Learn more about regularization dropout in deep learning and how they work and how to implement them in tensorflow.
Learn more about the hyperparameter tuning like learning rate, number of batches, number of epochs, number of hidden layer, activation functions.
Learn how to build a neural network, and how to build layers of the neural network.
How to use Neural Networks
We will learn Loss function for deep machine learning and deep learning and some algorithms to optimize the model like Backpropagation and Gradient Descent.
Learn the mathematics behind Deep Learning including Linear Algebra, Matrices and Vectors
Intro to Deep Learning with TensorFlow Tutorial
Introduction to machine learning
Learn Python with simple easy tutorial including variables, Lists, Loops, Functions, Classes, String, Modules, Files and Dictionaries
What is LSTM? Overview of Recurrent Neural Network and How do LSTMs Work? and LSTM Applications
What is Machine Learning (ML)? What is Deep Learning (DL)? What is Difference between Machine Learning and Deep Learning?
Learn more about Machine learning terms like data, training and testing, Model, Loss and accuracy, Hyperparameters, Overfitting and Underfitting.
What is a loss functions? common loss functions like Mean Squared Error (MSE), Mean Absolute Error (MAE), Cross-Entropy Loss, What loss function should i use?
What is artificial intelligence (AI)?, How does AI work? , Why is artificial intelligence important? , What are the types of artificial intelligence? and Advantages and Disadvantages of AI
