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DNN Activation Functions in PyTorch

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Packt

Introduction to RNN and DNN

Course 1 of 3 in the Deep Learning: Recurrent Neural Networks with Python Specialization

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Artificial Intelligence is transforming industries by enabling machines to learn from data and make intelligent decisions. This course offers an in-depth exploration of Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN), two pivotal AI technologies. You鈥檒l start with the basics of RNNs and their applications, followed by an examination of DNNs, including their architecture and implementation using PyTorch. You will master building and deploying sophisticated AI models, develop RNN models for tasks like speech recognition and machine translation, understand and implement DNN architectures, and utilize PyTorch for model building and optimization. By the end, you'll have a robust knowledge of RNNs and DNNs and the confidence to apply these techniques in real-world scenarios. Designed for data scientists, machine learning engineers, and AI enthusiasts with basic programming (preferably Python) and statistics knowledge, this course combines theory with practical application through video lectures, hands-on exercises, and real-world examples.

Skills You'll Learn

Deep Learning, AI Applications, Recurrent Neural Network, Recurrent Neural Networks, Data Science, Machine Learning, Deep Neural Networks

From the lesson

Deep Neural Network (DNN) Overview

In this module, we will explore the fundamentals of Deep Neural Networks (DNNs) and their implementation using PyTorch. You will learn about the architecture and representational power of DNNs, understand the importance of activation functions, and get hands-on experience with perceptrons. We will also cover gradient descent techniques, loss functions, and optimization strategies for building and refining DNN models.

Why PyTorch4:17

PyTorch Installation and Tensors Introduction10:32

Automatic Differentiation PyTorch New7:36

Why DNNs in Machine Learning4:13

Representational Power and Data Utilization Capacity of DNN7:13

Perceptron Exercise2:36

Perceptron Exercise Solution3:16

Perceptron Implementation7:26

DNN Architecture3:52

DNN Architecture Exercise2:06

DNN Architecture Exercise Solution4:33

DNN Forward Step Implementation8:21

DNN Why Activation Function Is Required4:47

DNN Why Activation Function Is Required Exercise1:48

DNN Why Activation Function Is Required Exercise Solution3:39

DNN Properties of Activation Function6:04

DNN Activation Functions in PyTorch3:49

DNN What Is Loss Function7:10

DNN What Is Loss Function Exercise0:58

DNN What Is Loss Function Exercise Solution4:25

DNN What Is Loss Function Exercise 020:54

DNN What Is Loss Function Exercise 02 Solution3:15

DNN Loss Function in PyTorch5:45

DNN Gradient Descent5:58

DNN Gradient Descent Exercise3:02

DNN Gradient Descent Exercise Solution4:15

DNN Gradient Descent Implementation6:51

DNN Gradient Descent Stochastic Batch Minibatch7:07

DNN Gradient Descent Summary2:37

DNN Implementation Gradient Step4:02

DNN Implementation Stochastic Gradient Descent13:53

DNN Implementation Batch Gradient Descent6:46

DNN Implementation Minibatch Gradient Descent9:04

DNN Implementation in PyTorch15:19

DNN Weights Initializations4:35

DNN Learning Rate4:03

DNN Batch Normalization2:05

DNN Batch Normalization Implementation2:41

DNN Optimizations4:08

DNN Dropout3:58

DNN Dropout in PyTorch2:03

DNN Early Stopping3:34

DNN Hyperparameters3:33

DNN PyTorch CIFAR10 Example15:56

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