Anthony So – författare

Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guideKey FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook DescriptionVarious intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models.Starting with an introduction to RL, youÔÇÖll be guided through different RL environments and frameworks. YouÔÇÖll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once youÔÇÖve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, youÔÇÖll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, youÔÇÖll find out when to use a policy-based method to tackle an RL problem.By the end of The Reinforcement Learning Workshop, youÔÇÖll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is forIf you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.

Get started with TensorFlow fundamentals to build and train deep learning models with real-world data, practical exercises, and challenging activitiesKey FeaturesUnderstand the fundamentals of tensors, neural networks, and deep learningDiscover how to implement and fine-tune deep learning models for real-world datasetsBuild your experience and confidence with hands-on exercises and activitiesBook DescriptionGetting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging.If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it’ll quickly get you up and running.You’ll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you’ll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models.Building on this solid foundation, you’ll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing.By the end of this deep learning book, you’ll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.What you will learnGet to grips with TensorFlow's mathematical operationsPre-process a wide variety of tabular, sequential, and image dataUnderstand the purpose and usage of different deep learning layersPerform hyperparameter-tuning to prevent overfitting of training dataUse pre-trained models to speed up the development of learning modelsGenerate new data based on existing patterns using generative modelsWho this book is forThis TensorFlow book is for anyone who wants to develop their understanding of deep learning and get started building neural networks with TensorFlow. Basic knowledge of Python programming and its libraries, as well as a general understanding of the fundamentals of data science and machine learning, will help you grasp the topics covered in this book more easily.

With knowledge and information shared by experts, take your first steps towards creating scalable AI algorithms and solutions in Python, through practical exercises and engaging activitiesKey FeaturesLearn about AI and ML algorithms from the perspective of a seasoned data scientistGet practical experience in ML algorithms, such as regression, tree algorithms, clustering, and moreDesign neural networks that emulate the human brainBook DescriptionYou already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine. But do you want to be able to create your own AI and ML models and develop your skills in these domains to kickstart your AI career?The Applied Artificial Intelligence Workshop gets you started with applying AI with the help of practical exercises and useful examples, all put together cleverly to help you gain the skills to transform your career.The book begins by teaching you how to predict outcomes using regression. You will then learn how to classify data using techniques such as k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. As you progress, you’ll explore various decision trees by learning how to build a reliable decision tree model that can help your company find cars that clients are likely to buy. The final chapters will introduce you to deep learning and neural networks. Through various activities, such as predicting stock prices and recognizing handwritten digits, you’ll learn how to train and implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs).By the end of this applied AI book, you’ll have learned how to predict outcomes and train neural networks and be able to use various techniques to develop AI and ML models.What you will learnCreate your first AI game in Python with the minmax algorithmImplement regression techniques to simplify real-world dataExperiment with classification techniques to label real-world dataPerform predictive analysis in Python using decision trees and random forestsUse clustering algorithms to group data without manual supportLearn how to use neural networks to process and classify labeled imagesWho this book is forThe Applied Artificial Intelligence Workshop is designed for software developers and data scientists who want to enrich their projects with machine learning. Although you do not need any prior experience in AI, it is recommended that you have knowledge of high school-level mathematics and at least one programming language, preferably Python. Although this is a beginner’s book, experienced students and programmers can improve their Python skills by implementing the practical applications given in this book.

Gain expert guidance on how to successfully develop machine learning models in Python and build your own unique data platformsKey FeaturesGain a full understanding of the model production and deployment processBuild your first machine learning model in just five minutes and get a hands-on machine learning experienceUnderstand how to deal with common challenges in data science projectsBook DescriptionWhere there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities.The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search.Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch.By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.What you will learnExplore the key differences between supervised learning and unsupervised learningManipulate and analyze data using scikit-learn and pandas librariesUnderstand key concepts such as regression, classification, and clusteringDiscover advanced techniques to improve the accuracy of your modelUnderstand how to speed up the process of adding new featuresSimplify your machine learning workflow for productionWho this book is forThis is one of the most useful data science books for aspiring data analysts, data scientists, database engineers, and business analysts. It is aimed at those who want to kick-start their careers in data science by quickly learning data science techniques without going through all the mathematics behind machine learning algorithms. Basic knowledge of the Python programming language will help you easily grasp the concepts explained in this book.

Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities.The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search.Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch.By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.

Key Features

Gain a full understanding of the model production and deployment processBuild your first machine learning model in just five minutes and get a hands-on machine learning experienceUnderstand how to deal with common challenges in data science projects

What you will learn

Explore the key differences between supervised learning and unsupervised learningManipulate and analyze data using scikit-learn and pandas librariesUnderstand key concepts such as regression, classification, and clusteringDiscover advanced techniques to improve the accuracy of your modelUnderstand how to speed up the process of adding new featuresSimplify your machine learning workflow for production

Who this book is for

This is one of the most useful data science books for aspiring data analysts, data scientists, database engineers, and business analysts. It is aimed at those who want to kick-start their careers in data science by quickly learning data science techniques without going through all the mathematics behind machine learning algorithms. Basic knowledge of the Python programming language will help you easily grasp the concepts explained in this book.

You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You''ll learn from real examples that lead to real results.Throughout The Data Science Workshop, you''ll take an engaging step-by-step approach to understanding data science. You won''t have to sit through any unnecessary theory. If you''re short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It''s your choice. Learning on your terms, you''ll build up and reinforce key skills in a way that feels rewarding.Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you''ll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You''ll even earn a secure credential that you can share and verify online upon completion. It''s a premium learning experience that''s included with your printed copy. To redeem, follow the instructions located at the start of your data science book.Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You''ll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you''ll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.

Key Features

Ideal for the data science beginner who is getting started for the first timeA data science tutorial with step-by-step exercises and activities that help build key skillsStructured to let you progress at your own pace, on your own termsUse your physical print copy to redeem free access to the online interactive edition

What you will learn

Find out the key differences between supervised and unsupervised learningManipulate and analyze data using scikit-learn and pandas librariesLearn about different algorithms such as regression, classification, and clusteringDiscover advanced techniques to improve model ensembling and accuracySpeed up the process of creating new features with automated feature toolSimplify machine learning using open source Python packages

Who this book is for

Our goal at Packt is to help you be successful, in whatever it is you choose to do. The Data Science Workshop is an ideal data science tutorial for the data science beginner who is just getting started. Pick up a Workshop today and let Packt help you develop skills that stick with you for life.

Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text

Key Features

Understand how to implement deep learning with TensorFlow and KerasLearn the fundamentals of computer vision and image recognitionStudy the architecture of different neural networks

Book Description

Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout.

The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You''ll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you''ll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you''ll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis.

By the end of this deep learning book, you''ll have learned the skills essential for building deep learning models with TensorFlow and Keras.

What you will learn

Understand how deep learning, machine learning, and artificial intelligence are differentDevelop multilayer deep neural networks with TensorFlowImplement deep neural networks for multiclass classification using KerasTrain CNN models for image recognitionHandle sequence data and use it in conjunction with RNNsBuild a GAN to generate high-quality synthesized images

Who this book is for

If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.