The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.īy the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence and implement a neural network in TensorFlow. Along the way, you will also get career advice from deep learning experts from industry and academia. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.ĪI is transforming many industries.
BACKPROPAGATION OF CNN FROMSCRATCH HOW TO
In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more.
grads and params are calculated above while we choose the learning_rate.The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. We take the gradients, weight parameters, and a learning rate as the input. Updated parameters are returned by updateParameters() function. To compute backpropagation, we write a function that takes as arguments an input matrix X, the train labels y, the output activations from the forward pass as cache, and a list of layer_sizes. More specifically, we have the following: Our neural net has only one hidden layer.
During backpropagation (red boxes), we use the output cached during forward propagation (purple boxes). Generally, in a deep network, we have something like the following. We’ll write a function that will calculate the gradient of the loss function with respect to the parameters. Now comes the best part of this all: backpropagation!
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