Course Overview

Advanced Deep Learning and Computer Vision

Training Options

online Bootcamp

$ 799

  • 90 days of flexible access to online classes
  • num_of_days days of access to high-quality, self-paced learning content designed by industry experts
  • Classes starting from:-
24th Apr: Weekend Class
18th Jun: Weekday Class
Show all classes

Course Curriculum

Course Content

  • Section 01 - Self-paced Learning Curriculum

    Preview
    • Lesson 01 - DL Overview and Denoising Images with Autoencoders

      42:02Preview
      • 1 The Course Overview
        03:49
      • 2 A High-Level Overview of Deep Learning
        08:38
      • 3 Installing Keras and TensorFlow
        06:20
      • 4 Building a CNN Based Autoencoder to Denoise Images
        22:07
      • 5 Summary
        01:08
    • Lesson 02 - 2 Image Classification with Keras

      19:41Preview
      • 1 An Introduction to ImageNet Dataset and VGG Model
        06:13
      • 2 Using a Pre-Trained VGG Model
        11:32
      • 3 Summary and What’s Next
        01:56
    • Lesson 03 - Construct a GAN with Keras

      24:54Preview
      • 1 Introduction to GANs
        03:45
      • 2 Building GANs to Learn MNIST Dataset
        19:47
      • 3 Summary and What’s Next
        01:22
    • Lesson 04 - Object Detection with YOLO

      25:19Preview
      • 1 An Introduction to Object Detection and YOLO
        06:01
      • 2 Installing and Setting Up Keras Implementation of YOLO
        09:36
      • 3 Using a Pre-Trained YOLO Model for Object Detection
        06:47
      • 4 Summary and What’s Next
        02:55
    • Lesson 05 - Generating Images with Neural Style

      11:55
      • 1 An Introduction to Neural Style Transfer
        05:51
      • 2 Using Keras Implementation of Neural Style Transfer
        04:58
      • 3 Summary
        01:06
  • Section 02 - Live Class Curriculum

    Preview
    • Lesson 01 - Course Introduction

      • Course Objective
      • Course Outline
      • Course Components
      • Course Prerequisites
      • Overview
      • Project Highlight
      • Course Outcome
    • Lesson 02 - Prerequisites for the Course

      • Basic of Generative Models
      • KL Divergence
      • Image processing with OpenCv
    • Lesson 03 - RBM and DBNs

      • Collaborative Filtering
      • Boltzmann Machines
      • RBMs as DBN
      • RBM on MNIST handwritten dataset
      • Convolutional Boltzman Machines
      • Movie Recomendation system using RBM
    • Lesson 04 - Variational AutoEncoder

      • Stacked Autoencoder
      • Denoising Autoencoder
      • Sparse Autoencoder
      • Variational Autoencoder
      • Vector Quantized Variational AutoEncode
      • Temporal Difference VAE
      • Variational Autoencoder with Tensorflow
      • Variational Autoencoder with Keras
      • Build a variational autoencoder model to regenerate images of MNIST.
    • Lesson 05 - Working with Deep Generative Models

      • Introduction to Generative Adversarial Networks
      • Generative vs. Discriminative Algorithms
      • Architectural Overview
      • Basic building block – generator
      • Basic building block – discriminator
      • Types of GANs
      • Introduction to Deep Convolutional GANs (DCGAN)
      • Generating images with DCGANs
      • DCGAN
      • Augmenting datasets with conditional GANs
      • CGAN
      • Introduction to Least Square GANs (LSGAN)
      • Introduction to Auxiliary Classifier GAN (ACGAN)
      • Introduction to infoGAN
      • Image Translation with GANs : pix2pix
      • Pix2Pix
      • Image Translation with GANs : CycleGANs
      • Cycle GAN
      • Age CGAN
      • Use Keras or TensorFlow to build a deep generative model that will translate drawings of shoes to designs.
    • Lesson 06 - Applications: Neural Style transfer and Object Detection

      • An Introduction to neural style Transfer
      • Concept of Neural Style Transfer
      • Neural Style Transfer with Tensorflow
      • Create a Photo Editing Feature Using PyTorch
      • Success and History
      • AlexNet
      • VGG Net
      • RestNet
      • Transfer Learning
      • Transfer learning method
      • Object Detection
      • Intersection Over Union
      • Yolo
      • Object Identification Using YoloV3
      • Object Detection With Pretrained YoloV3
    • Lesson 07 - Distributed & Parallel Computing for Deep Learning Models

      • Introduction to CUDA architecture
      • Training tensorflow models on GPUs with Keras
      • Parallel Training
      • Distributed vs Parallel Computing
      • Distributed computing in Tensorflow
      • Introduction to tf.distribute
      • Distributed training across multiple CPUs
      • Distributed Training
      • Distributed training across multiple GPUs
      • Federated Learning
      • Parallel computing in Tensorflow
      • Introduction to tf federated
      • Train a CNN model on AWS SageMaker that classifies the fashion-mnist dataset using distributed training.
    • Lesson 08 - Reinforcement Learning

      • Introduction to Reinforcement learning
      • What is Reinforcement learning and its types
      • Reinforcement learning framework
      • Elements of Reinforcement Learning and Approaches
      • Mathematical formulation of Reinforcement Learning
      • Solution Methods: Dynamic Programming
      • Solution Methods: Algorithms
      • OpenAI gym
    • Lesson 09 - Deploying Deep Learning Models and Beyond

      • Understanding model Persistence in Keras
      • Saving and Serializing Models in Keras
      • Saving Models
      • Restoring and loading saved models
      • Loading Models
      • Introduction to Tensorflow Serving
      • Creating Custom REST APIs for your models with Flask/Django
      • Tensorflow Serving Rest
      • Deploying deep learning models with Docker
      • Tensorflow Serving Docker
      • Deploying deep learning models on Kubernetes
      • Tensorflow Deployment Flask
      • Deploying deep learning models in Serverless Environments
      • Deploying Model to Sage Maker
      • Introducing Tensorflow Lite
      • Introducing ONNx
      • ONNx
      • Train and deploy a CNN model with TensorFlow on SageMaker to classify fashion articles.
      • Restricted Boltzmann Machines
      • Convolutional Boltzman Machines
      • RBMs as DBN

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