2.5 Weight Initialization Techniques | CS601 |

Machine Learning

2.5 Weight Initialization Techniques

Welcome to our comprehensive guide on Neural Networks and Deep Learning! In this video series, we'll delve deep into understanding the fundamentals of neural networks, their representations, training techniques, and advanced topics like autoencoders, batch normalization, and regularization.

2.1 Introduction of Neural Network Representation: We kickstart with the basics, introducing you to the concept of neural networks and their representations.

2.2 Neural Network: From Biology to Simulation: Understand the inspiration behind neural networks and how they simulate biological neurons.

2.3 Neural Network Representation: Dive into the various components of neural networks, starting with activation functions.

2.4 Multilayer Notation: Learn about the notation used to represent multilayer neural networks.

2.5 Weight Initialization Techniques: Explore different methods to initialize weights in neural networks for effective learning.

2.6 Cross Validation- Testing and Training data: Understand the importance of cross-validation techniques in training and testing neural networks.

2.7 Backpropagation: Delve into the backpropagation algorithm, the backbone of training neural networks.

2.8 Gradient Descent: Learn about various flavors of gradient descent optimization algorithms.

2.9 Autoencoders: Discover the architecture and types of autoencoders, powerful tools for unsupervised learning.

2.10 Need of Batch Normalization: Understand the issues of covariate shift and how batch normalization addresses them.

2.11 Batch Normalization: Explore the concept and implementation of batch normalization in neural networks.

2.12 Overfitting & Underfitting: Learn about the challenges of overfitting and underfitting in neural networks and techniques to mitigate them.

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