Introduction To Deep Learning A Beginner S Edition
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Deep Learning for Beginners
Author: Dr. Pablo Rivas
language: en
Publisher: Packt Publishing Ltd
Release Date: 2020-09-18
Implement supervised, unsupervised, and generative deep learning (DL) models using Keras and Dopamine with TensorFlow Key FeaturesUnderstand the fundamental machine learning concepts useful in deep learningLearn the underlying mathematical concepts as you implement deep learning models from scratchExplore easy-to-understand examples and use cases that will help you build a solid foundation in DLBook Description With information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning. This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and you already have the basic mathematical and programming knowledge required to get started. The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples, and learn how to build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book. By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks. What you will learnImplement recurrent neural networks (RNNs) and long short-term memory (LSTM) for image classification and natural language processing tasksExplore the role of convolutional neural networks (CNNs) in computer vision and signal processingDiscover the ethical implications of deep learning modelingUnderstand the mathematical terminology associated with deep learningCode a generative adversarial network (GAN) and a variational autoencoder (VAE) to generate images from a learned latent spaceImplement visualization techniques to compare AEs and VAEsWho this book is for This book is for aspiring data scientists and deep learning engineers who want to get started with the fundamentals of deep learning and neural networks. Although no prior knowledge of deep learning or machine learning is required, familiarity with linear algebra and Python programming is necessary to get started.
Neural Network and Deep Learning for Beginners: Concept and Implementation Using TensorFlow and Keras
This book provides a structured guide for beginners to learn about neural networks and yet use them to develop intelligence systems. This book is delivered to readers in three parts. The introduction chapter engages readers in various applications that use neural networks as their backbone. Readers are exposed to the significant use of neural networks in these applications, which represents the intelligence of the human brain. The first part provides readers with important background topics: basic programming and the supervised learning paradigm. This is crucial, as it is the foundation of artificial intelligence application development using neural networks. It gives detailed processes for deep learning system development. The second part explains the mechanism of a neural network in extensive detail. Readers will learn about important components in a neural network, namely the input layer, hidden layer, and output layer. Within that layer, readers are exposed to concepts known as loss function and propagation in detail, which represent a machine learning ability. At the end of this part, readers will also learn the tuning process of a neural network model for best performance. The third part gives examples of case studies. It guides readers on how to develop a real-world intelligence system from scratch. The case studies expose the readers to the processes of assessing and solving the problem, dataset compatibility, model development, training and testing, and finally measuring the accuracy of the system. As readers progress through the whole course, hands-on materials will be provided as part of the practise. The hands-on uses the Python programming language with TensorFlow and Keras libraries.
Introducing Data Science for Beginners 2025 | Learn Data Analysis, Visualization & Machine Learning Basics
Introducing Data Science for Beginners 2025 is your essential guide to understanding the fundamentals of data science, even if you have no prior experience. This beginner-friendly book breaks down core concepts such as data analysis, visualization, statistics, and the basics of machine learning. With real-world examples and simplified explanations, it helps you build a strong foundation in Python, data handling, and decision-making through data. Whether you're a student, professional, or enthusiast, this book provides the perfect starting point to enter the world of data science with confidence.