Neural Networks

Intro [DPU]

Intro Again (Including Project) [DPU]

Perceptron [MMM]

What is a Perceptron? Perceptron Vs Neuron | Perceptron Geometric Intuition [CampusX]

Perceptron Trick | How to Train a Perceptron [CampusX]

Perceptron Loss Function | Hinge Loss | Binary Cross Entropy | Sigmoid Function [CampusX]

Problem with Perceptron [CampusX]

Perceptron [DPU]

Activation Functions | Sigmoid, Tanh and ReLU [CampusX]

ReLU Variants | Leaky ReLU, PReLU, ELU, SELU [CampusX]

Loss Functions in Deep Learning [CampusX]

MLP — First Part [MMM]

Hidden Neuron Computation [MMM]

Matrix Representation [MMM]

MLP Notation [CampusX]

Multi Layer Perceptron | MLP Intuition [CampusX]

Forward Propagation | How a Neural Network Predicts Output [CampusX]

XOR, MLP Intro, Boolean Function w/ AND/OR/NOT Perceptron [DPU]

Batch Normalization in Deep Learning [CampusX]

Gradient Descent | Batch Vs Stochastic Vs Mini-Batch [CampusX]

Optimizers in Deep Learning — Part 1 [CampusX]

Exponentially Weighted Moving Average [CampusX]

SGD with Momentum Explained [CampusX]

Nesterov Accelerated Gradient (NAG) Explained [CampusX]

AdaGrad Explained [CampusX]

RMSProp Explained [CampusX]

Adam Optimizer Explained [CampusX]

NN Training, Regularization, Optimization [DPU]

Backpropagation — Part 1: The What? [CampusX]

Backpropagation — Part 2: The How? [CampusX]

Backpropagation — Part 3: The Why? [CampusX]

MLP Backpropagation Part I [DPU]

MLP Backpropagation Part II & Computational Graph Overview [DPU]

Computational Graph [DPU]

Early Stopping in Neural Networks [CampusX]

Dropout Layer in Deep Learning [CampusX]

Dropout Layers — Code Example [CampusX]

Regularization | L1 & L2 | Weight Decay [CampusX]

RNN [MMM]

Why RNNs Are Needed | RNN Vs ANN [CampusX]

Recurrent Neural Network | Forward Propagation | Architecture [CampusX]

Types of RNN | Many-to-Many, One-to-Many, Many-to-One [CampusX]

How Backpropagation Works in RNN | Backpropagation Through Time [CampusX]

Problems with RNN [CampusX]

RNN Intro [DPU]

RNN BPTT [DPU]

LSTM [MMM]

LSTM | Long Short Term Memory — Part 1: The What? [CampusX]

LSTM Architecture — Part 2: The How? [CampusX]

LSTM — Part 3: Next Word Predictor [CampusX]

LSTM, Peephole LSTM [DPU]

GRU [MMM]

Gated Recurrent Unit (GRU) [CampusX]

GRU, Deep/Multilayer RNN, Bidirectional RNN & Syllabus Review [DPU]

CNN [MMM]

CNN Math [MMM]

What is CNN? CNN Intuition [CampusX]

Convolution Operation [CampusX]

Padding & Strides in CNN [CampusX]

Pooling Layer in CNN | Max Pooling [CampusX]

Backpropagation in CNN — Part 1 [CampusX]

CNN Backpropagation — Part 2 [CampusX]

CNN Intro [DPU]

CNN Part 1 [DPU]

CNN Part 2 [DPU]

UL, AE, VAE [DPU]

VAE, LSTM Autoencoder [DPU]

Encoder Decoder | Sequence-to-Sequence Architecture [CampusX]

Attention Mechanism | Seq2Seq Networks [CampusX]

Bahdanau Attention Vs Luong Attention [CampusX]

Introduction to Transformers — Part 1 [CampusX]

What is Self Attention? — Part 2 [CampusX]

Self Attention in Transformers | Simple Explanation with Code [CampusX]

Scaled Dot Product Attention [CampusX]

Self Attention Geometric Intuition [CampusX]

Why Is Self Attention Called 'Self'? | Self Vs Luong Attention [CampusX]

Multi-Head Attention in Transformers [CampusX]

Positional Encoding in Transformers [CampusX]

Layer Normalization | Layer Norm Vs Batch Norm [CampusX]

Transformer Architecture — Part 1: Encoder [CampusX]

Masked Self Attention | Masked Multi-Head Attention [CampusX]

Cross Attention in Transformers [CampusX]

Transformer Decoder Architecture [CampusX]

Transformer Inference [CampusX]

Transformer Part 1 [DPU]

Transformer Part 2 [DPU]

GNN [DPU]

Kohonen Neural Network — Part 2 [MMM]

Kohonen Neural Network [MMM]

Project Discussion [DPU]