Intro

An effort to create a
state of art institution
for AI study and
research.

A disciplined and structured approach to learning and implementing the fundamentals of AIML.

About

TSAI provides a profound understanding of AI for Vision Comprehension

through bleeding edge concepts, and an amazing peer group to learn with.

Programs

Three unique and challenging semester-style programs

Through EVA and EIP, TSAI has trained more than 6500 students!

Details - EVA

Extensive Vision AI Program

Probably the most exhaustive and updated Deep Vision Program in the world! It is spread over three semester-style phases, each restricted by a qualifying exam.

Phase #1 - FUNDAMENTALS

Lecture Title
Background & Basics: Machine Learning Intuition
Python: Python 101 for Machine Learning
DNN Concepts: Convolutions, Pooling Operations & Channels
PyTorch: Pytorch 101 for Vision Machine Learning
First Neural Network: Kernels, Activations, and Layers
Architectural Basics: We go through 9 model iterations together, step-by-step to find the final architecture
Receptive Fields: The CORE fundamental concept behind EVA Program
BN, Kernels & Regularization: Mathematics behind Batch Normalization, Kernel Initialization, and Regularization
Backpropagation and Advance Convolutions: Depthwise, Pixel Shuffle, Dilated, Transpose and others
Advanced Image Augmentation Techniques: Albumentations, Richman's data augmentation and benchmarks
DNN Interpretability: Class Activation Maps, the most powerful debugging tool at your disposal
Advanced Training Concepts: Optimizers, LR Schedules, LR Finder & Loss Functions
SuperConvergence: Cyclic Learning Rates, One Cycle Policy, and Dawnbench
ResNets: Training ResNet for TinyImageNet from scratch
Inception: Understanding Inception and DenseNet Architectures
YoloV2: Understanding YOLOV2 Loss Function
YoloV5: Implementing Object Detection Training & Transfer Learning
RCNN Family: RCNN, Fast-RCNN, FasterRCNN & MaskRCNN
CapStone: Monocular Depth Estimation and Background/Foreground Extraction

Phase #2 - APPLICATIONS

Lecture Title
Deploying over AWS: Train, Dockerize and then deploy your model on AWS.
MobileNet & Other Edge DNNs: Training a DNN for EDGE Deployment from scratch. Understanding MobileNets and ShuffleNets
Face Recognition Part 1: Face Detection and Detection Strategies
OpenCV Refresher and Face Recognition Part 2: Implementing Object Tracking and Stabilization, OpenCV and DLIB, for face recognition and others
Human Pose Estimation: State of Art HPE and Human Localization
Super-Resolution/Neural-Style-Transfer: Leveraging Transfer learning for NST and "Dense" models
Segmentation and Usage in Medical Domain: U-NET, its relatives and usage in Medical Science
GANs: Generative Adversarial Network and Variants
Encoders: Auto Encoders and Variational AutoEncoders
Neural Work Embedding: The Embedding Layer
Sequence Models: RNNs and LSTMs
GRU, Attention Mechanism & Transformers: Attention is all you need!
Image Captioning: Image Captioning - Integrating CNN + LSTMs
Speech: Advanced Speech Processing (TTS/STT)
CapStone Project: Hallowed be thy name

Phase #3 - REAL ROBOTICS

Lecture Title
Reinforcement Learning Basics: Markov Decision Processes, Deterministic, and Stochastic Environments & Bellman Equation
Q-Learning: Q-Learning, Plan vs Policy Networks, and Environment Models
Deep Q-Learning & DeepTraffic: Custom Environments, OpenGym, Exploration vs Exploitation, and improvements to DQN
Deep Reinforcement Learning: Policy Gradients, Dynamic Programming, Policy Evaluations, and Temporal Difference Learning
Actor-Critic Models: Memory Structures, Gibbs Softmax, Eligibility Traces, and Polyak Averaging
A3C Models: A3C, A3C optimizations,, and implementation logic
Deep Deterministic Policy Gradients: DDPG Background, Off-Policy Networks, Continuous Action Spaces, and Replay Buffers
Twin Delayed DDPG Part 1: Clipped Double-Q Learning, Delayed Policy Updates, and Target Policy Smoothing
Twin Delayed DDPG Part 2: Full TD3 implementation to make a robot walk, and solve a custom environment
Autonomous Robotics Introduction: Introduction to ARI platform, and its control systems. Real Robot we mean!
Sensor Fusion for Localization: Sensor fusion, depth estimation, and stereo imaging for robotic localization
3D Environmental Reconstruction Part 1: Solving 3D mapping for static environment
3D Environmental Reconstruction Part 2: Solving 3D mapping for dynamic (moving) objects in the static environment
3D Environmental Reconstruction Part 3: Solving 3D mapping for dynamic objects in a dynamic environment
Advanced Path planning, and Navigation: A*, and other Path planning, and algorithms
EndGame: CapStone project to implement everything we learned
The later part of this course's topics are inspired from Udacity Nanodegree but only just the topics, not its contents. We would be implementing these on a real robot, without ROS, and using TD3, not DQN as in Udacity
Details - EPAi

Extensive Python & PyTorch for AI

An advanced Python course for fundamental understanding of Python Language and PyTorch. Designed for those who want to become software and application Architects.

This is not for beginners. Take this sample test to see what you'd be learning. Registration are open for Phase 1. Register now!

PHASE #1 - FUNCTIONAL PYTHON

Lecture Title
Basics: Python Type Hierarchy, Multi-line statements and strings, Variable Names, Conditionals, Functions, The While Loop, Break Continue and the Try Statement, The For Loop and Classes
Object Mutability and Interning: Variables and Memory References, Garbage Collection, Dynamic vs static Typing, Variable Re-assignment, Object Mutability, Variable Equailty, Everything is an Object and Python Interning
Numeric Types I: Integers, Constructors, Bases, Rational Numbers, Floats, rounding, Coercing to Integers and equality
Numeric Types II: Decimals, Decimal Operations, Decimal Performance, Complex Numbers, Booleans, Boolean Precedence and Comparison Operators
Functional Parameters: Argument vs Parameter, Positional and keyword Arguments, Unpacking Iterables, Extended Unpacking, __*args_, Keyword Arguments, __**kwags_, Args and Kwargs together, Parameter Defaults and Application
First Class Functions Part I: Lambda Expressions, Lambdas and Sorting, Functional Introspection, Callables, Map, Filter, Zip and List Comprehension
First Class Functions Part II: List Comprehension, Reducing functions, Partial Functions, Operator Module, Docstrings and Annotations.
Scopes and Closures: Global and Local Scopes, Nonlocal scopes, Closures, and Closure Applications
Decorators: Decorators and Decorator applications (timers, logger, stacked decorators, memoization, decorator class and dispatching)
Tuples and Named Tuples: Tuples, Tuples as data structures, named Tuples, DocStrings, and Application
Modules, Packages and Namespaces: Module, Python Imports, importlib, import variants, reloading modules, __main__, packages, structuring, and namespaces
fStrings, Timing Functions and Command Line Arguments: Dictionary Ordering, kwargs, tuples, fStrings, Timing Functions and Command Line Arguments
Sequence Types I: Sequence Types, Mutable Sequence Types, List vs Tuples, Index Base and Slice Bounds, Copying Sequence and Slicing
Sequence Types II and Advanced List Comprehension: Custom Sequences, In-place Concatenation and Repetition, Sorting Sequences, List Comprehensions + Small Project
Iterables and Iterators: Iterating Collections, Iterators, Iterables, Cyclic Iterators, in-built Iterators, iter() function and iterator applications
Generators and Iteration Tools: Yielding and Generator Functions, Generator Expressions, Yield From, Aggregators, Chaining and Teeing, Zipping and their applications
Context Managers: Context Managers, Lasy Iterators, Genrators and Context Managers, Nested Context Managers and their application
Coroutines and Data Pipelines: Coroutines, Generator States, Exceptions, Data Pipeline and application

Phase #2 - OOPS & PYTORCH

Lecture Title
Hash Maps and Dictionaries:Associative Arrays, Hash Maps, Hash Functions, Dictionary Views, Handling Dictionaries and Custom Classes
Sets and Serialized Dictionaries:Set Theory, Python Sets, Frozen Sets, and Set Applications, DefaultDict, OrderedDict, Counters and UserDict
Serialization and Deserialization:Picking, JSON Serialization, Encoding and Decoding JSON, and Applications
Classes Part I:Object and Classes, Attributes, Callables, Functional Attributes and Run-time attributes
Classes Part II + Dataclasses:Properties, Decorators, Read-Only Properties, Class and Static Methods, Scopes, Dataclasses and Application
Polymorphism and Special Methods:Polymorhpism, __str__ and __repr__ methods, rich comparisons, hashing and equality, callables, and applications
Single Inheritance:Single Inheritance, Object Class, Overriding, Extending, Delegation, Slots, and applications
Descriptors:Descriptors, Getters and Setters, Instance Propertiers, Strong and Weak References, __set_name__ method, Proprty Lookup Resolution and application
Enumerations and Exceptions:Enumerations, Aliases, Custom Enums, Python Exceptions, Handling and Raising Exceptions and creating custom exceptions
Pytorch Basics I :Matrices, Tensors, Variables, Numpy and PyTorch inter-operatibility, Rank, Axes and Shapes
PyTorch Basics II:Data and Dataloader, Forward Method, Training Loop and Training Pipeline
PyTorch Intermediate I + Pytorch Internals:PyTorch Classes, Containers, Layers and Activations. PyTorch Internals or how Pytorch uses Advanced Python internally
PyTorch Intermediate II:Distance and Basic Loss Functions, Utilities, Profiling Layers, MACs/FLOPs calculations and Memory Usage
PyTorch Advanced I:Convolution Algorithm Implementation, Autograd Mechanics and Dynamic Computation Graph
PyTorch Advanced II:Optimizers, Custom Dataloaders, Tensorboard Integration, Memory Management and Half Precision Training
PyTorch Advanced III:Advanced Loss Functions for GAN, Kullback Lieber, Embeddings, Focal, IoU, Perceptual, CTC, Triplet and DICE

Course Feedback

Feedback from Phase 1 students moving to Phase 2
Initially i thought it would be just like all other python course, i joined just thinking i might learn few things more.. but as session progressed i was like.. okay i dont know python.. amazing sessions and course content
A very indepth course and excellent concepts
I really liked the course content. I never learnt python in this much depth. Now I can say that I am a python developer 😊
Slightly hectic, when it comes to assignment submission.More days(atleast week time should be given for assignment submission)
ONE OF THE BEST COURSE AT AFFORDABLE PRICE
Has helped me know about intricate things on python
Course content is good and in depth which makes it easy to understand for anyone
Seriously EPAi one of the best Intermediate Python Courses that I have taken up. In-depth Content, Fun-To-Do Assignments, what else do I need?
Honestly i learned a lot from this course.. Course contants are really good and covered in depth which i really liked.
It is really awesome course !!! Good in-depth sessions.
For me course really helped a lot. Got to learn many new things.
Course is well structure but would request for more time for the assignment submissions. Like other courses a week should be good in my opinion.
Excellent stuff
Very nice course I learned a lot
The course content is very well structured and assignments are also top notch
Excellent course
This course is extremely unique. Not surprised as it's from TSAI. My overall experience with TSAI has been amazing ever since.
Hits the very core foundation of important concepts
One of the best courses I have ever attended
Good course content, too fast pace, challenging assignments
Gives in depth knowledge about the design and working behaviour of python
Advanced course in python which teaches CI/CD as an extra practice.
It's very good
Its great course for Advance Pythons for AI
good platform to explore so many new concepts
In-depth understanding of python for writing optimized, error-free and modular codes. Creating our own packages and module.
Course content is good and touching the deep roots of python
The course is very useful for me, I have not seen a course with this good content online.
its beautiful
Awesome, worth it
I think it's a very exhaustive course and is really going to be useful for my career.

Instructor Feedback

Feedback from Phase 1 students moving to Phase 2
ABOUT THE INSTRUCTOR
I joined because you were teaching
Take a bow
What can I say, he is the best person to teach this course
Too good
BEST I CAN SAY. ROHAN SHRAVAN IS MY NEW ROLE MODEL. I WISH HIM SUCCESS FOR HIS FUTURE PLANS.
PRO
Rohan is an amazing instructor and makes every attempt to clarify things
G.O.D
One of the best instructor and influencer i have seen in my life.
Fabulous. Please take some more courses like this - C++, Javascript.
When i think of Rohan, I see dedication, commitment and discipline. when i look back i see much change in me. Thank you very much Rohan.
Good as always
Instructor is awesome like always!
Class apart
good
Knowledgeable
Excellent
Knowledgeable
Awesome
Awesome
Good hardworking
Perfect
Good
Extremely knowledgeable and experienced. Honoured to have such a mentor
Knowledgeable, explains even the tiniest detail
Rohan is very very very knowledgeable. And more than that, he is an awesome and inspirational teacher. He knows the issues that the students might face and keep his content and hands on session based on these factors.
Man! No comments!
Perfect to describe in one word and easily one of the best teachers I have studied under in my life yet
expert in python
Good
One of the finest instructor from whom I have taken the course
He gives great insight into topic taken
Excellent & Has lot of passion & patience
He is well knowledge in the topics
Rohan has ability to deliver complex concepts in a nice and simple yet powerful way.
Rohan is awesome.
Rohan is exceptional.
cant get better
I don't think it can get better than Rohan. Its his humble nature and passion that he brings to each class that drives me.
Details - END

The END Program

GPT3 or the END of NLP as we know it!
Learn Deep NLP beyond RNNs/LSTMs from scratch and learn why everything until GPT3 is absolete now. Registrations will open in mid-December!

PHASE #1 - Transformers

Lecture Title
Background and Basics of Modern NLP
Embeddings & Dimensionality
Advanced Python for NLP & Processing
PyTorch for NLP and Model Types
Backpropagation for NLP
Dependency Parsing for NLP
RNNs are dead and their renewed relevance
Attention and Memory in Deep NLP
Deep NLP using Convolutions
Transformers with Linear Attention
Infinity Capstone Project

Phase #2 - GPT

Lecture Title
Coreference Resolution & Synthesizer
Reformer: the efficient transformer
Bi-Directional Transformers
Document Level Models & Contextual Representations
Autoregressive Pretraining for Language Understanding
GPT1 & Models of Dialog
Building and training GPT2
GPT3 Deep Dive: Part 1 Architecture & Preprocessing
GPT3 Deep Dive: Part 2 Training and DEployment
Advanced NLP over the Edge
Endgame Capstone Project
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Register here or fill the form below now for EPAi 2 Phase 1 registrations. New batches are starting from 2nd January. Registrations end in December.



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