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Deep Learning Course Overview

In this Deep Learning course with Keras and Tensorflow certification training, you will become familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and more. Upon completion, you will be able to build deep learning models, interpret results, and build your own deep learning project.

Tensorflow Certification Key Features

100% Money Back Guarantee
No questions asked refund*

At Simplilearn, we value the trust of our patrons immensely. But, if you feel that a course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • 34 hours of Blended Learning
  • Real-life industry-based projects
  • 24/7 support with dedicated project mentoring sessions
  • Flexibility to choose classes
  • Dedicated mentoring session from our Industry expert faculties

Skills Covered

  • Keras and TensorFlow Framework
  • PyTorch and its elements
  • Image Classification
  • Artificial Neural Networks
  • Autoencoders
  • Deep Neural Networks
  • Conventional Neural Networks
  • Recurrent Neural Networks
  • ADAM Adagrad and Momentum

Benefits

By 2023, the Deep Learning market size is estimated to be worth $18.16 billion, growing at a rate of 41.7 percent from 2018 to 2023. Industrial sectors like healthcare, information technology, fin-tech, and e-commerce need professionals with deep learning skills.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Data Scientist
    Annual Salary
    $83KMin
    $113KAverage
    $154KMax
    Source: Glassdoor
    Hiring Companies
    Accenture
    Oracle
    Microsoft
    Walmart
    Amazon
    Source: Indeed
  • AI Engineer
    Annual Salary
    $51KMin
    $72KAverage
    $110KMax
    Source: Glassdoor
    Hiring Companies
    Qualcomm
    Nvidia
    LarsenAndTurbo
    Honeywell
    Source: Indeed

Training Options

online Bootcamp

$ 799

  • 90 days of flexible access to online classes
  • Lifetime access to high-quality self-paced e-learning content and live class recordings
  • 24x7 learner assistance and support
  • Classes starting from:-
5th Dec: Weekend Class
Enroll now

Corporate Training

Customized to your team's needs

  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Tensorflow Certification Course Curriculum

Eligibility

Demand for skilled Deep Learning Engineers is booming across a wide range of industries, making this Deep Learning course with Keras and Tensorflow certification training well-suited for professionals at the intermediate to advanced level. We recommend this course particularly for Software Engineers, Data Scientists, Data Analysts, and Statisticians with an interest in deep learning.
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Pre-requisites

Participants in this Deep Learning online course should have familiarity with programming fundamentals, a fair understanding of the basics of statistics and mathematics, and a good understanding of machine learning concepts.
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Course Content

  • Section 1 - Deep Learning with Tensor Flow (Self Learning)

    Preview

    • Lesson 1 - Welcome!

      02:06Preview
      • 1.1 Welcome!
        02:06
      • 1.2 Learning Objectives

    • Lesson 2 - Introduction to Tensorflow

      31:55Preview
      • 2.1 Learning Objectives
        07:00
      • 2.2 Introduction to TensorFlow
        07:00
      • 2.3 TensorFlow's Hello World
        03:28
      • 2.4 Tensorflow Hello World
      • 2.5 Linear Regression With Tensorflow
      • 2.6 Logistic Regression With Tensorflow
      • 2.7 Activation Functions
      • 2.8 Intro to Deep Learning
        02:39
      • 2.9 Deep Neural Networks
        11:48

    • Lesson 3 - Convolutional Networks

      21:51
      • 3.1 Learning Objectives
      • 3.2 Intro to Convolutional Networks
        04:37
      • 3.3 CNN for Classifications
        04:09
      • 3.4 CNN Architecture
        13:05
      • 3.5 Understanding Convolutions
      • 3.6 CNN with MNIST Dataset

    • Lesson 4 - Recurrent Neural Network

      24:43Preview
      • 4.1 Learning Objectives
        03:06
      • 4.2 The Sequential Problem
        03:06
      • 4.3 The RNN Model
        05:28
      • 4.4 The LSTM Model
        05:25
      • 4.5 Applying RNNs to Language Modeling
        07:38
      • 4.6 LTSM Basics
      • 4.7 MNIST Data Classification With RNN/LSTM
      • 4.8 Applying RNN/LSTM to Language Modelling
      • 4.9 Applying RNN/LSTM to Character Modelling

    • Lesson 5 - Restricted Boltzmann Machines (RBM)

      14:14
      • 5.1 Learning Objectives
        04:29
      • 5.2 Intro to RBMs
        04:29
      • 5.3 Training RBMs
        05:16
      • 5.4 RBM MNIST
      • 5.5 Collaborative Filtering With RBM

    • Lesson 6 - Autoencoders

      17:20Preview
      • 6.1 Learning Objectives
        04:51
      • 6.2 Intro to Autoencoders
        04:51
      • 6.3 Applying RNNs to Language Modelling
        07:38
      • 6.4 Autoencoders
      • 6.5 DBN MNIST

    • Lesson 7 - Course Summary

      02:17
      • 7.1 Course Summary
        02:17
      • Unlocking IBM Certificate

  • Section 2 - Deep Learning with Keras and Tensor Flow (Live Classes)

    Preview

    • Lesson 1 - Course introduction

      • Introduction

    • Lesson 2 - AI and Deep learning introduction

      • What is AI and Deep learning
      • Brief History of AI
      • Recap: SL, UL and RL
      • Deep learning : successes last decade
      • Demo & discussion: Self driving car object detection
      • Applications of Deep learning
      • Challenges of Deep learning
      • Demo & discussion: Sentiment analysis using LSTM
      • Fullcycle of a deep learning project
      • Key Takeaways
      • Knowledge Check

    • Lesson 3 - Artificial Neural Network

      • Biological Neuron Vs Perceptron
      • Shallow neural network
      • Training a Perceptron
      • Demo code: Perceptron ( linear classification) (Assisted)
      • Backpropagation
      • Role of Activation functions & backpropagation
      • Demo code: Backpropagation (Assisted)
      • Demo code: Activation Function (Unassisted)
      • Optimization
      • Regularization
      • Dropout layer
      • Key Takeaways
      • Knowledge Check
      • Lesson-end Project (MNIST Image Classification)

    • Lesson 4 - Deep Neural Network & Tools

      • Deep Neural Network : why and applications
      • Designing a Deep neural network
      • How to choose your loss function?
      • Tools for Deep learning models
      • Keras and its Elements
      • Demo Code: Build a deep learning model using Keras (Assisted)
      • Tensorflow and Its ecosystem
      • Demo Code: Build a deep learning model using Tensorflow (Assisted)
      • TFlearn
      • Pytorch and its elements
      • Key Takeaways
      • Knowledge Check
      • Lesson-end Project: Build a deep learning model using Pytorch with Cifar10 dataset

    • Lesson 5 - Deep Neural Net optimization, tuning, interpretability

      • Optimization algorithms
      • SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
      • Batch normalization
      • Demo Code: Batch Normalization (Assisted)
      • Exploding and vanishing gradients
      • Hyperparameter tuning
      • Interpretability
      • Key Takeaways
      • Knowledge Check
      • Lesson-end Project: Hyperparameter Tunning With Keras Tuner

    • Lesson 6 - Convolutional Neural Network

      • Success and history
      • CNN Network design and architecture
      • Demo code: CNN Image Classification (Assisted)
      • Deep convolutional models
      • Key Takeaways
      • Knowledge Check
      • Lesson-end Project: Image Classification

    • Lesson 7 - Recurrent Neural Networks

      • Sequence data
      • Sense of time
      • RNN introduction
      • LSTM ( retail sales dataset kaggle)
      • Demo code: Stock Price Prediction with LSTM (Assisted)
      • Demo code: Multiclass Classification using LSTM (Unassisted)
      • Demo code: Sentiment Analysis using LSTM (Assisted)
      • GRUs
      • LSTM Vs GRUs
      • Key Takeaways
      • Knowledge Check
      • Lesson-end Project: Stock Price Forecasting

    • Lesson 8 - Autoencoders

      • Introduction to Autoencoders
      • Applications of Autoencoders
      • Autoencoder for anomaly detection
      • Demo code: Autoencoder model for MNIST data (Assisted)
      • Key Takeaways
      • Knowledge Check
      • Lesson-end Project: Anomaly detection with Keras

  • Section 3 - Practice Projects

    Preview

    • Practice Projects

      • PUBG Players Finishing Placement Prediction
  • Free Course

  • Math Refresher

    Preview

    • Math Refresher

      30:36Preview
      • Math Refresher
        30:36
  • Free Course

  • Deep Learning Fundamentals

    Preview

    • Lesson 1 - Learning Objectives

      • Learning Objectives

    • Lesson 2 - Introduction to Deep Learning

      22:31Preview
      • Learning Objectives
      • 1.1 Deep Learning: The Series Introduction
        03:46
      • 1.2 What is a Neural Network
        06:28
      • 1.3 Three Reasons to go Deep
        03:56
      • 1.4 Your choice of Deep Net
        02:58
      • 1.5 An Old Problem
        05:23

    • Lesson 3 - Deep Learning Models

      22:56Preview
      • Learning Objectives
      • 2.1 Restricted Boltzmann Machines
        04:50
      • 2.2 Deep Belief Nets
        04:31
      • 2.3 Convolutional Nets
        08:15
      • 2.4 Recurrent Nets
        05:20

    • Lesson 4 - Additional Deep Learning Models

      19:13Preview
      • Learning Objectives
      • 3.1 Autoencoders
        03:51
      • 3.2 Recursive Neural Tensor Nets
        05:49
      • 3.3 Use Cases
        09:33

    • Lesson 5 - Deep Learning Platforms & Libraries

      25:37Preview
      • Learning Objectives
      • 4.1 What is a Deep Net Platform?
        03:41
      • 4.2 H2O.ai
        03:42
      • 4.3 Dato GraphLab
        03:32
      • 4.4 What is a Deep Learning Library?
        01:58
      • 4.5 Theano
        03:21
      • 4.6 Caffe
        02:48
      • 4.7 Tensorflow
        06:35
      • Unlocking IBM Certificate

Contact Us

1-844-532-7688

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Project

  • Project 1

    PUBG Players Finishing Placement Prediction

    Create a model that predicts players’ finishing placement based on their final stats, on a scale of 1 (first place) to 0 (last place).

  • Project 2

    Lending Club Loan Data Analysis

    Create a model that predicts whether a loan will go into default using the historical data.

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Tensorflow Exam & Certification

Deep Learning Certificate

  • Who provides the certification and how long is it valid for?

    Upon successful completion of the Deep Learning course with Tensorflow training, you will be awarded an industry-recognized course completion certificate from Simplilearn which has lifelong validity.

  • What do I need to do to unlock my Simplilearn certificate?

    To obtain the Deep Learning with TensorFlow certification, you will need to:
    • Attend one complete batch of Deep Learning course with Tensorflow training
    • Complete and attain evaluation of any one of the given projects

  • What is the fee for TensorFlow Developer certification exam?

    The TensorFlow Developer certification exam costs $100, which includes one exam attempt.

  • What is the duration of TensorFlow Developer certification exam?

    Once you start the Tensorflow certification exam, you will have 5 hours to complete it and submit it. However, if you do not submit the answers within 5 hours the portal will automatically submit your answers once the time completes.

  • How many attempts do I have to pass Tensorflow Developer certification exam?

    You will have three attempts to pass the TensorFlow Developer exam.

  • What are the system requirements for taking the Tensorflow Developer certification exam?

    To be able to appear TensorFlow certification exam, here are the minimum requirements that you need to have:

    • RAM - 4 GB
    • Disk Space - 2.5 GB and another 1 GB for caches
    • Monitor resolution - 1024 x 768
    • Operating system - officially released 64-bit versions of Microsoft Windows 8 or later, macOS 10.13 or later, or any Linux distribution that supports Gnome, KDE, or Unity DE.

  • What are the benefits of taking Tensorflow Developer certification exam?

    Here are the benefits of taking TensorFlow certification exam:

    • Learn new things about Machine Learning - This certification exam will help you increase your proficiency in Machine Learning.
    • Receive recognition - Once you are certified, it is imperative that you will be recognized by the TensorFlow community
    • Showcase skills - The TensorFlow certification is a testament to the fact that you are well-learned about it, which also is a proof of your skills.

  • How do I crack Tensorflow Developer certification exam?

    The best way to crack the TensorFlow Developer certification exam is by taking up this Deep Learning course. Once you complete the course, you can register and appear for the TensorFlow developer certification exam. During the exam, there will be five categories and students will complete five models, one from each category. The categories include a basic ML model, model from learning dataset, CNN with real-world image dataset, NLP Text Classification with real-world text dataset, and Sequence Model with the real-world numeric dataset. One can participate in this examination with a system that supports the PyCharm IDE requirements. (source: analyticsindiamag)

Tensorflow Certification Reviews

  • A.Anthony Davis

    A.Anthony Davis

    The Simplilearn Data Scientist Master’s Program is an awesome course! You learn how to solve real-world problems, and the wide variety of projects give you hands-on experience to make you industry-ready. The lecturers are experts and share their knowledge energetically. Thank you for an excellent learning experience.

  • Abhishek Tripathi

    Abhishek Tripathi

    Good online content for data science. I completed Data Science with R and Python. The instructors have good knowledge on the subject. Self-learning videos help a lot, too. Thanks, Simplilearn.

  • Angiras Modak

    Angiras Modak

    Associate System Engineer at IBM India Pvt. Ltd.

    Simplilearn is one of the best online training providers available. The trainer was really great in explaining the concepts to the minute detail and also gave multiple real-world examples. The course content was very informative. I understood the concept of CNN. Overall I really enjoyed the training a lot.

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Why Online Bootcamp

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Tensorflow Training FAQs

  • What is Deep Learning?

    Deep Learning also known as Deep Neural Learning, is a subset of machine learning, an application of AI, where machines imitate the workings of the human brain and employ artificial neural networks to process the information.

  • What is TensorFlow?

    TensorFlow is an open source library created and released by google for numerical computation and building deep learning models.

  • Why should I learn Deep Learning?

    In the traditional machine learning, most of the applied features need to be identified by a domain expert in order to reduce the complexity of the data. Whereas the biggest advantage of the Deep Learning algorithm is it tries to learn high-level features from data in an incremental manner, which makes the process simpler and popular. Deep Learning techniques outperform other techniques when the data size is large and complex, and also, this technique is behind many high-end innovations.

  • How do I become a Deep Learning Engineer?

    This Deep Learning with Keras and TensorFlow certification course will give you a complete overview of  Deep Learning concepts, enough to prepare you to excel in your next role as a Deep Learning Engineer. It will help you become familiar with artificial neural networks, PyTorch, autoencoders, and more. At the end of the course, you will get an industry-recognized course completion certificate from Simplilearn, which will be a testament to your skills.

  • What is online classroom training?

    All of the Tensorflow training classes are conducted via live online streaming. They are interactive sessions that enable you to ask questions and participate in discussions during class time.

  • Who are the instructors and how are they selected?

    All of our highly qualified trainers are Deep Learning and Machine Learning industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

  • How will the labs be conducted?

    Simplilearn provides Integrated labs for all the hands-on execution of projects. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.  

  • Is this live training or will I watch pre-recorded videos?

    The Tensorflow certification training is conducted through live streaming. They are interactive sessions that enable you to ask questions and participate in discussions during class time. We do, however, provide recordings of each session you attend for your future reference. Classes are attended by a global audience to enrich your learning experience.

  • What if I miss a class?

    Simplilearn provides recordings of each class of Deep Learning course so you can review them as needed before the next session. With Flexi-pass, Simplilearn gives you access to all classes for 90 days so that you have the flexibility to choose sessions as per your convenience.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

  • Do you provide a money back guarantee for the training programs?

    Yes. We do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support portal.

  • How do I enroll for the Deep Learning course?

    You can enroll for this Deep Learning course on our website and make an online payment using any of the following options: 
    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal
    Once payment is received you will automatically receive a payment receipt and access information via email.

  • If I need to cancel my enrollment, can I get a refund?

    Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, please read our Refund Policy.

  • How can I learn more about this Deep Learning course?

    Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details.
     

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