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Course Description

Deep Learning & Artificial Intelligence (AI) Training

Artificial Intelligence (AI) is the big thing in the technology field and a large number of organizations are implementing AI and the demand for professionals in AI is growing at an amazing speed. Artificial Intelligence (AI) course with ExcelR will provide a wide understanding of the concepts of Artificial Intelligence (AI) to make computer programs to solve problems and achieve goals in the world.

What is Artificial Intelligence (AI) ?

Artificial Intelligence (AI) makes computers to perform tasks such as speech recognition, decision-making and visual perception which normally requires human intelligence that aims to develop intelligent machines.

The basic grounding in the ExcelR’s practices in AI is likely to become valuable in the field of business, and profession. This course is intended to cover the concepts of Artificial Intelligence (AI) from the basics to advanced implementation.

What are the course objectives?

Artificial Intelligence (AI) is becoming smarter day by day in all business functions to elevate performances. AI is used widely in gaming, media, finance, robotics, quantum science, autonomous vehicles, and medical diagnosis. AI technology is a crucial prerequisite of much of the digital transformation taking place today as organizations position themselves to capitalize on the ever-growing amount of data being generated and collected.

To build a successful career in Artificial Intelligence, this course is intended to give a complete understanding of Artificial Intelligence concepts. This course offers you get practical, hands-on experience to ensure hassle-free execution of real-life projects. This AI course leverages world-class industry expertise in making you professional data science experts.

ExcelR familiarises you with the basic terminologies, problem-solving, and learning methods of AI and also discuss the impact of AI

What skills will you learn?

In this Artificial Intelligence (AI) course, you will be able to

  • Understand the basics of AI and how these technologies are re-defining the AI industry
  • Learn the key terminology used in AI space
  • Learn major applications of AI thru use cases

Who should take this course?/h2>

ExcelR’s course on Artificial Intelligence (AI) gives you the basic knowledge of Artificial Intelligence. This course doesn’t need any programming skills and best suited for:

  • Well-suited for management and non-technical participants
  • Students who want to learn Artificial Intelligence

Newbies who are not familiar with AI or its implications

Things You Will Learn

  • What is Vision
  • Applications of Image & Video Analytics
  • Challenges in the Space of Image & Video Analytics
  • Image Representation as a Matrix & a Function
  • Image Transformations & Operations
  • Point Operations
    • Reversing ContrastPoint Operations
    • Contrast Stretching 
    • Histogram Equalization
    • Average
  • Local Operations 
    • Average for Noise Reduction
    • Moving Average – Uniform & Non-Uniform Weights
    • 2D Moving Average
  • Linear Filtering 
    • Cross-Correlation
    • Average Filtering 
    • Gaussian Filter
    • Convolution
    • Boundary Effects
    • Sharpening Filters
    • Separable Filters
  • Cross-correlation for Template Matching
  • Derivatives & Edges
    • Derivatives with Convolutio
    • Partial Derivative of an Image
    • Sobel Edge Detection Filter
    • Finite Difference Filters
    • Image Gradient
    • Effects of Noise
  • Convolution – Differentiation Property
  • Derivative of Gaussian filters
    • 1D & 2D Gaussian
    • Second Derivative
  • Laplacian Filter
    • Smoothening with Gaussian
    • Laplacian of Gaussian ( LoG)
    • LoG filter
    • Reducing noise using Gaussian Filter
  • Non-Linear Filters
    • Bilateral Filters
    • Optimal Edge Detection
    • Canny Edge Detector
    • Non-Maximum Suppression 
    • Hysteresis Thresholding
  • Frequency Spectra
    • Magnitude vs Phase
    • Rotation & Edge effects 
    • Fourier Filtering 
    • High Pass Filtering 
    • Low-pass Filtering 
    • Band-pass Filtering 
  • Filtering in Frequency domain
  • Fourier Amplitude & Phase Spectrum
    • fftshift(x)
  • Image Aliasing & Wagon Wheel Effect
    • Shannon’s Sampling Theorem
    • Downsampling
  • Gaussian Pre-Filtering
    • Image Pyramid
    •  Gaussian Pyramid
    • Image Upsampling
    • Image Interpolation
  • Nearest Neighbour Interpolation
    •  Linear & Bilinear Interpolation
    • Reconstruction Filters
    • Cubic & Cubic Spline Interpolation
  • Interpolation Filters
    • Interpolation & Decimation
    • Image Rotation
    • Multiresolution Representations 
  • Laplacian Pyramid & Image Blending
  • Why extract Image features
    • Local features 
    • Detection, Description & Matching 
  • Interest Operator Repeatability
  • Descriptor Distinctiveness
    • Invariant local Features
    • Local features Detection – Local measure of Uniqueness
  • A simple matching criteria 
    • SSD error
    • SSD weighted
    • Selecting, Interest Point & Overview of Eigenvector & Eigenvalues
  • Harris Corner Detector
    • Image Transformations
    • Scale Invariant Detection
    • Automatic Scale Selection
    • Blob Detection in 2D & Characteristic Scale
  • Scale-Invariant Interest Points & Fast Approximation
    • Signature Function
  • The ideal feature descriptor
    • How to achieve Invariance?
    • Raw Pixels as local Descriptors
  • Scale Invariant Feature Transform – SIFT
    • SIFT – Scale-Space Extrema Detection
    • SIFT – Choosing Parameters
    • SIFT – Keypoint Localization
    • SIFT – Orientation Assignment
    • SIFT – Feature Descriptor
    • SIFT – Partial Voting
  • PCA-SIFT
    • Gradient Location-Orientation histogram (GLOH)
    • SIFT (Scale Invariant Feature Transform) vs SURF (Speeded Up Robust Features)
    • HOG (Histogram of Oriented Gradients)
    • LBP (Local Binary Patterns)
    • Filter Banks
    • Indexing Local Features: Inverted file Index
    • Visual words
    • Visual vocabulary 
    • Bag of visual words
    • Constructing the tree
    • Parsing the tree
  • Image mosaicking
    • Wide baseline stereo matching
    • Spatial verification
    • Least Square Line Fitting 
  • Random Sample Consensus(RANSAC), Choosing parameters
    • Hough Voting 
    • Hough Transform 
    • Hough Space 
    • Hough Voting – Illustration, Several Lines
    • Dealing with Noise
    • Hough Transform for Circles
    • Generalized Hough Transform
  • RANSAC: Going from line-fitting to image mosaicing
  • Image Transformation
    • Translation
    • Rotation
    • Scaling
  • How many parameters in the model?
  • Geometric Transformations
  • Matching / Alignment as Fitting
  • Affine Transformations
  • Feature-based Alignment
    • Dealing with Outliers
    • Matching Local Features
    • How to measure performance – ROC curve
  • General Recognition Framework 
    • Window-based models 
    • Part-based models
  • Window-based model
    • Generating & Scoring Candidates
    • Sliding Windows Methods
    • Global Representation
    • Representation Texture – Material, Orientation, Scale
    • Filter Banks
    • Gabor Transform, Gabor Basics
  • Classifier: Nearest Neighbour for Scene Gist Detection
  • Classifier: SVM for person detection
  • Classifier: Boosting for Face Detection – Viola-Jones Face Detector – Adaboost

Neural Network

  • Artificial Neuron
  • Integration Function
  • Activation Function 
    • Step
    • Ramp
    • Sigmoid
    • Tanh
    • ELU
    • ReLU
    • Leaky ReLU
    • Maxout
    • Softmax
  • AND gate, XOR gate using Perceptron
  • Perceptron
    • Change integration & Multi-Layered Perceptron
    • Error Surface
    • Back Propagation Algorithm 
    • Loss function
    • Activation function 
    •  Iteration
    • Epoch
    • Learning rate (alpha)
    • Batch Size 
  • Deep Learning Libraries 
    • caffe
    • Torch
    • Tensorflow
  • Deep Neural Network
  • Data Optimization Techniques
  • Gradient Descent (GD) Learning 
    • Vanishing / Exploding Gradient
    • Slow Convergence
    • Batch GD, Stochastic GD, Mini-Batch Stochastic GD
  • Momentum
    • Nesterov Momentum
    • Loss Functions
    • Cross-Entropy
    • Negative Log-Likelihood
  • Learning Rate (Alpha) – How to choose
  • Adaptive Learning Rate Methods 
    • Adagrad
    • RMSProp
    • Adam (Adaptive Moment Estimation)
  • Regularization Methods 
    • Empirical Risk Minimization (ERM)
  • Overfitting
    • Early stopping
    • Weight Decay
    • Dropout
    • Dropconnect
  • Noise
    • Data
    • Label 
    • Gradient
  • Data Manipulation Methods
    • Data Transformation
    • Batch Normalization
    • Covariate Shift
    • Data Augmentation
  • Convolution Neural Network – CNN
  • ImageNet Classification Challenge
    • Hierarchical Approach
    • Local Connectivity
    • Parameter Sharing
  • Normalization Layer
    • Last Layer Customization
    • Loss Functions
    • Transfer Learning
  • Convolution of an image with a filter
  • Convolution Layer – Basic ConvNet
  • ReLU (Rectified Linear Units) Layer
    • Stride
    • Pad
    • Pooling Layer
    • Fully Connected Layer
  • Weight Initialization – Xavier’s initialization
    • Semantic Segmentation
    • Fully Convolutional Networks
    • Classification + Localization
    • Object Detection using CNNs
  • Regional CNN
  • Fast RCNN
  • Siamese Networks
  • Recurrent Neural Networks for NLP
  • Traditional Language Models
  • Original Neural Language Model using MLPs
  • Recurrent Neural Networks
    • Back propagation through time (BPTT)
    • Recurrent Neural Networks loss computation
  • Image Captioning
  • Bidirectional RNNs
  • Deep Bidirectional RNNs
  • Memory based Models
  • Long Short-Term Memory (LSTM)
  • RNN vs LSTM
    • Deep RNNs vs Deep LSTMs
  • LSTM detailed description
  • Auto-encoders
    • Encoder part of auto-encoder
    • Decoder part of auto-encoder
    • Denoising Autoencoders (dA)
    • Stacking auto-encoders 
  • MxNet, TensorFlow, Keras libraries to solve the use cases

Contact Our Team of Experts

Why ExcelR?

Testimonials

FAQs

  • The all new and exclusive JUMBO PASS is the latest initiative taken by ExcelR to offer you access to attend unlimited batches over the duration of 365 days. You will be able to attend unlimited number of classes for the course of your choice.
  • It is the science of developing intelligent computer programs which can understand human intelligence.Automating the tasks by applying predictive modelling and machine learning algorithms is called as Artificial Intelligence(AI).
  • Intelligence is the computational part of the ability to achieve goals in the world.
  • Becoming AI expert is the most logical move for people working on sata or Related work. One can choose to be a data modelle, ML expert,Data Scientist,etc., By learning AI & DL.
  • Basic knowledge of mathematics, programming concepts and a sense of curiosity and willingness to learn AI.

We offer this course in the below formats:

  • Live Virtual / Online Classroom
  • Online Self-Learning
  • Classroom Training
  • We arrange for recordings of each session you appear for your reference.
  • Yes, for our online training programs we do offer group discounts. For further details, please reach out us: [email protected]

Available payment options:  (to be filled)

  • Net Banking
  • Debit card
  • Cheque
  • Participants will learn majorly about Python and introduction to R programming in accomplising Artificial Neural Network Deep Learning algorithm. Python deep learning libraries including TensorFlow, Keras and Computer Vision, Image Processing Python library OpenCV will be explained practically while working on Deep Learning Neural Network architectures.
  • Participants will start by learning about Artificial Neural Network (ANN) and Multi-Layered Perceptron (MLP). This is followed by Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Units (GRU), Generative Adversarial Network (GAN), Autoencoders, Restricted Boltzmann Machine (RBM) and many other variants. For further details please go through the Course curriculum
  • Major portion of the training will focus on computer vision, image processing using OpenCV, which is de facto library for dealing with images. Also we will work on CNN, RNN-CNN variants to built prediction models for image and video problems
  • Instructor-led online training is an interactive mode of training where you and the trainer will log in at the same time and live sessions will be done virtually. These sessions will provide scope for active interaction between you and the trainer.
  • ExcelR offers a blended model of learning. In this model, you can attend classroom, instructor-led live online and e-learning (recorded sessions) with a single enrolment. A combination of these 3 will produce a synergistic impact on the learning. You can attend multiple Instructor-led live online sessions for one year from different trainers at no additional cost with the all new and exclusive JUMBO PASS.
  • It is a live instructor-led interactive session which is done at a specific time where you and the trainer will log in at the same time. The same session will be also recorded and access will be provided to revise, recap or watch any missed session.
  • Not a problem even if you miss a live Deep Learning and Artificial Intelligence session for some reason. Every session will be recorded and access will be given to all the videos on ExcelR’s state-of-the-art Learning Management System (LMS). You can watch the recorded Deep Learning and Artificial Intelligence sessions at your own pace and convenience.
  • Yes, after successfully completing the course you will be awarded a course completion certificate from ExcelR.
  • You can reach out to us by visiting our website and interact with our live chat support team. Our customer service representatives will assist you with all your queries. You can also send us an email at [email protected] with your query and our Subject Matter Experts / Sales Team will clarify your queries or call us on 1800-212-2120 (Toll-Free number – India), +1(281) 971-3065 (USA), 800 800 9706 (India), 203-514-6638 (United Kingdom), 128-520-3240 (Australia).
  • The different payment methods accepted by us are
    • Cash
    • Net Banking
    • Cheque
    • Debit Card
    • Credit Card
    • PayPal
    • Visa
    • Mastercard
    • American Express
    • Discover

Global Presence

ExcelR is a training and consulting firm with its global headquarters in Houston, Texas, USA. Alongside to catering to the tailored needs of students, professionals, corporates and educational institutions across multiple locations, ExcelR opened its offices in multiple strategic locations such as Australia, Malaysia for the ASEAN market, Canada, UK, Romania taking into account the Eastern Europe and South Africa. In addition to these offices, ExcelR believes in building and nurturing future entrepreneurs through its Franchise verticals and hence has awarded in excess of 30 franchises across the globe. This ensures that our quality education and related services reach out to all corners of the world. Furthermore, this resonates with our global strategy of catering to the needs of bridging the gap between the industry and academia globally.

ExcelR's Global Presence

Accolades

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