With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.

• Use Scikit-learn to track an example ML project end to end
• Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
• Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
• Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
• Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning

Si usted quiere aprender rob tica, siga leyendo...

Si usted quiere aprender rob tica, siga leyendo

La rob tica se ha ido metiendo en nuestras vidas y pronto los robots estar n en todas partes.

Usted sabe todo lo que hay que saber sobre rob tica?

Usted quiere saber m s sobre rob tica?

Usted quiere descubrir las ventajas de la rob tica?

Si es as , entonces usted ha venido al lugar correcto.

En este libro, usted aprender todo lo que necesita saber sobre rob tica como principiante:

• Los conceptos b sicos de la rob tica y cu les son algunas de las ventajas y desventajas.
• Razones por las que los expertos intentan advertirnos sobre los robots.
• Mitos y verdades sobre los robots.
• Automatizaci n rob tica de procesos y c mo se relaciona con la rob tica.
• Robots m viles y c mo han cambiado a lo largo de los a os.
• Inteligencia artificial y c mo se puede vincular a la rob tica.
• Aprendizaje autom tico y c mo lo usan los robots.
• Veh culos aut nomos y c mo funcionan.
• C mo los robots usan el reconocimiento de voz.
• Drones: qu son y c mo funcionan.
• C mo se utilizan los robots en los negocios y c mo podr an usurpar su trabajo.
• Respuestas a preguntas frecuentes sobre rob tica.
• Y mucho, mucho m s

Compre este libro ahora y aprenda m s sobre rob tica

2 manuscritos completos en 1 libro

• Inteligencia artificial: Lo que usted necesita saber sobre el aprendizaje autom tico, rob tica, aprendizaje profundo, Internet de las cosas, redes neuronales, y nuestro futuro
• Internet de las Cosas: Lo que Necesita Saber Sobre IdC, Macrodatos, An lisis Predictivo, Inteligencia Artificial, Aprendizaje Autom tico, Seguridad Cibern tica, y Nuestro Futuro

Este libro lo abarca todo, desde el aprendizaje autom tico hasta la rob tica y el Internet de las cosas.

Usted puede usarlo como una gu a til cuando se encuentre con titulares de noticias que hablan de un nuevo avance en la inteligencia artificial por parte de Google o Facebook.

Para cuando termine de leer, ser consciente de lo que son las redes neuronales artificiales, c mo funcionan el descenso de gradiente y la propagaci n hacia atr s, y qu es el aprendizaje profundo.

Tambi n aprender una historia completa de la IA (Inteligencia Artificial), desde el primer invento de las automatizaciones en la antig edad hasta los autos sin conductor de hoy.

La primera parte de este libro incluye:

• Entender c mo las m quinas pueden pensar y c mo aprenden.
• Aprender las cinco razones por las que los expertos nos advierten sobre la investigaci n de la IA.
• Encuentre las respuestas a los seis principales mitos de la inteligencia artificial.
• Aprenda qu son las redes neuronales y c mo funcionan, los cerebros del aprendizaje autom tico.
• Comprenda el aprendizaje por refuerzo y c mo se utiliza para ense ar sistemas de aprendizaje autom tico a trav s de la experiencia.
• Mant ngase actualizado con los m todos actuales de inteligencia artificial de vanguardia que utilizan el aprendizaje profundo.
• Aprenda los conceptos b sicos de los sistemas de recomendaci n.
• Ampl e su visi n actual de las m quinas y lo que es posible con la rob tica moderna.
• Entre al vasto mundo de las tecnolog as del Internet de las cosas.
• Descubra por qu la IA es el nuevo t tulo acad mico en negocios.
• Y mucho, mucho m s

Al leer la segunda parte de este libro, usted:

• Origen de IdC
• Seguridad IdC
• Hackeo tico
• Internet de las Cosas
• Bajo el C modo Pie de los Gigantes Tecnol gicos
• El Poder de los Fondos Infinitos
• Juguetes IdC
• Bio-rob tica
• An lisis Predictivo
• Aprendizaje Autom tico
• Inteligencia Artificial
• Seguridad Cibern tica
• Macrodatos
• Inteligencia de Negocios
• Realidad Aumentada
• Realidad Virtual
• Nuestro Futuro
• Y mucho, mucho m s

Obtenga este libro ahora para aprender m s sobre la inteligencia artificial y Internet de las Cosas

Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work.

This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included.

The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already

You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks.

Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples.

Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals.

Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi.

All the code in this has been tested to work on a Raspberry Pi Zero.

In Deep Learning with Python, Second Edition you will learn:

Deep learning from first principles
Image classification and image segmentation
Timeseries forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation
Full color printing throughout

Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You'll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach--even if you have no background in mathematics or data science. This book shows you how to get started.

About the book
Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you'll build your understanding through intuitive explanations, crisp color illustrations, and clear examples. You'll quickly pick up the skills you need to start developing deep-learning applications.

What's inside

Deep learning from first principles
Image classification and image segmentation
Time series forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation
Full color printing throughout

About the reader
For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.

About the author
François Chollet is a software engineer at Google and creator of the Keras deep-learning library.

Table of Contents
1 What is deep learning?
2 The mathematical building blocks of neural networks
3 Introduction to Keras and TensorFlow
4 Getting started with neural networks: Classification and regression
5 Fundamentals of machine learning
6 The universal workflow of machine learning
7 Working with Keras: A deep dive
8 Introduction to deep learning for computer vision
9 Advanced deep learning for computer vision
10 Deep learning for timeseries
11 Deep learning for text
12 Generative deep learning
13 Best practices for the real world
14 Conclusions

2024 Edition - Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository.

Purchase of the print or Kindle book includes a free PDF eBook.

Key Features:

- Learn how to leverage LangChain to work around LLMs' inherent weaknesses

- Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges

- Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality

Book Description:

ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis - illustrating the expansive utility of LLMs in real-world applications.

Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.

What You Will Learn:

- Create LLM apps with LangChain, like question-answering systems and chatbots

- Understand transformer models and attention mechanisms

- Automate data analysis and visualization using pandas and Python

- Grasp prompt engineering to improve performance

- Fine-tune LLMs and get to know the tools to unleash their power

- Deploy LLMs as a service with LangChain and apply evaluation strategies

- Privately interact with documents using open-source LLMs to prevent data leaks

Who this book is for:

The book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain.

Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.

Table of Contents

- What are Generative Models?

- LangChain: Core Fundamentals

- Getting started with LangChain

- Question Answering over Docs

- Building a Chatbot like ChatGPT/Bard

- Developing Software with LangChain Coder

- LLM for Data Analysis

- Prompt Engineering

- LLM applications in Production

- The Future of Generative Models

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.

Purchase of the print or Kindle book includes a free eBook in PDF format.

• Learn applied machine learning with a solid foundation in theory
• Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
• Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices

Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.

Why PyTorch?

PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.

You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).

This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

• Explore frameworks, models, and techniques for machines to 'learn' from data
• Use scikit-learn for machine learning and PyTorch for deep learning
• Train machine learning classifiers on images, text, and more
• Build and train neural networks, transformers, and boosting algorithms
• Discover best practices for evaluating and tuning models
• Predict continuous target outcomes using regression analysis
• Dig deeper into textual and social media data using sentiment analysis

If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.

Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.

1. Giving Computers the Ability to Learn from Data
2. Training Simple Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using Scikit-Learn
4. Building Good Training Datasets - Data Preprocessing
5. Compressing Data via Dimensionality Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
7. Combining Different Models for Ensemble Learning
8. Applying Machine Learning to Sentiment Analysis
9. Predicting Continuous Target Variables with Regression Analysis
10. Working with Unlabeled Data - Clustering Analysis
11. Implementing a Multilayer Artificial Neural Network from Scratch

(N.B. Please use the Look Inside option to see further chapters)

Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning.

About the Book

Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks.

What's inside

• The science behind deep learning
• Building and training your own neural networks
• Privacy concepts, including federated learning
• Tips for continuing your pursuit of deep learning

For readers with high school-level math and intermediate programming skills.

About the Author

Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform.

Table of Contents

1. Introducing deep learning: why you should learn it
2. Fundamental concepts: how do machines learn?
3. Introduction to neural prediction: forward propagation
4. Introduction to neural learning: gradient descent
5. Learning multiple weights at a time: generalizing gradient descent
6. Building your first deep neural network: introduction to backpropagation
7. How to picture neural networks: in your head and on paper
8. Learning signal and ignoring noise: introduction to regularization and batching
9. Modeling probabilities and nonlinearities: activation functions
10. Neural learning about edges and corners: intro to convolutional neural networks
11. Neural networks that understand language: king - man + woman == ?
12. Neural networks that write like Shakespeare: recurrent layers for variable-length data
13. Introducing automatic optimization: let's build a deep learning framework
14. Learning to write like Shakespeare: long short-term memory
15. Deep learning on unseen data: introducing federated learning
16. Where to go from here: a brief guide

Key Features
Written by PyTorch's creator and key contributors
Develop deep learning models in a familiar Pythonic way
Use PyTorch to build an image classifier for cancer detection
Diagnose problems with your neural network and improve training with data augmentation

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About The Book
Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more.

PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It's great for building quick models, and it scales smoothly from laptop to enterprise.

Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you'll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks.

What You Will Learn

Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning

This Book Is Written For
For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required.

About The Authors
Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer.

Table of Contents

PART 1 - CORE PYTORCH
1 Introducing deep learning and the PyTorch Library
2 Pretrained networks
3 It starts with a tensor
4 Real-world data representation using tensors
5 The mechanics of learning
6 Using a neural network to fit the data
7 Telling birds from airplanes: Learning from images
8 Using convolutions to generalize

PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER
9 Using PyTorch to fight cancer
10 Combining data sources into a unified dataset
11 Training a classification model to detect suspected tumors
12 Improving training with metrics and augmentation
13 Using segmentation to find suspected nodules
14 End-to-end nodule analysis, and where to go next

PART 3 - DEPLOYMENT
15 Deploying to production

To build is to be human. Creating things is part of our nature, and our principal mode of survival. The things we have built throughout history have always relied on design to turn discoveries into workable tools. But the underlying premise of design is that we can reason about the pieces and connections that make a thing work. From the stone axe to the rocket engine, design is predicated on our ability to see causal connections between the parts of a system.

But what happens when such causality is no longer apparent? When the things we must build to solve our challenges have inner workings that cannot be discerned? The answer lies in nature. Nature fashions truly complex objects that solve categorically hard problems. Complex things produce their most important outputs via emergence, whereby a system's structures and behaviors arise in ways that cannot be designed.

This book argues that we are entering an age where humanity must build truly complex things to continue our progress. This means learning to build as nature builds, and as it turns out, forces us to redefine knowledge and skill, and more broadly our scientific and engineering paradigm.

Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples

Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks

Purchase of the print or Kindle book includes a free eBook in PDF format

- Understand how to use PyTorch to build advanced neural network models

- Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker

- Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most from your data and build complex neural network models.

You'll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You'll deploy PyTorch models to production, including mobile devices. Finally, you'll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai for prototyping models to training models using PyTorch Lightning. You'll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face.

By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

- Implement text, vision, and music generating models using PyTorch

- Build a deep Q-network (DQN) model in PyTorch

- Deploy PyTorch models on mobile devices (Android and iOS)

- Become well-versed with rapid prototyping using PyTorch with fast.ai

- Perform neural architecture search effectively using AutoML

- Easily interpret machine learning models using Captum

- Design ResNets, LSTMs, and graph neural networks (GNNs)

- Create language and vision transformer models using Hugging Face

This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.

2 manuscritos completos en 1 libro

• Inteligencia artificial: Lo que usted necesita saber sobre el aprendizaje autom tico, rob tica, aprendizaje profundo, Internet de las cosas, redes neuronales, y nuestro futuro
• Internet de las Cosas: Lo que Necesita Saber Sobre IdC, Macrodatos, An lisis Predictivo, Inteligencia Artificial, Aprendizaje Autom tico, Seguridad Cibern tica, y Nuestro Futuro

Este libro lo abarca todo, desde el aprendizaje autom tico hasta la rob tica y el Internet de las cosas.

Usted puede usarlo como una gu a til cuando se encuentre con titulares de noticias que hablan de un nuevo avance en la inteligencia artificial por parte de Google o Facebook.

Para cuando termine de leer, ser consciente de lo que son las redes neuronales artificiales, c mo funcionan el descenso de gradiente y la propagaci n hacia atr s, y qu es el aprendizaje profundo.

Tambi n aprender una historia completa de la IA (Inteligencia Artificial), desde el primer invento de las automatizaciones en la antig edad hasta los autos sin conductor de hoy.

La primera parte de este libro incluye:

• Entender c mo las m quinas pueden pensar y c mo aprenden.
• Aprender las cinco razones por las que los expertos nos advierten sobre la investigaci n de la IA.
• Encuentre las respuestas a los seis principales mitos de la inteligencia artificial.
• Aprenda qu son las redes neuronales y c mo funcionan, los cerebros del aprendizaje autom tico.
• Comprenda el aprendizaje por refuerzo y c mo se utiliza para ense ar sistemas de aprendizaje autom tico a trav s de la experiencia.
• Mant ngase actualizado con los m todos actuales de inteligencia artificial de vanguardia que utilizan el aprendizaje profundo.
• Aprenda los conceptos b sicos de los sistemas de recomendaci n.
• Ampl e su visi n actual de las m quinas y lo que es posible con la rob tica moderna.
• Entre al vasto mundo de las tecnolog as del Internet de las cosas.
• Descubra por qu la IA es el nuevo t tulo acad mico en negocios.
• Y mucho, mucho m s

Al leer la segunda parte de este libro, usted:

• Origen de IdC
• Seguridad IdC
• Hackeo tico
• Internet de las Cosas
• Bajo el C modo Pie de los Gigantes Tecnol gicos
• El Poder de los Fondos Infinitos
• Juguetes IdC
• Bio-rob tica
• An lisis Predictivo
• Aprendizaje Autom tico
• Inteligencia Artificial
• Seguridad Cibern tica
• Macrodatos
• Inteligencia de Negocios
• Realidad Aumentada
• Realidad Virtual
• Nuestro Futuro
• Y mucho, mucho m s

Obtenga este libro ahora para aprender m s sobre la inteligencia artificial y Internet de las Cosas

In Deep Learning with R, Second Edition you will learn:

Deep learning from first principles
Image classification and image segmentation
Time series forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation

Deep Learning with R, Second Edition shows you how to put deep learning into action. It's based on the revised new edition of François Chollet's bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R.

About the book
Deep Learning with R, Second Edition is a hands-on guide to deep learning using the R language. As you move through this book, you'll quickly lock in the foundational ideas of deep learning. The intuitive explanations, crisp illustrations, and clear examples guide you through core DL skills like image processing and text manipulation, and even advanced features like transformers. This revised and expanded new edition is adapted from Deep Learning with Python, Second Edition by François Chollet, the creator of the Keras library.

What's inside

Image classification and image segmentation
Time series forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation

About the reader
For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.

About the author
François Chollet is a software engineer at Google and creator of Keras. Tomasz Kalinowski is a software engineer at RStudio and maintainer of the Keras and Tensorflow R packages. J.J. Allaire is the founder of RStudio, and the author of the first edition of this book.

Table of Contents
1 What is deep learning?
2 The mathematical building blocks of neural networks
3 Introduction to Keras and TensorFlow
4 Getting started with neural networks: Classification and regression
5 Fundamentals of machine learning
6 The universal workflow of machine learning
7 Working with Keras: A deep dive
8 Introduction to deep learning for computer vision
9 Advanced deep learning for computer vision
10 Deep learning for time series
11 Deep learning for text
12 Generative deep learning
13 Best practices for the real world
14 Conclusions

If you want to learn about robotics, then keep reading

Robotics is slowly creeping into our lives, and soon, robots will be everywhere.

Do you know everything there is to know about robotics?

Do you want to know more about robotics?

Do you want to discover the advantages of robotics?

If so, then you've come to the right place.

In this book, you will learn everything you need to know about robotics as a beginner:

• The basics of robotics and what some of the advantages and disadvantages are.
• Reasons that experts are trying to warn us about robots.
• Myths about robots and the actual truth.
• Robotic Process Automation and how it relates to robotics.
• Mobile robots and how they have changed throughout the years.
• Artificial Intelligence and how it can be tied to robotics.
• Machine learning and how robots use it.
• Autonomous vehicles and how they work.
• How robots use speech recognition.
• Drones - what they are and how they work.
• How robots are being used in business and how they could take your job.
• Answers to frequently asked questions about robotics.
• And much, much more

If you want to learn more about robotics, then scroll up and click add to cart

Summary
Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you'll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision!

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
How much has computer vision advanced? One ride in a Tesla is the only answer you'll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway.

About the book
How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition.

What's inside

Image classification and object detection
Advanced deep learning architectures
Transfer learning and generative adversarial networks
DeepDream and neural style transfer
Visual embeddings and image search

About the reader
For intermediate Python programmers.

About the author
Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio.

Table of Contents

PART 1 - DEEP LEARNING FOUNDATION

1 Welcome to computer vision

2 Deep learning and neural networks

3 Convolutional neural networks

4 Structuring DL projects and hyperparameter tuning

PART 2 - IMAGE CLASSIFICATION AND DETECTION

5 Advanced CNN architectures

6 Transfer learning

7 Object detection with R-CNN, SSD, and YOLO

PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS

8 Generative adversarial networks (GANs)

9 DeepDream and neural style transfer

10 Visual embeddings

Take the next steps toward mastering deep learning, the machine learning method thatâ s transforming the world around us by the second. In this practical book, youâ ll get up to speed on key ideas using Facebookâ s open source PyTorch framework and gain the latest skills you need to create your very own neural networks.

Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text, and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production.

• Learn how to deploy deep learning models to production
• Explore PyTorch use cases from several leading companies
• Learn how to apply transfer learning to images
• Apply cutting-edge NLP techniques using a model trained on Wikipedia
• Use PyTorchâ s torchaudio library to classify audio data with a convolutional-based model
• Debug PyTorch models using TensorBoard and flame graphs
• Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud

Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.

Continue your journey into the world of deep learning with Deep Learning with R in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/deep-​learning-with-r-in-motion).

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks.

About the Book

Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.

What's Inside

• Deep learning from first principles
• Setting up your own deep-learning environment
• Image classification and generation
• Deep learning for text and sequences

You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.

About the Authors

François Chollet is a deep-learning researcher at Google and the author of the Keras library.

J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras.

Table of Contents

PART 1 - FUNDAMENTALS OF DEEP LEARNING
1. What is deep learning?
2. Before we begin: the mathematical building blocks of neural networks
3. Getting started with neural networks
4. Fundamentals of machine learning
PART 2 - DEEP LEARNING IN PRACTICE
5. Deep learning for computer vision
6. Deep learning for text and sequences
7. Advanced deep-learning best practices
8. Generative deep learning
9. Conclusions

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.

Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.

You'll learn how to:

• Design ML architecture for computer vision tasks
• Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task
• Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model
• Preprocess images for data augmentation and to support learnability
• Incorporate explainability and responsible AI best practices
• Deploy image models as web services or on edge devices
• Monitor and manage ML models

An Introduction to Universal Artificial Intelligence provides the formal underpinning of what it means for an agent to act intelligently in an unknown environment. First presented in Universal Algorithmic Intelligence (Hutter, 2000), UAI offers a framework in which virtually all AI problems can be formulated, and a theory of how to solve them. UAI unifies ideas from sequential decision theory, Bayesian inference, and algorithmic information theory to construct AIXI, an optimal reinforcement learning agent that learns to act optimally in unknown environments. AIXI is the theoretical gold standard for intelligent behavior.

The book covers both the theoretical and practical aspects of UAI. Bayesian updating can be done efficiently with context tree weighting, and planning can be approximated by sampling with Monte Carlo tree search. It provides algorithms for the reader to implement, and experimental results to compare against. These algorithms are used to approximate AIXI. The book ends with a philosophical discussion of Artificial General Intelligence: Can super-intelligent agents even be constructed? Is it inevitable that they will be constructed, and what are the potential consequences?

This text is suitable for late undergraduate students. It provides an extensive chapter to fill in the required mathematics, probability, information, and computability theory background. You can also visit the author website: http: //www.hutter1.net/ai/uaibook2.htm.

Generative AI is our future, and with this action-filled guidebook, you can lift your organization to the next level.

Presented in a language that is accessible and organized, Generative AI for Leaders covers:

• What Generative AI is
• The benefits of Generative AI (including increased productivity and new product development)
• The challenges of Generative AI (including bias, security, and regulation)
• How to develop a Generative AI strategy to help you stand out in the marketplace
• How to build the right team and when to seek outside help
• Best practice methods for training employees on Generative AI
• A deeper look into sequences, word embeddings, and LLMs
• The progress and challenges of detecting Generative AI
• The future of Generative AI

Included is a list of 75+ concrete ideas you can implement today to begin making Generative AI work for your organization.

Best-selling author of The Sentient Machine and CEO of SparkCognition, Amir Husain has covered every nuts-and-bolts detail in this ONLY comprehensive guidebook on Generative AI that you will ever need.