Motivation:
Artificial Intelligence is to simulate, as much as possible, human kind of intelligence in machines so that they become capable of assisting us in best possible way. Though Artificial Intelligence has become most popular technological term in last decade but the journey is at least as old as the development of first computing machine.

It is completely inspired by perception by human senses, interpretation by the human brain and control actions by human organs. Mathematical and programmatical models have been developed to make machines learn like human and take decisions much beyond the things presented to them during development.

Now machines are able to defeat world greatest chess champions, recognising complex human languages and scenes, take automatic decisions in difficult manufacturing process, predict business situations etc.

Common Myths/Facts/Observations about AI/ML/DS :

• AI-ML is very complex knowledge in computational science.
  Fact:It is quite easy and anyone with basic programming knowledge can grasp easily.
• Only python is AI language and unless you learn it you are not eligible to pursue AI.
  Fact: A few decades ago the same myth was with "Prolog" and "Lisp". No doubt about availability of super rich libraries in Python; But you can realize AI with any language like C/C++/Java also.
• You need to master mathematical skills like algebra and calculus before you grasp it.
  Fact: A basic high school mathematical understanding is sufficient.
• We can solve every problem by just training and validation data sets using Deep Learning.
  Fact: One need to understand a domain/problem very well and then only he will be able to use true potential of applied AI-ML. Training and Validation is just one of common steps. You will have to do much more than these for a good solution.
• A lot of time of all aspirant learners is consumed in teaching just programming language syntax. Moreover, in the name of AI majority is learning just API calls.
  Fact: Library API knowledge is necessary (to use) but not sufficient. If one does not understand the underlying concept, he/she will do random experiment most of the time. Just studying API syntax will never make one confident about solving a particular problem.
• we have seen people struggling with understanding or defending the differences/similarities among Data-Science, Machine Learning, Artificial Intelligence, Deep Learning, Data-Mining etc.
  Fact: This is most common question which create unnecessary confusion among learners. We recommend, not to argue with "unnecessarily crafted definitions". Artificial Intelligence, Machine Learning, Data Science, Data Mining are practically interchangeable terms. Deep Learning is a new name given to Neural Networks (mostly more than 3 layers) which is one of computational methods used in AI. Here is an [article] providing our open view regarding the same.
• We need a big infrastructure or very powerful machine to do any AI-ML related experiments.
  Fact: Almost all small or medium size data problems can be solved with just ordinary hardware infrastructure with minimal cost. One just need good efficient programming skills with judicious use of appropriate data structure and algorithms. Unjustified increased cost of cloud/in-house hardware is an indication of inefficient programming skills.

Our Motto:
Simplified Artificial Intelligence: AI is a concept, and even a high school going student can learn it. Any Engineer, Scientist, Manager or Business-person can understand and use it for his/her best benefits. Only thing needed is to get the knowledge in a simple way with real-life examples.

• Simplification of technical concepts is our innate strength.
• We teach core detailed concepts and industrial applicability. We do not consume learner's time in teaching Programming Languages' syntax or whole detailed Algebra.
• We have written almost every single AI/ML algorithm [Earlier in C/C++/Java and now in Python] even without using any ready-made library. Thus, we possess in depth knowledge of all components of AI/ML.
• We have super rich diversified industry experience spanning various domains with continuous use of AI for more than 22 years. We have invented many granted patents and international IEEE publications in the field of Artificial Intelligence.
• Our Educational Programs have been designed to make the content very effective, short, interesting and easy with so many examples.
• We are from tier-1 educational institute (Computer Science from IIT Bombay) – depicting the core capabilities.
• We provide free post program support for any kind of queries, doubts.
• Benefit of networking with other people of similar interest.
• All programs are classroom program conducted in good hotels with excellent infrastructure along with Lunch, Tea-Coffee and Snacks.

While serving corporates with our AI/ML education programs, we came across very high demand to conduct short duration programs providing comprehensive and clear understanding of some particular topic[s] around AI/ML. Hence, we designed a few programs, focussed around each topic with detailed information. These programs give kick start to Engineers/Scientists and help them in solving their business/technical problems and make them feeling confident about effective realization of AI/ML in their respective fields.

Program Outline

AI-ML Concept	A brief introduction on the evolution of AI/ML
	Applications of AI/ML and its impact
	General methodology - from real life
	Practical Examples of Applied AI
Understanding Data	A brief introduction on the evolution of AI/ML
	Handling Various Types of Data
	Meaning of Dimensions/Features
	Feature Importance
	Distances - Similarities - Euclidean, Manhattan
Data Clustering	Unsupervised Learning
	Data Clustering - Applications
	Different types of Clustering
	K-Means Clustering
	Density Based Clustering
Data Classification Concepts	Supervised Learning
	Training/Validation Data Preparation
	Accuracy Measurement - Precision, Recall, F-Measure
	Ensembling of Models
	Weighted Voting Mechanism
Classification Algorithms Nearest Neighbours	Nearest Neighbour,
	K-Nearest Neighbour, Weighted KNN
	Analysis K-Nearest Neighbour
Classification Algorithms Decision Tree	Sequence of Decisions
	Entropy Measurement, Information Gain
	ID3 Algorithm - Decision Tree Classifier
	Analyzing and Improving Decision Tree
	Random Forests
Bayesian Classification	Bayes Theorem
	Continuous and Discrete Probabilities
	Bayesian Text Classification
	Binomial & Multinomial Computation
	Handling Numeric and Text Data
	Analyzing Bayesian Classification
Support Vector Machine	Maximum Margin Computation
	Support Vector Machine Algorithm
	Kernels and Non-Linearity
Neural Networks	Neural Networks Introduction
	Power of Perceptron
	Gradient Descent
	Multilayer Perceptron - Neural Networks
	Error Backpropagation Algorithm
	Important Parameters
	Convolutional Neural Networks
	Transfer Learning
	Complete Working Examples
	Recurrent Neural Networks
Natural Language Processing	NLP - Introduction
	Grammar Rules (English) - Predicates, Clauses
	Tokenizing & Stemming
	Importance of Punctuations
	Handling Acronyms
	Part of Speech
	Sentence/List Boundary
	Word References
	Prepositional Phrase
	Nouns and Qualifiers
	Using Wordnet
	Working Examples
Image Processing & Computer Vision	Introduction
	Image Data Understanding
	Image Data Conversion
	Filtering Techniques
	Transformation
	Histogram Equalization
	Edge Detection
	Morphological Operations
	Image Compression
	Video Processing
	Image Clustering
	Image Search and Match
	Object Detecting - Localization
	Object Tracking in Motion
	Use of CNN for Image Recognition
	Example: Autonomous Driving (ADAS)
Information Retrieval Search Engines	Data Collection & Management
	Focussed Crawlers
	Understanding Indexes
	Query Parsing
	Spell Correction
	Fuzzy Text Search
	Boolean & Range Search
	Ranking Results
	Local & Global Context
	Trends & Aging
	Personalization
Recommendation Engines	Problem Formulations
	Collaborative Filtering
	Singular Value Decomposition
	Netflix Example
	Associative Rule Mining
	Simple Strategy
	Multiple Sources of Data
	Incremental Learning
	Temporal Trend Analysis
	Relevance and Personalization
Time Series Forecasting	Understanding Problem & Data
	Accuracy Evaluation
	Data Cleaning and Smoothing
	Single vs Multiple Dimensional Time Series
	Trends & Seasonalities
	Correlation and Causality
	Moving Average Modelling
	Auto Regressive Modelling
	Neural Networks Modelling
Problem Solving Data Structure, Algorithms & Complexity (A very special program needed everywhere)	Problems with Very Large Data
	Huge Cost of Computations
	Efficient - Insertion, Deletion, Update, Access
	Dividing Problems for Distributed Computing
	Saving Memory and CPU Cycles
	Complexity of Algorithms
	Comparing Different Data Structure
	Detail Study and Analysis Set, Linked-List, Array, Map, Hash, Trees, Graphs
	Simplified Implementation
	Call by Value and Call by Reference
	Iteration vs Recursion
	Search Algorithms
	Sorting Algorithms
	Tree and Graph Implementation
	Heuristics

Neural Networks have been in existence and extensively used in almost all major intelligent applications across the world since approx 1960. But they have gain a very huge popularity in last few years, majorly due to:

• Hardware Development for Parallel Computation
• Open Source Libraries for Quick Experimentations
• Simple Infrastructure for Distributed Computing
• Huge Availability of Knowledge Resources on Internet

Although there are plenty of resources about Deep Learning (Neural Networks) on Internet, still it is very difficult to find ones which are very easy to understand, and capable of making one confident to the level of conveying the same to others. Moreover, once one gets stuck at some point, it is indeed very difficult to find a clear and concise answer.

Keeping that in mind, we have designed a short, intense but very interesting classroom program "Neural Networks (Deep Learning) - Made Super Easy". The core concept is explained with very simple real-world practical examples.


Neural Networks (Deep Learning) - Made Super Easy
Brief Content

Fundamentals of AI and its Facets

Data Preparation for Various Domains

From Single Perceptron to Multi-layer Neural Networks

Application in Business Problems

Efficient Computation for high ROI

Classic and Modern Concepts (Deep Learning)

Convolutional Neural Networks & Transfer Learning

Expected Outcome

• You will have immense interest in Neural Networks' application.
• Extremely clear fundamental knowledge about Neural Networks.
• Ability to evaluate judiciously the problems and use-cases for applicability.
• Confidence of efficient implementation and use.
• Ability to bridge between business and technological realization.

Remark:

• Free: Post Program Email Support for 15 Days.
• Participation Certificate to All.

Program Will Be Conducted By

Ram Dayal Goyal
Founder & CEO - RNS Labs Pvt Ltd

• An IIT Bombay Computer Science alumni.
• 22+ years of AI-ML industry experience
• Holding multiple USPTO granted patents in applied AI
• Multiple international IEEE publications in AI domain.
• Keynote and invited speaker in many technical/academic forums
• R&D Head and Consultant for Autonomous Driving, Location Intelligence etc.




• Earlier Coducted AI Programs at
  • Schneider Electric - Bengaluru
  • Govt. College of Engineering - Pune
  • Hella Automotive - Pune
  • Kristu Jayanti College - Bengaluru
  • Wenger & Watson - Bengaluru
  • Ketera (Deem) - Bengaluru, San Francisco (USA)
  • Rokitt Inc. - New York (USA)
  • Stellapps Tech. - Bengaluru
  • Hindustan Unilever - Bengaluru
  • and many more....

General Day Schedule

9:30 - 10:00	Registration Process
10:00 - 10:30	Keynote and Opening Remarks
10:30 - 11:30	Session 1
11:30 - 12:00	Networking - Tea Break
12:00 - 13:00	Session 2
13:00 - 14:00	Networking - Lunch Break
14:00 - 15:00	Session 3
15:00 - 16:00	Session 4
16:00 - 16:30	Networking - High Tea Break
16:30 - 17:30	Session 5
17:30 - 18:00	Closing Remark

Upcoming events will be published here

Please call us at +91-911 080 0039
or
Send an Email to info@RNSLabs.com