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Deep Learning

Syllabus, study tools, lectures, and curriculum map.

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Weekly outline

Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

History of Deep Learning, McCulloch Pitts Neuron, Thresholding Logic, Perceptron Learning Algorithm and Convergence

Incomplete

02W02

Multilayer Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons, Gradient Descent

Incomplete

03W03

Feedforward Neural Networks, Representation Power of Feedforward Neural Networks, Backpropagation

Incomplete

04W04

Gradient Descent(GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, Adagrad, AdaDelta,RMSProp, Adam,AdaMax,NAdam, learning rate scheduler

Incomplete

05W05

Autoencoders and relation to PCA , Regularization in autoencoders, Denoising autoencoders, Sparse autoencoders, Contractive autoencoders

Incomplete

06W06

Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Parameter sharing and tying, Injecting noise at input, Ensemble metho

Incomplete

07W07

Greedy Layer Wise Pre-training, Better activation functions, Better weight initialization methods, Batch Normalization

Incomplete

08W08

Learning Vectorial Representations Of Words, Convolutional Neural Networks, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet

Incomplete

09W09

Visualizing Convolutional Neural Networks, Guided Backpropagation, Deep Dream, Deep Art, Fooling Convolutional Neural Networks

Incomplete

010W10

Recurrent Neural Networks, Backpropagation Through Time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT

Incomplete

011W11

Gated Recurrent Units (GRUs), Long Short Term Memory (LSTM) Cells, Solving the vanishing gradient problem with LSTM

Incomplete

012W12

Encoder Decoder Models, Attention Mechanism, Attention over images, Hierarchical Attention, Transformers.

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

1 min

⚙️ Optimization Methods

Open this first for the core logic and traps.

Dive in

1 min

⚙️ Optimizers Comparison

Open this first for the core logic and traps.

Dive in

1 min

➡️ Forward Propagation & Loss Functions

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 // Playlist Connected

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

BSCS3004

BSc Degree

4 Credits

Deep Learning

To study the basics of Neural Networks and their various variants such as the Convolutional Neural Networks and Recurrent Neural Networks, to study...

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

History of Deep Learning, McCulloch Pitts Neuron, Thresholding Logic, Perceptron Learning Algorithm and Convergence

Module 2

Multilayer Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons, Gradient Descent

Module 3

Feedforward Neural Networks, Representation Power of Feedforward Neural Networks, Backpropagation

Module 4

Gradient Descent(GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, Adagrad, AdaDelta,RMSProp, Adam,AdaMax,NAdam, learning rate scheduler

Module 5

Autoencoders and relation to PCA , Regularization in autoencoders, Denoising autoencoders, Sparse autoencoders, Contractive autoencoders

Module 6

Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Parameter sharing and tying, Injecting noise at input, Ensemble metho

Module 7

Greedy Layer Wise Pre-training, Better activation functions, Better weight initialization methods, Batch Normalization

Module 8

Learning Vectorial Representations Of Words, Convolutional Neural Networks, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet

Module 9

Visualizing Convolutional Neural Networks, Guided Backpropagation, Deep Dream, Deep Art, Fooling Convolutional Neural Networks

Module 10

Recurrent Neural Networks, Backpropagation Through Time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT

Module 11

Gated Recurrent Units (GRUs), Long Short Term Memory (LSTM) Cells, Solving the vanishing gradient problem with LSTM

Module 12

Syllabus

Topic

History of Deep Learning, McCulloch Pitts Neuron, Thresholding Logic, Perceptron Learning Algorithm and Convergence

Multilayer Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons, Gradient Descent

Feedforward Neural Networks, Representation Power of Feedforward Neural Networks, Backpropagation

Gradient Descent(GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, Adagrad, AdaDelta,RMSProp, Adam,AdaMax,NAdam, learning rate scheduler

Autoencoders and relation to PCA , Regularization in autoencoders, Denoising autoencoders, Sparse autoencoders, Contractive autoencoders

Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Parameter sharing and tying, Injecting noise at input, Ensemble metho

Greedy Layer Wise Pre-training, Better activation functions, Better weight initialization methods, Batch Normalization

Learning Vectorial Representations Of Words, Convolutional Neural Networks, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet

Visualizing Convolutional Neural Networks, Guided Backpropagation, Deep Dream, Deep Art, Fooling Convolutional Neural Networks

Recurrent Neural Networks, Backpropagation Through Time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT

Gated Recurrent Units (GRUs), Long Short Term Memory (LSTM) Cells, Solving the vanishing gradient problem with LSTM

Encoder Decoder Models, Attention Mechanism, Attention over images, Hierarchical Attention, Transformers.

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

Execution Protocol

Topic

History of Deep Learning, McCulloch Pitts Neuron, Thresholding Logic, Perceptron Learning Algorithm and Convergence

Multilayer Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons, Gradient Descent

Feedforward Neural Networks, Representation Power of Feedforward Neural Networks, Backpropagation

Gradient Descent(GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, Adagrad, AdaDelta,RMSProp, Adam,AdaMax,NAdam, learning rate scheduler

Autoencoders and relation to PCA , Regularization in autoencoders, Denoising autoencoders, Sparse autoencoders, Contractive autoencoders

Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Parameter sharing and tying, Injecting noise at input, Ensemble metho

Greedy Layer Wise Pre-training, Better activation functions, Better weight initialization methods, Batch Normalization

Learning Vectorial Representations Of Words, Convolutional Neural Networks, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet

Visualizing Convolutional Neural Networks, Guided Backpropagation, Deep Dream, Deep Art, Fooling Convolutional Neural Networks

Recurrent Neural Networks, Backpropagation Through Time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT

Gated Recurrent Units (GRUs), Long Short Term Memory (LSTM) Cells, Solving the vanishing gradient problem with LSTM

Encoder Decoder Models, Attention Mechanism, Attention over images, Hierarchical Attention, Transformers.

Video Archive

Intelligence Hub