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Deep Learning Course Overview

To know about PyTorch, auto-encoders, and more, take the Deep Learning Course with Tensorflow training in Chicago. Clubbed with Deep Learning with Keras and Tensorflow certification, this Deep Learning Course with Tensorflow course in Chicago will help you assemble profound learning models and deploy your deep learning project.

Deep Learning Course 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 this Deep Learning 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!
  • 8X higher live interaction in live online classes by industry experts
  • Real-life industry-based projects
  • 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 taking the Deep Learning Course with Tensorflow training in Chicago, you know about the global profound learning framework market that is expected to increase in 2028, with a steady CAGR of 39.1%. Deep Learning Course with Tensorflow course in Chicago stimulates the machine's calculations and deep learning with each of its capabilities.

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

Training Options

online Bootcamp

$ 1,499

  • 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 in Chicago from:-
9th Dec: Weekend Class
Show all classes

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

Deep Learning Course Curriculum

Eligibility

The Deep Learning Course with Tensorflow training in Chicago can help you advance your career to the next level. Interest in talented Deep Learning Engineers is exploding across a wide range of industries, making our Deep Learning course in NYC with Keras and Tensorflow credentials ideal for professionals at the intermediate to advanced levels. We recommend this Deep Learning Course with Tensorflow course in Chicago to Software Engineers, Data Scientists, Data Analysts, and statisticians interested in supervised learning.
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Pre-requisites

You can learn programming skills by taking the Deep Learning Course with Tensorflow course in Chicago. Members of this Deep Learning course in NYC should have a basic understanding of programming, a reasonable knowledge of the principles of insights and arithmetic, and a sound awareness of AI concepts. You can go to the next programming stage by taking the Deep Learning Course with Tensorflow training in Chicago.
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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

      32:48Preview
      • 2.1 Learning Objectives
        07:00
      • 2.2 Introduction to TensorFlow
        07:00
      • 2.3 TF2x and Eager Execution
        04:21
      • 2.4 Tensorflow Hello World
      • 2.5 Linear Regression With Tensorflow
      • 2.6 Logistic Regression With Tensorflow
      • 2.7 Intro to Deep Learning
        02:39
      • 2.8 Deep Neural Networks
        11:48

    • Lesson 3 - Convolutional Networks

      21:51Preview
      • 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 LTSM Basics
      • 4.6 Applying RNNs to Language Modeling
        07:38
      • 4.7 LSTM Language 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 with MNIST

    • Lesson 6 - Autoencoders

      13:52
      • 6.1 Learning Objectives
        04:51
      • 6.2 Intro to Autoencoders
        04:51
      • 6.3 Autoencoder Structure
        04:10
      • 6.4 Autoencoders

    • 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

      03:11Preview
      • Introduction
      • Accessing Practice Lab
        03:11

    • 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

    • Lesson 01: Course Introduction

      06:23Preview
      • 1.01 About Simplilearn
        00:28
      • 1.02 Introduction to Mathematics
        01:18
      • 1.03 Types of Mathematics
        02:39
      • 1.04 Applications of Math in Data Industry
        01:17
      • 1.05 Learning Path
        00:25
      • 1.06 Course Components
        00:16

    • Lesson 02: Probability and Statistics

      32:38Preview
      • 2.01 Learning Objectives
        00:29
      • 2.02 Basics of Statistics and Probability
        03:08
      • 2.03 Introduction to Descriptive Statistics
        02:12
      • 2.04 Measures of Central Tendencies​
        04:50
      • 2.05 Measures of Asymmetry
        02:24
      • 2.06 Measures of Variability​
        04:55
      • 2.07 Measures of Relationship​
        05:22
      • 2.08 Introduction to Probability
        08:36
      • 2.09 Key Takeaways
        00:42
      • 2.10 Knowledge check

    • Lesson 03: Coordinate Geometry

      06:31
      • 3.01 Learning Objectives
        00:35
      • 3.02 Introduction to Coordinate Geometry​
        03:16
      • 3.03 Coordinate Geometry Formulas​
        01:51
      • 3.04 Key Takeaways
        00:49
      • 3.05 Knowledge Check

    • Lesson 04: Linear Algebra

      29:53Preview
      • 4.01 Learning Objectives
        00:29
      • 4.02 Introduction to Linear Algebra
        03:21
      • 4.03 Forms of Linear Equation
        05:21
      • 4.04 Solving a Linear Equation
        05:21
      • 4.05 Introduction to Matrices
        02:05
      • 4.06 Matrix Operations
        07:07
      • 4.07 Introduction to Vectors
        01:00
      • 4.08 Types and Properties of Vectors
        01:52
      • 4.09 Vector Operations
        02:39
      • 4.10 Key Takeaways
        00:38
      • 4.11 Knowledge Check

    • Lesson 05: Eigenvalues Eigenvectors and Eigendecomposition

      08:56Preview
      • 5.01 Learning Objectives
        00:29
      • 5.02 Eigenvalues
        01:19
      • 5.03 Eigenvectors
        04:09
      • 5.04 Eigendecomposition
        02:21
      • 5.05 Key Takeaways
        00:38
      • 5.06 Knowledge Check

    • Lesson 06: Introduction to Calculus

      09:47Preview
      • 6.01 Learning Objectives
        00:30
      • 6.02 Basics of Calculus
        01:20
      • 6.03 Differential Calculus
        03:01
      • 6.04 Differential Formulas
        01:01
      • 6.05 Integral Calculus
        02:33
      • 6.06 Integration Formulas
        00:47
      • 6.07 Key Takeaways
        00:35
      • 6.08 Knowledge Check

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 in Chicago

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

    Following the successful completion of the Deep Learning course in NYC with Tensorflow preparation, Simplilearn will provide you with both the Deep Learning Course with Tensorflow training in Chicago and an industry-recognized course finishing endorsement with perpetual legitimacy. The Deep Learning Course with Tensorflow training in Chicago is quite beneficial from an exam standpoint.

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

    Professionals must complete the following tasks to obtain Deep Learning using TensorFlow certification:
    • Complete the Deep Learning Course with Tensorflow training in Chicago.
    • In NYC, attend a comprehensive Deep Learning course with Tensorflow assistance.
    • For any of the tasks offered, complete and receive an assessment.
    Candidates can unlock the Simplilearn credential by taking the Deep Learning Course with Tensorflow course in Chicago.

  • What is the fee for the TensorFlow Developer certification exam?

    After taking the Deep Learning Course with Tensorflow training in Chicago, you will ease with the exam. The TensorFlow Developer certification exam cost is $100, including one exam attempt and one test attempt. You have a half year from the date of purchase of The Deep Learning Course with Tensorflow course in Chicago to take the test before your purchase expires.

  • What is the duration of the TensorFlow Developer certification exam?

    The Deep Learning Course with Tensorflow training in Chicago can help you advance and attain the next level in your career. Once you begin the TensorFlow certification exam, you will have 5 hours to complete and submit it. Suppose you do not submit your responses within 5 hours. In that case, the portal will do it on your behalf once the time restriction has passed—the Deep Learning Course with Tensorflow course in Chicago aids in completing certification tests.

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

    You will take the TensorFlow Developer certification exam three times. You should work extensively on the fundamental principles of AI, ML, Deep-Learning, and Big Data Analytics, followed by the Deep Learning Course with Tensorflow Training in Chicago. You should enroll in the Deep Learning Course with Tensorflow training in Chicago, an online course.

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

    The Deep Learning Course with Tensorflow training in Chicago will help you create a clear image of what you need. To take the TensorFlow affirmation test, you must complete the following minor requirements: 4 GB RAM, 2.5 GB plate space plus 1 GB for shops, 1024 x 768 screen resolution, and The Working framework - any Linux distribution that supports Gnome, KDE, or Unity DE, or any authoritatively distributed 64-bit form of Microsoft Windows 8 or later, macOS 10.13 or later is described in the Deep Learning Course with Tensorflow course in Chicago.

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

    The following are some of the advantages of taking the TensorFlow accreditation test:

    • The Deep Learning Course with Tensorflow course in Chicago will help you learn new Machine Learning skills. This certification exam will help you improve your Machine Learning skills.
    • The TensorFlow people group insists on being perceived by the Deep Learning Course with Tensorflow training in Chicago. The TensorFlow individual’s group will identify you once your registration is confirmed.
    • Demonstrate your abilities - The TensorFlow certification demonstrates your knowledge of the subject as well as your ability.

  • How do I crack the Tensorflow Developer certification exam?

    The easiest method to pass the TensorFlow Developer certification exam is to take our  Deep Learning Course with Tensorflow training in Chicago and a Deep Learning course in NYC. You'll be able to register for the TensorFlow developer certification exam once you've completed the  Deep Learning Course with Tensorflow course in Chicago. 

    There will be five categories on the test, and students will have to complete five models, one from each category. The fundamental machine learning model, a model from a learning dataset, CNN with a real-world image dataset, NLP Text Classification with a real-world text dataset, and Sequence Model with a real-world numeric dataset are the categories covered in the Deep Learning Course Tensorflow Training.  Among the types covered in the Deep Learning Course with Tensorflow training in Chicago are the fundamental machine learning model, a model from a learning dataset, CNN with a real-world image dataset, NLP Text Classification with a real-world text dataset, and Sequence Model with a real-world numeric dataset. Candidates can complete this test on any PC that meets the PyCharm IDE's requirements.

Deep Learning Training Reviews

  • A.Anthony Davis

    A.Anthony Davis

    Kingston

    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

    Bangalore

    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., Kolkata

    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

Deep Learning 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 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 in Chicago 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.

  • Why is Deep Learning important?

    Companies are gathering a massive amount of data every day and analyzing them to draw meaningful business insights. Most of that data is in an unstructured format, i.e. in the form of text, image, audio, and video rather than numerical. Deep learning is quite effective for analyzing such types of data and has become vitally important for business decision making. With our Deep Learning Training with TensorFlow & Keras certification, you can learn all the essential deep learning concepts from scratch.

  • What kind of careers can I pursue with a background in Deep Learning?

    With the relevant skills that you gain from our Deep Learning course, you can apply for top job roles like Machine Learning Engineer, Data Scientist, Business Intelligence Developer, NLP Scientist, and more.

  • Why should you take this Deep Learning course?

    Deep learning skills are in high demand and offer professionals a clear edge over others when applying for top related job roles like Machine Learning Engineer, Data Scientist, or NLP Specialist. Requiring a high level of technical understanding, one may not find it easy to learn deep learning through self-study. Taking up this Deep Learning course is a better option where you get the right guidance from industry experts.

  • What is online classroom training?

    All of the TensorFlow training classes are conducted via live online streaming. These classes for the TensorFlow course 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 TensorFlow in your first attempt. They engage students proactively to ensure the Deep Learning 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 TensorFlow course 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 Deep Learning course online with us.

  • How do I enroll for the Deep Learning course?

    You can enroll for this Deep Learning Training 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 Deep Learning 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.

  • What is the recommended learning path after completing a Deep Learning course?

    You can either enroll in our Artificial Intelligence certification course or if you are looking to get the University certificate, you can enroll in the Post Graduate Program in AI and Machine Learning.

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  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

This course has been upgraded to

Post Graduate Program in AI and Machine Learning

  • Simplilearn Career Service helps you get noticed by top hiring companies
  • Program completion certificate from Purdue University and Simplilearn
  • Gain exposure to ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools