access to Alumni Network
  • Additional workshops on advanced level concepts 
  • Regular emails/newsletters on Blogs/Tutorials and other informational content

Yes, Zeolearn has well-equipped labs with the latest version of hardware and software. We provide Cloudlabs to explore every feature of Tensorflow through hands-on exercises. Cloudlabs provides an environment that lets you build real-world scenarios and practice from anywhere across the globe.  You will have live hands-on coding sessions and will be given practice assignments to work on after the class.

At Zeolearn, we have Cloudlabs for all the major categories like Web development, Cloud Computing, and Data Science.

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

PROJECT-1
TITLE - Handwriting digit recognition using CNN with TensorFlow. 

DESCRIPTION - 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.

PROJECT-2
TITLE - Build a time series model to forecast future values using LSTM.

DESCRIPTION - 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.

PROJECT-3
TITLE - Build Recommender System for a Retail Chain to recommend the right products to its users

DESCRIPTION - You do not need a market research team to know what your customers are willing to buy. Netflix is a big example, having successfully used recommender system to recommend movies to its viewers. 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. 

This course is delivered by industry-recognized experts who would be having more than 10 years of real-time experience in Tensorflow.

Not only will they impart knowledge of the fundamentals and advanced concepts, they will provide end-to-end mentorship and hands-on training to help you work on real-world projects with regards to Tensorflow.

Once you register for the course you will be provided with system requirements and lab setup document which contains detailed information to prepare the environment for the course.

To attend Tensorflow training program, the basic hardware and software requirements are as mentioned below - 

Hardware requirements

  • Intel i3 processor.
  • 4 GB RAM.
  • 1TB hard disk.
  • Nvidia 210 GPU.

Software Requirements

  • Latest NVIDIA GPU Driver installed
  • TensorFlow
  • Keras

Tensorflow Online Training Experience

All our training programs are quite interactive and fun to learn with plenty of time spent on lot of hands-on practical training, use case discussions and quizzes. Our instructors also use an extensive set of collaboration tools and techniques which improves your online training experience.

This will be live interactive training led by an instructor in a virtual classroom.

You will receive a registration link to your e-mail id from our training delivery team. You will have to log in from your PC or other devices.

Yes, for all the online public workshops there would be participants logging in from different locations.

In case of any queries, you can reach out to our 24/7 dedicated support at any of the numbers provided in the link below: http://www.zeolearn.com/contact-us, or send an email to hello@zeolearn.com.

We also have Slack workspace for the corporates to discuss the issues. If the query is not resolved by email, we will facilitate a one-on-one discussion session with our trainers.

If you miss a class, you can access the class recordings anytime from our LMS. At the beginning of every session, there will be a 10-12 minute recapitulation of the previous class. You can watch the online recording and clarify your doubts at that time. You may need to login 15 minutes before the main lecture begins to avail this facility.

We also have a Free Lifetime enrollment for most of our courses. In case you miss out a class, you can also enroll for another complete workshop or only for a particular session.

Finance related

Typically, Zeolearn’s training are exhaustive and the mentors help you out in understanding the in-depth concepts. 

However, if you find it difficult to cope, you may discontinue within the first 4 hours of training and avail a 100% refund. Learn more about our refund policy here.

Zeolearn offers a 100% money back guarantee if the candidates withdraw from the course right after the first session. To learn more about the 100% refund policy, visit our refund page.

Yes, we have scholarships available for Students and Veterans. We do provide grants that can vary upto 50% of the course fees. 

To avail scholarships, please get in touch with us at hello@zeolearn.com. The team shall send across the forms and instructions to you. Based upon the responses and answers that we receive, the panel of experts take a decision on the Grant. The entire process could take around 7 to 15 days.

Yes, we do have instalment options available for the course fees. To avail instalments, please get in touch with us at hello@zeolearn.com. The team shall explain how the instalments work and would provide the timelines for your case.

Usually we allow payment in 2 to 3 instalments but have all to be paid before you complete the course.

Project

Handwriting digit recognition using CNN with TensorFlow.

This project will help build a model using Convolutional Neural Network to recognize handwriting.

Read More

Build a time series model to forecast future values using LSTM.

A time series  is a sequence taken at successive equally spaced points in time. 

Read More

Build Recommender System for a Retail Chain to recommend the right products to its users

You do not need a market research team to know what your customers are willing to buy. Netflix is a big example, having successfully used recommender system to recommend movies to its viewers. 

Read More

Have More Questions?