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Deep Learning and Artificial Intelligence Training Course in Gurgaon

The Digital Universe is expected to reach 44 trillion gigabytes by 2020, we are churning out roughly 3 quintillion bytes of data on daily basis. This will create huge demand of AI professionals. The training will provide you real time scenarios related to analytics such as Prediction Analysis, Linear Regression, Image Processing, Audio & Video Analytics, Text Data Processing, IOT and various Machine Learning & Deep Learning Algorithms.

Instructor Led Training  |  Free Course Repeat  |  Placement Assistance  |  Job Focused Projects  |  Interview Preparation Sessions

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Curriculum

    Course -1: TensorFlow for Deep Learning with MLOps

    Course -2: Keras for Deep Learning with MLOps

    Course -3: Deep learning Computer Vision - CNN, OpenCV, YOLO, SSD & GANs



    • Environmental Setup using YAML
    • Using Conda Packages
    • Numpy
    • Pandas
    • Data Visualization
    • Scikit Learn
    • Introduction to Neural Network
    • Neural Network Activation Functions
      • RELU
      • TANH
      • Signoid
      • Hyperbolic
    • Cost Functions
    • Gradient Descent Back propagation
    • Tensorflow Playground (playground.tensorflow.org)
    • Manually Creating Neural Network
    • Manually creation of Neural Network – Operations
    • Manually creation of Placeholders & variables
    • Manually creation of Session
    • Manual Neural Network – Classification point of View
    • Introduction to tensor flow
    • Tensorflow – graphs
    • Tensorflow – Variables & Placeholders
    • Neural Network using Tensorflow
    • Regression using Tensorflow
    • Classification using Tensorflow
    • Saving & Restoring models using Tensorflow
    • Introduction to CNN
    • Manual Creation of CNN
    • CNN Implementation using Tensorflow
    • Cnn Project Solution using Tensorflow
    • Introduction to RNN
    • Manual creation of RNN
    • Vanishing Gradients
    • LSTM & RGRU
    • Introduction to RNN with Tensorflow API
    • RNN with Tensorflow
    • RNN Plotting
    • Time Series App using RNN
    • Word2Vec using RNN


    • Deep Nets with TensorFlow Abstraction API
    • Deep Nets with TensorFlow Abstraction API – Keras
    • Deep Nets with TensorFlow Abstraction API – Layers
    • Tensorboard
    • Autoencoder Basics
    • Dimensionality reduction with Linear AutoEncoder
    • Linear Autoencoder PCA
    • Stacked Autoencoder
    • Introduction to Reinforcement Learning with OpenAI gym
    • Introduction to OpenAI gym
    • OpenAI Gym setup
    • OpenAI gym Environment Basics
    • OpenAI gym observations
    • OpenAI gym Actions
    • Simple Neural Network game
    • Policy gradient theory Implementation & core base
    • Introduction to GANs
    • GAN Implementation
    • Face Aging GAN


    • ML Deployment Introduction
    • ML Pipeline – Research Environment
    • ML System Architecture
    • Building a reproducible Machine Learning Pipeline
    • Creating a ML Pipeline Application
    • Serving the models via REST API
    • Continuous Integration & Deployment Pipelines
    • Deploying to IAAS (AWS ECS)
    • Machine learning Projects
    • Differential Testing
    • Deploying to PAAS (Heroku) without containers
    • Running Apps with Containers (Docker)
    • Packaging of a Deep Learning Model (Deep Learning Model with Big Data)
    • ANN Introduction
    • The Neuron
    • The Activation Function
    • How do Neural Networks work?
    • How do Neural Network Learn?
    • Gradient Descent
    • Stochastic Gradient Descent
    • Backpropagation
    • Building an ANN
    • Evaluating the ANN
    • Improving the ANN
    • Tuning the ANN
    • Convolutional Networks Introduction
    • What are Convolutional Neural Networks (CNN)?
    • Convolution Operation
    • Various Layers in CNN
    • Relu Layer
    • Pooling Layer
    • Flatening Layer
    • Fully Connected Layer in CNN
    • Softmax & Cross-Entropy
    • Building a CNN
    • Evaluating, Improving and Tuning the CNN
    • RNN Introduction
    • Idea behind Recurrent Neural Networks
    • Vanishing Gradient Porblem
    • LSTM's (Long Short Term Memory)
    • LSTM Variations
    • Building a RNN
    • Evaluating, Improving and Tuning the RNN
    • SOM's Introduction
    • How do Self Organizing Maps work?
    • Live SOM Example
    • Reading an Advanced SOM
    • Building a SOM
    • Mega Case Study
    • Introduction to Boltzmann Machines
    • Energy-Based Modesl (EBM)
    • Restricted Boltzmann Machines(RBM's)
    • Contrastive Divergence
    • Deep Belief Networks (DBN)
    • Deep Boltzmann Machines (DBM)
    • Building a Boltzmann Machine
    • AutoEncoders Introduction
    • Note on Baises
    • Training an Auto Encoder
    • Overcomplete Hidden Layers
    • Sparse Autoencoders
    • Denoising Autoencoders
    • Contractive Autoencoders
    • Stacked Autoencoders
    • Deep Autoencoders
    • Building an Autoencoder
    • ML Deployment Introduction
    • ML Pipeline – Research Environment
    • ML System Architecture
    • Building a reproducible Machine Learning Pipeline
    • Creating a ML Pipeline Application
    • Serving the models via REST API
    • Continuous Integration & Deployment Pipelines
    • Deploying to IAAS (AWS ECS)
    • Machine learning Projects
    • Differential Testing
    • Deploying to PAAS (Heroku) without containers
    • Running Apps with Containers (Docker)
    • Packaging of a Deep Learning Model (Deep Learning Model with Big Data)
    • Introduction to Computer Vision & Deep Learning
    • What is Computer Vision?
    • Intro to OpenCV, OpenVINO & their Limitations
    • Setting Up Deep Learning Virtual Machines
    • Manual Setup on Ubuntu Virtual Machine
    • Handwriting Recognition, Simple Object Classification OpenCV Demo
    • Experiment with a Handwriting Classifier
    • Experiment with a Image Classifier
    • OpenCV Demo Live Sketch with Webcam
    • Setup OpenCV
    • Storing Images on Computers
    • Getting Started with OpenCV
    • Grayscaling - Converting Color Images to Shades of Gray
    • Understanding Color Spaces - Thw Many Ways Color Images Are Stored Digitally
    • Histogram Representation of Images - Visualizing the Component Of Images
    • Creating Images & Drawing on Images - Make Squares , Circles, Polygons & Add Text
    • Transformations, Affine and Non-Affinne
    • Image Translations- Moving Images Up, Down, Left And Right
    • Rotations - How to Spin Our Image Around and Do Horizontal Flipping
    • Scaling, Resizing and Iterpolations - Understanding How Re-sizing Affects Quality
    • Image Pyramids - Another way of Resizing
    • Arithmetic Operations - Brightening & Darkening Images
    • Bitwise Operations - How Image Masking Works
    • Blurring - The Many ways We can Blur images
    • Sharpening - Reverse Your Images Blurs
    • Thresholding (Binarization) - Making Certain Images Areas Black or White
    • Dilation, Erosion, Opening/Closing - Importance of Thickening/Thinning Lines
    • Edge Detection using Image Gradients & Canny Edge Detection
    • Perspective & Affine Transforms - Take An Off Angle Shot & MakeIt Look Top Down
    • Segmentation & Contours - Extract Defined Shaped in our images
    • Sorting Counters - Sort Thoses Shapes By Size
    • Approximate Contours & Finding Their Convex Hull - Clean Up Messy Counters
    • Matching Contour Shapes - Match Shapes in Images Even when distorted
    • Line Detection - Detect Straight Lines Eg : The Lines On A Sudoku Game
    • Circle Detection
    • Blob Detection - Detect The Centre Of Flowers
    • Feature Description Theory - How we digitally Represent Objects
    • Finding CORNERS - Why Corners In Images are important to Object Detection
    • Histogram Of Oriented Gradients - Another Novel Way Of Representing Images
    • HAAR Cascade Classifiers - Learn How Classifiers Work And Why They are amazing
    • Face & Eye Detection - Detect Human Faces & Eyes in Any Image
    • Neural Network Introduction
    • Forward Propagation
    • Activation Functions
    • LOSS Functions
    • Backpropagation & Gradient Descent
    • Backpropagation & Learning Rates
    • Regularization, Overfitting, Generalization
    • Epochs, Iterations & Batch Sizes
    • Measuring Performance & the Confusion Matrix


    CNNs in Detail

    • CNN Introduction
    • Convolution & Images Features
    • Depth, Stride & Padding
    • ReLU
    • Pooling
    • Fully Connected Layer
    • Training CNNs
    • Designing our own CNN
    • Build Handwriting Recognition CNN
    • Plotting LOSS & Accuracy Charts
    • Displaying Model Visually
    • Building Simple Image Classifier CIFAR10
    • Introduction to Visualizing What CNNs 'see' & Filter Visualizations
    • Saliency Maps & Class Activation Maps
    • Filter Visualizations
    • Heat Map Visualizations of Class Activations
    • Data Augmentation Introduction
    • Train Cats Vs Dogs Classifier
    • Boosting Accuracy with Data Augmentation
    • Types of Data Augmentation
    • Introduction to the types of Optimizers, Learning Rates & Callbacks
    • Types Optimizers & Adaptive Learning Rates Methods
    • KERAS Callbacks & Checkpoint, Early Stopping & Adjust Leanring Rates that PI
    • Build a Fruit Classifier


    • Intro to Building Lenet, AlexNet in KERAS & Understand Batch Normalization
    • Build LeNet & test on MNIST
    • Build AlexNet & test on CIFAR10
    • Batch Normalization
    • Build a Clothing & Apparel Classifier (Fashion MNIST)
    • Introduction to ImageNet
    • ImageNet - Experimenting with pre-trained Models in KERAS (VGG16,ResNet50, Mobi)
    • Understanding VGG16 & VGG19
    • Understanding ResNet50
    • Understanding InceptionV3
    • Transfer Learning & Fine Tuning
    • Build a Monkey Breed Classifier with MobileNet using Transfer Learning
    • Build a Flower Classifier with VGG16 using Transfer Learning
    • LittleVGG Introduction
    • Simpsons Character Recognition using LittleVGG
    • Advanced Activation Functions
    • Dying ReLU Problem & Introduction to Leaky ReLU , ELU & PReLUs
    • Advanced Initializations
    • Deep Surveillance Introduction
    • Build an Emotion, Facial Expression Detector
    • Build Emotion/Age/Gender Recognition in our Deep Surveillance Monitor
    • Overview of Image Segmentation & Medical Imaging in U-Net
    • What’s is Segmentation? And Applications in Medical Imaging
    • U-Net: Image Segmentation with CNNs
    • Intersection over Union (IoU) Metric
    • Finding the Nuclei in Divergent Images
    • Object Detection Introduction - Sliding Windows with HOGs
    • R-CNN, Fast R-CNN, FasterR-CNN and Mask R-CNN
    • Single Shot Detectors (SSDs)
    • YOLO to YOLOv3
    • TFOD API Install & Setup
    • Experiment with a ResNet SSD on images, webcam and videos
    • How to train a TFOD model
    • Setting up & Install YOLO DarNet & DarkFlow
    • Experiment with YOLO on still images,webcam and videos
    • Build our own YOLO Object Detector - Detecting London Undergorund Signs
    • DeepDream - How AI Generated Art All Started
    • Neural Style Transfer
    • GAN Overview
    • Mathematics of GAN
    • Mathematics of GAN
    • Face Aging GAN


    • Basic Face Recognition using LittleVGG CNN
    • Face Matching with VGGFace
    • Face Recognition using WebCAM & Identifying Friends TV Show Characters in Video
    • CV Introduction
    • Alternative Frameworks : PyTorch ,MXNet, Caffe,Theano & OpenVNO
    • Popular APIs Google,Microsoft ,ClarifAI, Amazon Rekognition & others
    • Popular Computer Vision Conferences & Finding Datasets
    • Building Deep Learning Machines & Cloud GPUs
    • Training our model
    • Extracting A Credit Card from the Background
    • Use Model to identify the Digits & Display it onto our Credit Card
    • Why use Cloud GPUs & How to Setup a PaperSpace Gradient Notebook
    • Train a AlexNet on PaperSpace
    • Install & Run Flask
    • Running our Computer Vision Web App on Flask Locally
    • Running our Computer Vision API
    • Setting up AWS EC2 instances & Installing KERAS, Tensorflow , OpenCV & Flask
    • Changing our EC2 Security Group
    • Running our CV WebApp on EC2
    • Running our CV API on EC2


Course Description

    Deep Learning training will build your expertise in real time scenarios related to analytics such as Prediction Analysis, Linear Regression, Image Processing, Audio & Video Analytics, Text Data Processing, IOT and various Machine Learning & Deep Learning Algorithms. 

    After the completion of Course, you will be able to:

    • Develop and Implement various Deep Learning Algorithms in daily practices & Live Environment.
    • Build Real-time Deep Learning Applications
    • Implement Data Analytics models (CNN & RNN) on various Data Sets
    • Data Mining across various file formats using Deep Learning models
    • Building Image & Video Classifiers, Speech Analytics using Deep Learning models
    • Perform various type of Analysis (Time Series, Image, Video, Audio, Face Detection & Recognition)
    • Implement plotting & graphs using various Deep Learning Libraries (Tensor Flow & Keras)
    • Perform Big Data Analytics using DeepLearning4j & other frameworks
    • Building different Neural networks using TensorFlow, Keras, PyTorch & other Deep Learning  Libraries.

    Gyansetu Data Science program is delivered by faculty having a strong educational M.Tech (CS) from IIT-Hyderabad, B.Tech (CS) from NIT-Surat (Gold Medalist) & currently working with world's top IT company Microsoft.

    We at Gyansetu understand that teaching any course is not difficult but to make someone job ready is the essential task. That's why we have prepared capstone projects which will drive your learning through real time industry scenarios and help you clearing interviews.

    All the advanced level topics will be covered at Gyansetu in a classroom/online Instructor led mode with recordings.

    Python programming is required to start Deep Learning course.

    Gyansetu is providing complimentary placement service to all students. Gyansetu Placement Team consistently work on industry collaboration and associations which help our students to find their dream job right after the completion of training.

    • Our placement team will add Big Data skills & projects in your CV and update your profile on Job search engines like Naukri, Indeed, Monster, etc. This will increase your profile visibility in top recruiter search and ultimately increase interview calls by 5x.
    • Our faculty offers extended support to students by clearing doubts faced during the interview and preparing them for the upcoming interviews.
    • Gyansetu’s Students are currently working in Companies like Sapient, Capgemini, TCS, Sopra, HCL, Birlasoft, Wipro, Accenture, Zomato, Ola Cabs, Oyo Rooms, etc.


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    • Gyansetu trainer’s are well known in Industry; who are highly qualified and currently working in top MNCs.
    • We provide interaction with faculty before the course starts.
    • Our experts help students in learning Technology from basics, even if you are not good in basic programming skills, don’t worry! We will help you.
    • Faculties will help you in preparing project reports & presentations.
    • Students will be provided Mentoring sessions by Experts.

Certification

Deep Learning / AI Certification

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Deep Learning and Artificial Intelligence Training Course in Gurgaon Features

FAQs

    We have seen getting a relevant interview call is not a big challenge in your case. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. We help you prepare your CV by adding relevant projects and skills once 80% of the course is completed. Our placement team will update your profile on Job Portals, this increases relevant interview calls by 5x.

    Interview selection depends on your knowledge and learning. As per the past trend, initial 5 interviews is a learning experience of

    • What type of technical questions are asked in interviews?
    • What are their expectations?
    • How should you prepare?


    Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

    We have seen getting a technical interview call is a challenge at times. Most of the time you receive sales job calls/ backend job calls/ BPO job calls. No Worries!! Our Placement team will prepare your CV in such a way that you will have a good number of technical interview calls. We will provide you interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. Our placement team will update your profile on Job Portals, this increases relevant interview call by 3x

    Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews is a learning experience of

    • What type of technical questions are asked in interviews?
    • What are their expectations?
    • How should you prepare?


    Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

    We have seen getting a technical interview call is hardly possible. Gyansetu provides internship opportunities to the non-working students so they have some industry exposure before they appear in interviews. Internship experience adds a lot of value to your CV and our placement team will prepare your CV in such a way that you will have a good number of interview calls. We will provide you interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training and we will update your profile on Job Portals, this increases relevant interview call by 3x

    Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews is a learning experience of

    • What type of technical questions are asked in interviews?
    • What are their expectations?
    • How should you prepare?


    Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

    Yes, a one-to-one faculty discussion and demo session will be provided before admission. We understand the importance of trust between you and the trainer. We will be happy if you clear all your queries before you start classes with us.


    We understand the importance of every session. Sessions recording will be shared with you and in case of any query, faculty will give you extra time to answer your queries.

    Yes, we understand that self-learning is most crucial and for the same we provide students with PPTs, PDFs, class recordings, lab sessions, etc, so that a student can get a good handle of these topics.

    We provide an option to retake the course within 3 months from the completion of your course, so that you get more time to learn the concepts and do the best in your interviews.

    We believe in the concept that having less students is the best way to pay attention to each student individually and for the same our batch size varies between 5-10 people.

    Yes, we have batches available on weekends. We understand many students are in jobs and it's difficult to take time for training on weekdays. Batch timings need to be checked with our counsellors.

    Yes, we have batches available on weekdays but in limited time slots. Since most of our trainers are working, so either the batches are available in morning hours or in the evening hours. You need to contact our counsellors to know more on this.

    Total duration of the course is 100 hours (50 hours of live instructor-led-training and 50 hours of self-paced learning).

    You don’t need to pay anyone for software installation, our faculties will provide you all the required softwares and will assist you in the complete installation process.

    Our faculties will help you in resolving your queries during and after the course.

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