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Data Sciences

Deep Learning (DL)-Master class

ratings 

In this course you will learn about the deep Learning which uses multiple layers to extract higher-level features from the raw input. This course will help you become good at Deep Learning.

PRIVATE
Course Access

Unlimited Duration

Last Updated

July 29, 2021

Students Enrolled

20

Total Reviews

Posted by

Ernesto Lee

Certification

Deep learning has evolved hand-in-hand with the digital era  which has brought about an explosion of data in all forms and from every region of the world. Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans. In deep learning a computer model learns to perform classification tasks directly from images, text, or sound. A deep neural network is an artificial neural network  with multiple layers between the input and output layers. There are different types of neural networks but they always consist of the same components and these functioning similar to the human brains. Deep learning models can sometimes exceeding human-level performance. Deep learning AI is able to learn without human supervision and drawing from data that is both unstructured and unlabeled. The human brain works similarly, whenever we receive any new information the brain tries to compare it with known objects, the same concept is also used by deep neural networks.

Course Curriculum

    • Introduction to Neural Network and Deep Learning 00:00:00
    • How does Neural Network Work 00:00:00
    • Activation Functions Part 1 00:00:00
    • How to train Multilayer Neural Network and Gradient Descent 00:00:00
    • Chain Rule of Differentiation with Back Propagation 00:00:00
    • Vanishing Gradient Problem in ANN 00:00:00
    • Exploding Gradient Problem in ANN 00:00:00
    • Various Weights Initialization Techniques 00:00:00
    • Drop Out Layers 00:00:00
    • Rectified Linear Unit(Relu) and Leaky Relu Activation Functions Part2 00:00:00
    • Sigmoid, SoftMax Activation Functions 00:00:00
    • Stochastic Gradient Descent Vs Gradient Descent 00:00:00
    • Global Minima and Local Minima in Depth Understanding 00:00:00
    • Stochastic Gradient Descent with Momentum 00:00:00
    • Adagrad Optimizer 00:00:00
    • AdaDelta and RMSprop optimizer 00:00:00
    • HyperParameter Tuning – To Select Hidden Layers and Number of Hidden Neurons in ANN 00:00:00
    • Importing Necessary Packages 00:00:00
    • How to Import Data 00:00:00
    • Train, Test, Validation Split 00:00:00
    • Data preprocessing 00:00:00
    • Feature Scaling 00:00:00
    • Creating a Sequential object 00:00:00
    • Build a model using sequential object 00:00:00
    • Train the model 00:00:00
    • Validate the model on Test set 00:00:00
    • Perform the Hyper Parameter Tuning 00:00:00
    • Save the Final Model for Predictions 00:00:00
    • Convolution Neural Network vs Human Brain 00:00:00
    • What is Convolution operation in CNN 00:00:00
    • Padding in CNN 00:00:00
    • Operation of CNN 00:00:00
    • Max Pooling Layer in CNN 00:00:00
    • Flattening Layer followed by ANN 00:00:00
    • What is Data Augmentation 00:00:00
    • Create Image Dataset using Data Augmentation 00:00:00
    • Creating and optimize using Keras Tuner 00:00:00
    • Importing the Necessary Packages 00:00:00
    • Import the Images 00:00:00
    • Train, Test and Validation Split 00:00:00
    • Image Preprocessing 00:00:00
    • Data Augmentation 00:00:00
    • Image scaling 00:00:00
    • Create a Sequential Object 00:00:00
    • Build the model 00:00:00
    • Train the model 00:00:00
    • Test the model using validation set 00:00:00
    • Save the model for predictions 00:00:00
    • Introduction to Recurrent Neural Networks 00:00:00
    • RNN Forward Propagation 00:00:00
    • RNN Backward Propagation 00:00:00
    • Problem with simple RNN 00:00:00
    • Understanding LSTM Networks 00:00:00
    • Step-by-step LSTM walk Through 00:00:00
    • What is Gradient Exploding 00:00:00
    • How to know whether model is suffering from Exploding Gradient 00:00:00
    • How LSTM solves Gradient Exploding Problem 00:00:00
    • What is Vanishing Gradient 00:00:00
    • How to solve Vanishing Gradient Problem 00:00:00
    • Import the necessary packages 00:00:00
    • Import the data set 00:00:00
    • Convert data into One hot Representation 00:00:00
    • Data set Preprocessing 00:00:00
    • Embedding Representation 00:00:00
    • Create a model object 00:00:00
    • Train, Test split 00:00:00
    • Train the model 00:00:00
    • Regularizing the model with Dropout 00:00:00
    • Validating the model 00:00:00
    • Save the model for final prediction 00:00:00

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