BSDA5006 · workspace

Dl CV

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Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Introduction and Overview:

Incomplete

02W02

Visual Features and Representations:

Incomplete

03W03

Visual Matching:

Incomplete

04W04

Deep Learning Review:

Incomplete

05W05

Convolutional Neural Networks (CNNs):

Incomplete

06W06

Visualization and Understanding CNNs:

Incomplete

07W07

CNNs for Recognition, Verification, Detection, Segmentation:

Incomplete

08W08

Recurrent Neural Networks (RNNs):

Incomplete

09W09

Attention Models:

Incomplete

010W10

Deep Generative Models:

Incomplete

011W11

Variants and Applications of Generative Models in Vision:

Incomplete

012W12

Recent Trends:

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

1 min

BSDA5006 — Deep Learning for Computer Vision (DL-CV)

Open this first for the core logic and traps.

Dive in

4 min

Data Augmentation: Transformations, MixUp, CutMix, RandAugment, AutoAugment

Open this first for the core logic and traps.

Dive in

4 min

GANs for Computer Vision: Generator, Discriminator, DCGAN, CGAN, WGAN, CycleGAN

Open this first for the core logic and traps.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

Ask less. Recall more.

Open Practice Dock

SM-2 Active

Spaced Repetition

Flashcards

Spaced repetition engine for long-term retention of core concepts.

Start session

Not Available

Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // @IITMadrasBSDegreeProgramme

Uplink 12ms

Syllabus Synchronization: Active

Open current cue

Syllabus Matrix Registry

Global Course Index

Open Full Frame

Master Registry

v6.4 Directory

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSDA5006

PG / MTech

4 Credits

Deep Learning for Computer Vision

-Knowledge of basics of image processing and computer vision -Knowledge of building blocks of deep learning including feedforward networks, convolu...

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

Introduction and Overview:

Module 2

Visual Features and Representations:

Module 3

Visual Matching:

Module 4

Deep Learning Review:

Module 5

Convolutional Neural Networks (CNNs):

Module 6

Visualization and Understanding CNNs:

Module 7

CNNs for Recognition, Verification, Detection, Segmentation:

Module 8

Recurrent Neural Networks (RNNs):

Module 9

Attention Models:

Module 10

Deep Generative Models:

Module 11

Variants and Applications of Generative Models in Vision:

Module 12

Syllabus

Topic

Introduction and Overview:

Visual Features and Representations:

Visual Matching:

Deep Learning Review:

Convolutional Neural Networks (CNNs):

Visualization and Understanding CNNs:

CNNs for Recognition, Verification, Detection, Segmentation:

Recurrent Neural Networks (RNNs):

Attention Models:

Deep Generative Models:

Variants and Applications of Generative Models in Vision:

Recent Trends:

Compact recall strip

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Flashcards

Visual Labs

Mock Exam

OPPE Simulator

Secure Comm-Link Terminal

Syllabus Matrix Registry

Master Registry

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

Deep Learning for Computer Vision

Execution Protocol

Topic

Introduction and Overview:

Visual Features and Representations:

Visual Matching:

Deep Learning Review:

Convolutional Neural Networks (CNNs):

Visualization and Understanding CNNs:

CNNs for Recognition, Verification, Detection, Segmentation:

Recurrent Neural Networks (RNNs):

Attention Models:

Deep Generative Models:

Variants and Applications of Generative Models in Vision:

Recent Trends:

Video Archive

Intelligence Hub