Training content for self learning Engineers. Ranging from image processing, computer vision, machine learning and deep learning.

1. FUTURE FIVE
Training Course Content

2. Contents
 Algorithm & Image processing
 Computer Vision
 Machine learning
 Deep learning
 PROFILES

3. Algorithms & Image processing
 Why Study Algorithms?
 Integer Multiplication
 Sort – Merge sort, quick
sort,
 Big omega and theta
 Space optimization
 Optimal substructure
 Reweighting and
Johnsons algorithms
 Polynomial, Vertex,
Vectors, Matrices
 Signals & Systems
 Fourier transform
 Color spaces
 Image enhancement
 Image recovery
 Image compression
 Sampling & Quantization
Day-1 Day-2 & 3
 DSP
 Filtering
 Segmentation
 Sparsity
 Localization
 Feature Engineering
 Clustering
 Exercises
Day-4 & 5

4. Computer Vision
 MATLAB, Toolkits (VTK,ITK), OpenCL,
OpenGL, OpenCV
 Mask operations
 Thresholding, Pyramid generation
 Implementation of median filter
 Contrast, Brightness, Fourier transform
 Smoothing, Sharpening, Eroding, dilating
 Morphological processes
 Edges, Contours, Features
 Hough lines, Shapes, Colors
 Histogram equalization
 Template Matching
 Watershed, Top hat filters
 Calibration of cameras, stitching
 Harris corner detection, perspective
 Flann method, RANSAC
 Homograph generation
 Trackers – Kalmanm KLT
 Practical Exercises
Day-1 & 2 Day-3 & 4

5. Machine learning
 Python
 Loops, statements, data
visualizations
 Training and Testing of data
– linear regression
 Evaluating over-drift
 Analyzing and visualizing
features
 Inspecting model and
predicting
 Linear classifier
 Similarity assessment
 Precision vs Recall curve
 Supervised classifiers
 Unsupervised classifiers
 Curve fitting
 Nearest neighborhood
 Case studies
 Comparing approaches
Day-1 Day-2 & 3
 Stochastic Gradients
 Map reduce, Bias-variance,
clustering, retrieval
 Kernel Regression
 Decision trees, booting,
Classification, Over-fitting
 Weight manipulation,
redistribution
 Exercises
Day-4 & 5

6. Deep learning
 Neural networks, Fuzzy logics
 Gradient descent
 Computation graph and derivatives
 Vectorization
 Activation functions
 Forward and Back propogation
 ReLU, Random initialization, Tangent
 Parameters vs hyper parameters
 Regularization and optimization
 Multitask learning
 End to end learning
 Tensor flow, Caffe, Torch
 CNN based exercises
 CNN – architectures
 Semantic analysis
 Parallel computation
 How to .?
 Feedback.
Day-1 & 2 Day-3 & 4

7. Individual competencies
PROFILES

8. Gopinath C – Architect (10+ years)
Area of Expertise
Business analysis
Machine learning
Embedded C programming
Project management
Image processing, Computer vision
Responsibilities
Develop project proposals, project plan, effort and schedule
Brainstorm solution to problem statements, come up with
approach and action items
Build proof of concept followed by production ready solutions
Test, benchmark industry standards and advance the
maturity of the solution (improve performance numbers)
Review and adhere to all quality standards
Applications worked
Forward collision warning, Pedestrian detection, Traffic sign detection
Oxygenator modeling
Feature engineering, Calibrations and sensor fusions (Laser and Camera)
Algorithm development for braking assistance (stop lamp based)
Awards and Accomplishments
Lead the team in R&D department and Involved in Patenting of Innovative
Ideas
Awarded “Outstanding Performer”, “Beyond Excellence”
Onsite coordinator for design justification and negotiation
Awarded for Good practices and white paper submission on Autonomous
Vehicle (algorithms)

9. THANK YOU
Future Five