Bücher von Anurag Jain

Attention Aspiring Business Owners, Entrepreneurs, and Leaders: AI may not replace you, but someone using AI will. Don't get left behind!Are you ready to step into an AI-powered future?i) Ready to harness the power of Artificial Intelligence to 10x your growth?ii) Wondering how AI tools like CHATGPT can increase your productivity?iii) Seeking a proven playbook to integrate AI across Business functions like Market Research, Marketing, Sales, HR, Customer Support & more?iv) Looking for Actionable, AI-driven business templates tailored for your business as a Founder, Leader, or Aspiring Entrepreneur?v) Curious to learn about the practical application of AI to build a business from scratch and create wealth with limited resources?If you've nodded in agreement to any of the above, you're about to unlock the AI powerhouse within you.10 Plug and Play AI Strategies for Entrepreneurs and Leaders:Crafted by a seasoned business expert, this playbook is your golden ticket to the AI-driven future. Strategically implement AI to skyrocket your growth.i) Unravel 10 proven strategies to Integrate AI into your Business.ii) Guaranteed to 10x your business and leadership impact.iii) Understand Best AI Tools, Prompt Engineering, Automation and more with AI.iv) Step-by-step playbook to Apply AI with 10 Live Examples of Launching different businesses like E-Commerce, D2C, Digital Marketing Agency, and more.v) AI handbook for Business Owners, Leaders, Entrepreneurs, and Students.BONUS: 10 ready-to-use AI-driven business launch templates!Who Needs this Book: Aspiring Entrepreneurs, Leaders, Career Professionals or Anyone aspiring to build their business or career with no prior experience.About the Author: Anurag Jain, a seasoned business leader, has spent over two decades building and consulting brands across diverse sectors like consumer, healthcare, e-commerce, fashion, travel, and tech.

L'apprendimento profondo è una sottoclasse dell'apprendimento automatico. L'apprendimento profondo si basa su metodi di apprendimento supervisionati e non supervisionati, basati su reti neurali artificiali. La sua architettura consiste in più livelli di elaborazione. Ogni strato produce una risposta non lineare, che viene generata utilizzando la risposta dello strato precedente. L'apprendimento profondo funziona imitando il cervello umano che lavora per l'elaborazione dei dati e la creazione di modelli. Utilizza una rete in grado di apprendere da dati non supervisionati. Questo libro presenta un approccio pratico per implementare le architetture di deep learning utilizzando la programmazione Python.

El aprendizaje profundo es una subclase del aprendizaje automático. El aprendizaje profundo se basa en métodos de aprendizaje supervisado y no supervisado, basados en redes neuronales artificiales. Su arquitectura consta de múltiples capas de procesamiento. Cada capa produce una respuesta no lineal, que se genera utilizando la respuesta de la capa anterior. El aprendizaje profundo funciona imitando el funcionamiento del cerebro humano para el procesamiento de datos y la creación de patrones. Utiliza una red capaz de aprender a partir de datos no supervisados. Este libro presenta un enfoque práctico para implementar arquitecturas de aprendizaje profundo utilizando programación Python.

Deep Learning ist eine Unterklasse des maschinellen Lernens. Deep Learning basiert auf überwachten und nicht überwachten Lernmethoden, die auf künstlichen neuronalen Netzen beruhen. Seine Architektur besteht aus mehreren Verarbeitungsebenen. Jede Schicht erzeugt eine nichtlineare Antwort, die aus der Antwort der vorherigen Schicht generiert wird. Deep Learning funktioniert, indem es die Arbeit des menschlichen Gehirns bei der Datenverarbeitung und Musterbildung nachahmt. Es verwendet ein Netzwerk, das in der Lage ist, aus unüberwachten Daten zu lernen. Dieses Buch stellt einen praktischen Ansatz zur Implementierung von Deep-Learning-Architekturen mit Hilfe der Python-Programmierung vor.

A aprendizagem profunda é uma subclasse de aprendizagem de máquinas. A aprendizagem profunda é baseada em métodos de aprendizagem supervisionados e não supervisionados, que se baseiam numa rede neural artificial. A sua arquitectura consiste em múltiplas camadas de processamento. Cada camada produz uma resposta não-linear, que é gerada utilizando a resposta da camada anterior. A aprendizagem profunda funciona imitando o cérebro humano que trabalha para o processamento de dados e criação de padrões. Utiliza uma rede, que é capaz de aprender a partir de dados não supervisionados. Este livro apresenta uma abordagem prática para implementar arquitecturas de aprendizagem profunda utilizando a programação Python.

L'apprentissage profond est une sous-classe de l'apprentissage automatique. L'apprentissage profond repose sur des méthodes d'apprentissage supervisé et non supervisé, qui sont basées sur un réseau de neurones artificiels. Son architecture se compose de plusieurs couches de traitement. Chaque couche produit une réponse non linéaire, qui est générée en utilisant la réponse de la couche précédente. L'apprentissage profond fonctionne en imitant le fonctionnement du cerveau humain pour le traitement des données et la création de modèles. Il utilise un réseau capable d'apprendre à partir de données non supervisées. Ce livre présente une approche pratique pour mettre en ¿uvre des architectures d'apprentissage profond à l'aide de la programmation Python.

Power quality improvement is an important issue in respect of increasing application for non-linear loads. A group of devices, called Custom Power Devices (CPDs) are in use for power quality improvement. One of the CPD distributed static compensator (DSTATCOM) is proposed for compensating the power quality problems. The DSTATCOM is design for improvement, performance of distribution system under linear and non-linear loads. Especially in non-linear load DSTATCOM inject harmonics current components into the system to improve power quality of source current and source voltage. Further in the proposed systems the rating and size of DSTATCOM is reduced with the help of Star/Delta and Zig/Zag transformer. The proposed systems are employed for the reduce rating of DC current, compensation of harmonic current, reactive power, neutral current, load balancing and the voltage regulation. In this research work comparison of proposed topology of DSTATCOM consisting of a three leg VSC with the Star/Delta and Zig/Zag transformer is employed for power quality improvement. The performance of proposed DSTATCOM system is demonstrated under balanced, non-linear loads in MATLAB/Simulink environment.

Deep learning is a sub class of machine learning. Deep learning is based upon supervised and unsupervised learning methods, which are based on artificial neural network. Its architecture consists of multiple processing layers. Each layer produces nonlinear response, which is generated using the response of previous layer. Deep learning functions by imitating the human brain working for data processing and pattern creation. It uses a network, which is capable of learning from unsupervised data. This book presents a practical approach to implement deep learning architectures using Python programming.