Biznes IT

The Complete Metasploit Guide. Explore effective penetration testing techniques with Metasploit

Sagar Rahalkar, Nipun Jaswal

Most businesses today are driven by their IT infrastructure, and the tiniest crack in this IT network can bring down the entire business. Metasploit is a pentesting network that can validate your system by performing elaborate penetration tests using the Metasploit Framework to secure your infrastructure.This Learning Path introduces you to the basic functionalities and applications of Metasploit. Throughout this book, you’ll learn different techniques for programming Metasploit modules to validate services such as databases, fingerprinting, and scanning. You’ll get to grips with post exploitation and write quick scripts to gather information from exploited systems. As you progress, you’ll delve into real-world scenarios where performing penetration tests are a challenge. With the help of these case studies, you’ll explore client-side attacks using Metasploit and a variety of scripts built on the Metasploit Framework.By the end of this Learning Path, you’ll have the skills required to identify system vulnerabilities by using thorough testing.This Learning Path includes content from the following Packt products:Metasploit for Beginners by Sagar RahalkarMastering Metasploit - Third Edition by Nipun Jaswal

The Deep Learning with Keras Workshop. Learn how to define and train neural network models with just a few lines of code

Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat

New experiences can be intimidating, but not this one! This beginner’s guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks.What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework.The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you’ll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you’ll explore recurrent neural networks and learn how to train them to predict values in sequential data.By the end of this book, you'll have developed the skills you need to confidently train your own neural network models.

The Deep Learning with PyTorch Workshop. Build deep neural networks and artificial intelligence applications with PyTorch

Hyatt Saleh

Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you’re starting from scratch.It’s no surprise that deep learning’s popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you’ll use PyTorch to understand the complexity of neural network architectures.The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You’ll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you’ll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues.By the end of this book, you’ll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps.

The Deep Learning Workshop. Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras

Mirza Rahim Baig, Thomas V. Joseph, Nipun...

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.

The Definitive Guide to Google Vertex AI. Accelerate your machine learning journey with Google Cloud Vertex AI and MLOps best practices

Jasmeet Bhatia, Kartik Chaudhary

While AI has become an integral part of every organization today, the development of large-scale ML solutions and management of complex ML workflows in production continue to pose challenges for many. Google’s unified data and AI platform, Vertex AI, directly addresses these challenges with its array of MLOPs tools designed for overall workflow management.This book is a comprehensive guide that lets you explore Google Vertex AI’s easy-to-advanced level features for end-to-end ML solution development. Throughout this book, you’ll discover how Vertex AI empowers you by providing essential tools for critical tasks, including data management, model building, large-scale experimentations, metadata logging, model deployments, and monitoring. You’ll learn how to harness the full potential of Vertex AI for developing and deploying no-code, low-code, or fully customized ML solutions. This book takes a hands-on approach to developing u deploying some real-world ML solutions on Google Cloud, leveraging key technologies such as Vision, NLP, generative AI, and recommendation systems. Additionally, this book covers pre-built and turnkey solution offerings as well as guidance on seamlessly integrating them into your ML workflows.By the end of this book, you’ll have the confidence to develop and deploy large-scale production-grade ML solutions using the MLOps tooling and best practices from Google.

The DevOps 2.2 Toolkit. Self-Sufficient Docker Clusters

Viktor Farcic

Building on The DevOps 2.0 Toolkit and The DevOps 2.1 Toolkit: Docker Swarm, Viktor Farcic brings his latest exploration of the Docker technology as he records his journey to explore two new programs, self-adaptive and self-healing systems within Docker. The DevOps 2.2 Toolkit: Self-Sufficient Docker Clusters is the latest book in Viktor Farcic’s series that helps you build a full DevOps Toolkit. This book in the series looks at Docker, the tool designed to make it easier in the creation and running of applications using containers. In this latest entry, Viktor combines theory with a hands-on approach to guide you through the process of creating self-adaptive and self-healing systems. Within this book, Viktor will cover a wide-range of emerging topics, including what exactly self-adaptive and self-healing systems are, how to choose a solution for metrics storage and query, the creation of cluster-wide alerts and what a successful self-sufficient system blueprint looks like. Work with Viktor and dive into the creation of self-adaptive and self-healing systems within Docker.

The Infinite Retina. Spatial Computing, Augmented Reality, and how a collision of new technologies are bringing about the next tech revolution

Irena Cronin, Robert Scoble, Steve Wozniak

What is Spatial Computing and why is everyone from Tesla, Apple, and Facebook investing heavily in it?In The Infinite Retina, authors Irena Cronin and Robert Scoble attempt to answer that question by helping you understand where Spatial Computing?an augmented reality where humans and machines can interact in a physical space?came from, where it's going, and why it's so fundamentally different from the computers or mobile phones that came before.They present seven visions of the future and the industry verticals in which Spatial Computing has the most influence?Transportation; Technology, Media, and Telecommunications; Manufacturing; Retail; Healthcare; Finance; and Education.The book also shares insights about the past, present, and future from leading experts an other industry veterans and innovators, including Sebastian Thrun, Ken Bretschneider, and Hugo Swart. They dive into what they think will happen in Spatial Computing in the near and medium term, and also explore what it could mean for humanity in the long term.The Infinite Retina then leaves it up to you to decide whether Spatial Computing is truly where the future of technology is heading or whether it's just an exciting, but passing, phase.

The Kaggle Book. Data analysis and machine learning for competitive data science

Konrad Banachewicz, Luca Massaron, Anthony Goldbloom

Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career.The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won’t easily find elsewhere, and the knowledge they’ve accumulated along the way. As well as Kaggle-specific tips, you’ll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You’ll design better validation schemes and work more comfortably with different evaluation metrics.Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you.Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!