Overview:
This book concentrates on the domain, models, and algorithms needed for deep learning applications, helping you quickly increase your practical knowledge. Applications for Deep Learning Natural language processing, Chabot’s, and face and object recognition are all covered under the heading of using Python. Additionally, it talks about well-known APIs like scikit-learn, Microsoft Azure, and IBM Watson. Convolutional neural networks, recurrent neural networks, and multilayer perceptron’s are only a few examples of the advanced and intermediate levels of deep learning covered in this book. The objective is to provide you with the ideas, methods, and algorithm implementations required to develop deep learning-capable software.
Deep Learning with Python:
AI and machine learning have made it feasible for things like spam filters, recommendation systems, search engines, personal assistants, and fraud detection systems, and there will undoubtedly be more in the future. Product owners aim to create high-performing apps. Making software behave like a human requires developing algorithms that process information intelligently. Python is a language that works well for machine learning and AI. Python provides code that is clear and readable. Python’s simplicity enables developers to create dependable systems, but machine learning and AI are powered by complicated algorithms and flexible workflows. Instead of concentrating on the technical details of the language, developers get to spend all of their efforts on solving an ML problem. Python is a good choice for team development when several developers are engaged. Python is a general-purpose language, so it can do a variety of complicated machine learning jobs and make it simple to construct short prototypes for testing your product for machine learning.
Topics covered by this book:
- The fundamentals of TensorFlow, the deep learning framework, are covered in chapter 1. Deep learning excels at pattern detection, especially when applied to time-series data, speech, sound, and image data. Deep learning enables classification, prediction, clustering, and feature extraction.
- According to Chapter 2, Keras frees developers from having to worry about the minute details of tensors, their shapes, and their mathematical formulations so they may concentrate on the core ideas of deep learning, such as building layers for neural networks.
- In chapter 3, you will learn about artificial neural networks in general before beginning to study multilayered perceptrons.
- We learn about a multilayered perceptron in chapter 4. You can add one or more hidden layers between the input and output layers in this straightforward neural network architecture.
- We shall talk about the development of multilayer perceptrons in chapter 5.To predict numerical targets, linear regression and nonlinear regression are utilised.
- The convolutional neural network is discussed in Chapter 6. It is a deep, feed-forward artificial neural network that learns internal feature representations and generalises the features in common image tasks like object recognition and other computer vision issues to maintain the hierarchical structure.
- The creation of a CNN model using TensorFlow will be demonstrated in chapter 7. You can create an image classifier that predicts and categorises the photos with the aid of a CNN model. Typically, you add a few layers to the model architecture with initial weight and bias values.
- The creation of CNN models using Keras will be demonstrated in chapter 8. You can create an image classifier that predicts and categorises the photos with the aid of a CNN model. Typically, you add a few layers to the model architecture with initial weight and bias values.
Zain