Deep Learning

Deep Learning is a cutting-edge field of artificial intelligence that mimics the human brain's ability to learn and make decisions. By mastering deep learning, you'll gain the skills to build and train complex neural networks that power technologies like self-driving cars, image and speech recognition, and advanced natural language processing. With applications across healthcare, finance, entertainment, and more, deep learning is transforming industries and driving innovation. Start learning deep learning today and become a part of the AI revolution that is shaping the future of technology

Course Content

Demo Video

Neurons

Synapses

Weights

Biases

Activation functions

Input layer

Hidden layers

Output layer

Feedforward networks

Backpropagation

Gradient descent

Learning rate

Loss function

Epochs

Batch size

Training data

Validation data

Test data

Overfitting

Underfitting

Regularization

Dropout

Weight initialization

Normalization

Standardization

Hyperparameters

Model evaluation

Cross-validation

Sigmoid

Tanh

ReLU (Rectified Linear Unit)

Leaky ReLU

ELU (Exponential Linear Unit)

Softmax

Swish

Hard Sigmoid

Parametric ReLU (PReLU)

Thresholded ReLU

Mean Squared Error (MSE)

Mean Absolute Error (MAE)

Binary Cross-Entropy

Categorical Cross-Entropy

Sparse Categorical Cross-Entropy

Hinge Loss

Kullback-Leibler Divergence

Huber Loss

Log-Cosh Loss

Cosine Proximity

Stochastic Gradient Descent (SGD)

Mini-batch Gradient Descent

Momentum

Nesterov Accelerated Gradient (NAG)

Adagrad

Adadelta

RMSprop

Adam

Adamax

Nadam

L1 regularization

L2 regularization

Dropout

DropConnect

Early stopping

Data augmentation

Batch normalization

Layer normalization

Accuracy

Precision

Recall

F1-score

ROC-AUC

Confusion matrix

Precision-Recall curve

Mean Average Precision (mAP)

Log loss

R-squared.

Convolutional layers

Filters (kernels)

Feature maps

Receptive field

Stride

Padding

Pooling layers

Max pooling

Average pooling

Global pooling

Flattening

Fully connected layers

Softmax activation

Cross-entropy loss

Backpropagation

Gradient descent

Batch normalization

Layer normalization

Dropout

VGG 16

VGG19

ResNet

TensorFlow

Keras

PyTorch

Recurrent Neural Networks (RNNs)

Hidden state

Sequence modeling

Backpropagation through time (BPTT)

Vanishing gradient problem

Exploding gradient problem

Long Short-Term Memory (LSTM)

Gated Recurrent Unit (GRU)

Image reading and writing

Image resizing

Image rotation

Image translation

Image filtering

Edge detection

Contour detection

Color space conversion

Image thresholding

Geometric transformations

Image blending

Feature detection

Object tracking

Face detection

Video processing

Viola Jones

HOG

Yolo v1

Yolo v2

Yolo v3

Yolo v4

Yolo v7

Yolo v8

Unet Segmentation

v7 Segmentation

POS Estimation

Instructor

Kamal

Data Science and Generative AI Trainer

4.4 Rating
10k+ Students Trained
100+ Batches Completed

Computer Vision Engineer at E&Y Experience in Neural Networks Currently working on Computer Vision Use Cases Artificial Intelligence Trainer and Mentor