Artificial Intelligence

What is Artificial Intelligence?

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.

AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

  • Who can learn?

    You can be an Analyst, trainer, and entrepreneur, college student, engineer, an architect, and finance professional, doctors with an ambition to solve a problem through data, machines and cup of coffee. Artificial Intelligence (AI) course 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
  • What do I 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

Artificial Intelligence

What is Vision
Applications of Image & VideAnalytics
Challenges in the Space of Image & VideAnalytics
Image Filtering
Image Representation as a Matrix & a Function
Image Transformations & Operations
Point Operations
Reversing Contrast Point Operations
Contrast Stretching
Histogram Equalization
Local Operations
Average for Noise Reduction
Moving Average – Uniform & Non-Uniform Weights
2D Moving Average
Linear Filtering
Average Filtering
Gaussian Filter
Boundary Effects
Sharpening Filters
Separable Filters
Cross-correlation for Template Matching
Edge Detection Origin of edges
Derivatives & Edges
Derivatives with Convolution
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 Domain
Fourier Transform
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
Image sub-sampling
Image Aliasing & Wagon Wheel Effect
Shannon’s Sampling Theorem
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
Image Features Detection
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
Image Feature Descriptors
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
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
Feature Matching
Image mosaicking
Wide baseline stereo matching
Spatial verification
Fitting Problem
Least Square Line Fitting
Total Least Squares
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
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
Window-based Models for Category Recognition
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 Neural Networks (ANN)
Artificial Neuron
Integration Function
Activation Function
Leaky ReLU
AND gate, XOR gate using Perceptron
Change integration & Multi-Layered Perceptron
Error Surface
Back Propagation Algorithm
Loss function
Activation function
Learning rate (alpha)
Batch Size
Deep Learning Libraries
Deep Neural Network
Data Optimization Techniques
Real-world scenarios of Deep Learning
Gradient Descent (GD) Learning
Vanishing / Exploding Gradient
Slow Convergence
Batch GD, Stochastic GD, Mini-Batch Stochastic GD
Nesterov Momentum
Loss Functions
Negative Log-Likelihood
Learning Rate (Alpha) – How to choose
Adaptive Learning Rate Methods
Adam (Adaptive Moment Estimation)
Regularization Methods
Empirical Risk Minimization (ERM)
Early stopping
Weight Decay
Data Manipulation Methods
Data Transformation
Batch Normalization
Covariate Shift
Data Augmentation
Convolution Neural Network (CNN)
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
Pooling Layer
Fully Connected Layer
Weight Initialization – Xavier’s initialization
Semantic Segmentation
Fully Convolutional Networks
Classification + Localization
Object Detection using CNNs
Regional CNN
Siamese Networks
Recurrent Neural Networks (RNN)
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) & Auto-encoders
Long Short-Term Memory (LSTM)
Deep RNNs vs Deep LSTMs
LSTM detailed description
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


Course Features

  • Next Batch Saturdays
  • Duration 60 Hrs
  • Students 125
  • Certificate Yes
  • Price Call - 91485 67987

About Us

We are the leading State of the Art Skill enhancer in the field of professional's training. Our idea of enhancing skills is through detailed industry research, experiential training, consulting, collaborations, innovations, and importantly experiments.

We have a proud Partnership with Leading trainers in the Industry ExcelR Solutions.

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Rajarajeshwari Nagar, Bangalore-560098

+91 91485 67987


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