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Key Features

 

 

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Instructor-led Training
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Self-paced Videos
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Project Work & Exercises
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Exhaustive Hands-on Sessions
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24 x 7 Lifetime Support & Access
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Certification and Job Assistance

About The Program



If you are good in Python and want to start your journey towards the path of a Data Scientist then this course is definitely for you


  • The course is packed with the algorithms based on latest TensorFlow 2.0
  • Keras is now integrated with TensorFlow 2.0 thereby making it more powerful
  • Writing codes in TensorFlow is much more easier as compared to the previous version
  • TensorFlow 2.0 is now the most widely used library for Deep Learning
  • The course will gIve you a combined taste of text and image processing

After completing this Deep Learning Certification Course, you should be able to:


  • Get yourself introduced and trained with TensorFlow 2.0.
  • Understand the concept of Single Layer and Multi Layer Perceptron by implementing them in Tensorflow 2.0
  • Learn about the working of CNN algorithm and classify the image using the trained model
  • Grasp the concepts on important topics like Transfer Learning, RCNN, Fast RCNN, RoI Pooling, Faster RCNN, and Mask RCNN
  • Understand the concept of Boltzmann machine and Auto Encoders
  • Implement Generative Adversarial Network in TensorFlow 2.0
  • Work on Emotion and Gender Detection project and strengthen your skill on OpenCV and CNN
  • Understand the concept of RNN, GRU, and LSTM
  • Perform Auto-Image Captioning using CNN and LSTM

The Deep Learning Training is for all the professionals who are passionate about Deep Learning and want to go ahead and make their career as a Deep Learning Engineer or a Data Scientist. It is best suited for individuals who are:


  • Developers aspiring to be a 'Data Scientist'
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Deep Learning Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • Analysts wanting to understand Data Science methodologies
  • Basic programming knowledge in Python
  • Concepts about Machine Learning

    • To help you brush up these skills, you will get the following self-paced modules as pre-requisites in your LMS:

  • Python for AI-ML
  • Statistics and Machine Learning

Tools & Packages

 
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Deep Learning Course Modules


Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.


Learning Objective: At the end of this Deep Learning Training module, you will be able to understand the concepts of Deep Learning and learn how it differs from machine learning. This Deep Learning Certification module will also brief you out on implementing the concept of single-layer perceptron.


Topics:

  • What is Deep Learning?
  • Curse of Dimensionality
  • Machine Learning vs. Deep Learning
  • Use cases of Deep Learning
  • Human Brain vs. Neural Network
  • What is Perceptron?
  • Learning Rate
  • Epoch
  • Batch Size
  • Activation Function
  • Single Layer Perceptron

Learning Objective: At the end of this module, you should be able to get yourself introduced with TensorFlow 2.x. You will install and validate TensorFlow 2.x by building a Simple Neural Network to predict handwritten digits and using Multi-Layer Perceptron to improvise the accuracy of the model.


Topics:

  • Introduction to TensorFlow 2.x
  • Installing TensorFlow 2.x
  • Defining Sequence model layers
  • Activation Function
  • Layer Types
  • Model Compilation
  • Model Optimizer
  • Model Loss Function
  • Model Training
  • Digit Classification using Simple Neural Network in TensorFlow 2.x
  • Improving the model
  • Adding Hidden Layer
  • Adding Dropout
  • Using Adam Optimizer

Learning Objective: At the end of this module, you will be able to understand how and why CNN came into existence after MLP and learn about Convolutional Neural Network (CNN) by exploring the theory behind how CNN is used to predict ‘X’ or ‘O’. You will also use CNN VGG-16 using TensorFlow 2 and predict whether the given image is of a ‘cat’ or a ‘dog’ and save and load a model’s weight.


Topics:

  • Image Classification Example
  • What is Convolution
  • Convolutional Layer Network
  • Convolutional Layer
  • Filtering
  • ReLU Layer
  • Pooling
  • Data Flattening
  • Fully Connected Layer
  • Predicting a cat or a dog
  • Saving and Loading a Model
  • Face Detection using OpenCV

Learning Objective: At the end of this module, you will be able to understand the concept and working of RCNN and figure out the reason why it was developed in the first place. The module will cover various important topics like Transfer Learning, RCNN, Fast RCNN, RoI Pooling, Faster RCNN, and Mask RCNN.


Topics:

  • Regional-CNN
  • Selective Search Algorithm
  • Bounding Box Regression
  • SVM in RCNN
  • Pre-trained Model
  • Model Accuracy
  • Model Inference Time
  • Model Size Comparison
  • Transfer Learning
  • Object Detection – Evaluation
  • mAP
  • IoU
  • RCNN – Speed Bottleneck
  • Fast R-CNN
  • RoI Pooling
  • Fast R-CNN – Speed Bottleneck
  • Faster R-CNN
  • Feature Pyramid Network (FPN)
  • Regional Proposal Network (RPN)
  • Mask R-CNN

Learning Objective: At the end of this module, you should be able to understand what a Boltzmann Machine is and how it is implemented. You will also learn about what an Autoencoder is, what are its various types, and understand how it works.


Topics:

  • What is Boltzmann Machine (BM)?
  • Identify the issues with BM
  • Why did RBM come into picture?
  • Step by step implementation of RBM
  • Distribution of Boltzmann Machine
  • Understanding Autoencoders
  • Architecture of Autoencoders
  • Brief on types of Autoencoders
  • Applications of Autoencoders

Learning Objective: At the end of this module, you should be able to understand what generative adversarial model is and how it works by implementing step by step Generative Adversarial Network.


Topics:

  • Which Face is Fake?
  • Understanding GAN
  • What is Generative Adversarial Network?
  • How does GAN work?
  • Step by step Generative Adversarial Network implementation
  • Types of GAN
  • Recent Advances: GAN

Learning Objective: At the end of this module, you will be able to classify each emotion shown in the facial expression into different categories by developing a CNN model for recognizing the facial expression of the images and predict the facial expression of the uploaded image. During the project implementation, you will also be using OpenCV and Haar Cascade File to check the emotion in real-time.


Topics:

  • Where do we use Emotion and Gender Detection?
  • How does it work?
  • Emotion Detection architecture
  • Face/Emotion detection using Haar Cascade
  • Implementation on Colab

Learning Objective: After completing this module, you should be able to distinguish between Feed Forward Network and Recurrent neural network (RNN) and understand how RNN works. You will also understand and learn about GRU and finally implement Sentiment Analysis using RNN and GRU.


Topics:

  • Issues with Feed Forward Network
  • Recurrent Neural Network (RNN)
  • Architecture of RNN
  • Calculation in RNN
  • Backpropagation and Loss calculation
  • Applications of RNN
  • Vanishing Gradient
  • Exploding Gradient
  • What is GRU?
  • Components of GRU
  • Update gate
  • Reset gate
  • Current memory content
  • Final memory at current time step

Learning Objective: After completing this Deep Learning Certification module, you should be able to understand the architecture of LSTM and the importance of gates in LSTM. You will also be able to differentiate between the types of sequence-based models and finally increase the efficiency of the model using BPTT.


Topics

  • What is LSTM?
  • Structure of LSTM
  • Forget Gate
  • Input Gate
  • Output Gate
  • LSTM architecture
  • Types of Sequence-Based Model
  • Sequence Prediction
  • Sequence Classification
  • Sequence Generation
  • Types of LSTM
  • Vanilla LSTM
  • Stacked LSTM
  • CNN LSTM
  • Bidirectional LSTM
  • How to increase the efficiency of the model?
  • Backpropagation through time
  • Workflow of BPTT

Learning Objective: After completing this module, you should be able to implement Auto Image captioning using pre-trained model Inception V3 and LSTM for text processing.


Topics:

  • Auto Image Captioning
  • COCO dataset
  • Pre-trained model
  • Inception V3 model
  • Architecture of Inception V3
  • Modify last layer of pre-trained model
  • Freeze model
  • CNN for image processing
  • LSTM or text processing

GET AHEAD WITH DEEPNEURON DEVOPS CERTIFICATE

Earn your Deep Learning certificate

Our Deep Learning program is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.

Differentiate yourself with a Deep Learning Certificate

The knowledge and Deep Learning skills you've gained working on projects, simulations, case studies will set you ahead of the competition.

Share your achievement

Talk about it on Linkedin, Twitter, Facebook, boost your resume, or frame it - tell your friends and colleagues about it.

Data Scientist


DeepNeuron Testimonials


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-Anand Shyam,Data Scientist
"Am proud to be one of your students.I learnt Big data, Data Science and thoroughly enough.Here every session was great and very valuable. It was extremely useful and we... learnt data science concept more than what I expected."
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-Y.Naidu,Project Manager
"The hands-on training sessions were very good.Your excellent explanation on statistics super....Great job with your ppts!.You have covered most of the algorithms which were never covered even by reputed institutions."
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-Bhuvanesh,Senior Data Scientist
"The Training Which I Underwent From Sathya Who Is In This Industry For Long Is The Best....He Teaches Things Practically And How To Apply The Techniques Which Is Most Important , Its Worth Everything."
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-Boobathy,Project Manager
"Done An Awesome Job. Am thoroughly Satisfied With The Training Provided By Sasken(A) Sathya....End of the day I felt like "I am data scientist".I recommend whoever would like to be a Big Data Analytics/ Data Scientist."
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-Shiva Project Manager, CTS
I have never seen such an excellent faculty like Mr.sasken who is the best faculty . I feel very lucky and blessed to be a student ...of sasken , I would recommend anybody who has a passion..
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-ThulasiDass,CSC ,Chennai
"without doubt that his (Sasken (a) Sathya) training is the best compared to others. The way he explains statistical concepts in the intuitive way is very good, so that a person without any statistics background can understand and remember the terminology very easily....The training covered all of my expectations and helping me in the process of changing my carrier from legacy based technical skill sets to emerging hot technical sill sets The content of the material, code’s(R, Python& SAS) and datasets he uses to explain various machine learning algorithms are the best compared other training institutes/trainers.
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-Babu Lakshman -Cognizant
"The course is a combination of various data science concepts such as machine learning, visualization, data mining, programming, data munging. It gives hands on experience with statistical modelling techniques. The course has real world examples of how...analytics have been used to significantly improve business performance. Apart from machine learning the course introduces to map reduce, spark and Hadoop ecosystems, bias vs variance, learning curves, etc. The course gives a good understanding of R and also good introduction to Python and SAS."
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-Vijay , HCL
"The training was very perfect, as the teaching approach was very clear and the pace is slow, The way the faculty explain each and every topics in detail with simples examples helped me to understand ...even the complex algorithms. The training material is really good and helpful."

FAQs

  • What if I have queries after I complete this Deep Learning Certification course?

    Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.

  • How soon after Signing up would I get access to the Learning Content?

    Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover, the access to our 24x7 support team will be granted instantly as well. You can start learning right away.

  • Who is my trainer and what is his selection process?

    Our subject matter experts (SMEs) are experienced with industry expertise in their preferred domain and technologies. Our selection process is intriguing in reference to the course modules where we seek professional experts who have ideas and worked in the same technology for creating and deployment of applications in real-time. Professional experts SMEs will guide you through the entire module and will also make you get exposed to practice-based learning.

  • What can I expect after the Deep Learning course accomplishment?

    After the completion of the Deep Learning training course, you will gain expert knowledge to master the Deep Learning and will be a proficient player to tap Deep Learning tools at a depth level. Moreover, you will be part of the DeepNeuron community to leverage the knowledge-base shared by the members around the globe. You will also earn an industry-recognized Deep Learning certification from DeepNeuron.

  • What are the different modes of Deep Learning training that DeepNeuron provides?

    DeepNeuron offers Instructor-led online training delivered by highly experienced Deep Learning experts holding more than 10+ years of industry experience. Our separate cherry-picked pool of subject matter experts work with respective courses in curating the best contents and study materials for greater learning experience. Take our FREE Demo sessions to witness our trainer’s expertise.

  • What kind of projects are included as part of this Deep Learning training?

    As a part of this Data Science online course, in collaboration with IBM, you will receive the following:

    • Lifetime access to e-learning course syllabus for all of the Data Science courses included in the learning path (*only for Data2bussinessinsights courses)
    • Industry-recognized certificates from IBM*(for IBM courses) and Simplilearn upon successful completion of the program
    • USD 1200 worth of IBM cloud credits that you can leverage for hands-on exposure
    • Access to IBM cloud platforms featuring IBM Watson and other software for 24/7 practice

  • *For which all courses will I get certificates from IBM?

    Following are the list of courses for which you will get IBM certificates:

    • R Programming for Data Science
    • Python for Data Science

  • How do I earn the Master’s certificate?

    Upon completion of the following minimum requirements, you will be eligible to receive the Data Scientist Master’s certificate that will testify to your skills as an expert in Data Science.

    Course

    Course completion certificate

    		 Criteria
    Data Science and Statistics Fundamentals Required 85% of Online Self-paced completion
    Data Science with R Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation in at least 1 project
    Data Science with SAS Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation in at least 1 project
    Data Science with Python Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in course-end assessment and successful evaluation in at least 1 project
    Machine Learning and Tableau Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and successful evaluation in at least 1 project
    Big Data Hadoop and Spark Developer Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation of at least 1 project
    Capstone Project Required Attendance of 1 Live Virtual Classroom and successful completion of the capstone project

  • How do I enroll for the Data Scientist course?

    You can enroll in this Data Science 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 course price after deducting an administration fee. To learn more, please read our Refund Policy.

  • I am not able to access the online Data Science courses. Who can help me?

    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.

  • Who are the instructors and how are they selected?

    All of our highly qualified Data Science trainers are 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.

  • 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.

  • 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.

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  • + (91) 98407 62315
  • + (91) 98400 14739
  • 76/26 Indra nagar 1st Avenue, Adyar, Chennai 600 020

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