Ultimate Step By Step Guide To Deep Learning Using Python
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Ultimate Step By Step Guide To Deep Learning Using Python
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Author : Daneyal Anis
language : en
Publisher:
Release Date : 2020-07-19
Ultimate Step By Step Guide To Deep Learning Using Python written by Daneyal Anis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-19 with categories.
*Start your Data Science career using Python today!*Are you ready to start your new exciting career? Ready to master artificial intelligence and deep learning concepts?Are you overwhelmed with complexity of the books on this subject?Then let this breezy and fun little book on Python, Machine Learning and Deep Learning models make you a Data Scientist in 7 days!This book continues from where the first book in the series, Ultimate Step by Step Guide to Machine Learning Using Python, left of. In the first book you were introduced to Python concepts such as: -Data Structures like Pandas -Foundational libraries like Numpy, Seaborn and Scikit-Learn-Regression analysis-Classification-Clustering-Association Learning-Dimension ReductionThis book builds on those concepts to expand on Machine Learning algorithms like: -Linear and Logistical regression-Decision tree-Support vector machines (SVM)After that, this book takes you on a journey into Deep Learning and Neural Networks with important concepts and libraries like: -Convolutional and Recurrent Neural Networks-TensorFlow-Keras-PyTorch-Keras-Apache MXNet-Microsoft Cognitive Toolkit (CNTK)The final part of the book covers all foundational concepts that are required for Amazon Web Services (AWS) Certified Machine Learning Specialization by explaining how to deploy your models at scale on Cloud technologies. While AWS is used in the book for illustrative purposes, Microsoft Azure and Google Cloud are also introduced as alternative cloud technologies. After reading this book you will be able to: -Code in Python with confidence-Build new machine learning and deep learning models from scratch-Know how to clean and prepare your data for analytics-Speak confidently about statistical analysis techniquesData Science was ranked the fast-growing field by LinkedIn and Data Scientist is one of the most highly sought after and lucrative careers in the world!If you are on the fence about making the leap to a new and lucrative career, this is the book for you!What sets this book apart from other books on the topic of Python and Machine learning: -Step by step code examples and explanation-Complex concepts explained visually-Real world applicability of the machine learning and deep learning models introducedWhat do I need to get started?You will have a step by step action plan in place once you finish this book and finally feel that you, can master data science and artificial intelligence and start a lucrative and rewarding career! Ready to dive in to the exciting world of Python and Deep Learning?Then scroll up to the top and hit that BUY BUTTON!
Hacker S Guide To Machine Learning Concepts
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Author : Trilokesh Khatri
language : en
Publisher: Educohack Press
Release Date : 2025-01-03
Hacker S Guide To Machine Learning Concepts written by Trilokesh Khatri and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.
Hacker’s Guide to Machine Learning Concepts is crafted for those eager to dive into the world of ethical hacking. This book demonstrates how ethical hacking can help companies identify and fix vulnerabilities efficiently. With the rise of data and the evolving IT industry, the scope of ethical hacking continues to expand. We cover various hacking techniques, identifying weak points in programs, and how to address them. The book is accessible even to beginners, offering chapters on machine learning and programming in Python. Written in an easy-to-understand manner, it allows learners to practice hacking steps independently on Linux or Windows systems using tools like Netsparker. This book equips you with fundamental and intermediate knowledge about hacking, making it an invaluable resource for learners.
Ultimate Step By Step Guide To Machine Learning Using Python
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Author : Daneyal Anis
language : en
Publisher:
Release Date : 2020-02-17
Ultimate Step By Step Guide To Machine Learning Using Python written by Daneyal Anis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-17 with categories.
*Start your Data Science career using Python today!* Are you ready to start your new exciting career? Ready to crush your machine learning career goals? Are you overwhelmed with complexity of the books on this subject?Then let this breezy and fun little book on Python and machine learning models make you a data scientist in 7 days! First part of this book introduces Python basics including: 1) Data Structures like Pandas 2) Foundational libraries like Numpy, Seaborn and Scikit-Learn Second part of this book shows you how to build predictive machine learning models step by step using techniques such as: 1) Regression analysis 2) Decision tree analysis 3) Training and testing data models 4) And much more! After reading this book you will be able to: 1) Code in Python with confidence 2) Build new machine learning models from scratch 3) Know how to clean and prepare your data for analytics 4) Speak confidently about statistical analysis techniques Data Science was ranked the fast-growing field by LinkedIn and Data Scientist is one of the most highly sought after and lucrative careers in the world! If you are on the fence about making the leap to a new and lucrative career, this is the book for you! What sets this book apart from other books on the topic of Python and Machine learning: 1) Step by step code examples and explanation 2) Complex concepts explained visually 3) Real world applicability of the machine learning models introduced 4) Bonus free code samples that you can try yourself without any prior experience in Python! What do I need to get started? You will have a step by step action plan in place once you finish this book and finally feel that you, can master data science and machine learning and start lucrative and rewarding career! Ready to dive in to the exciting world of Python and Machine Learning? Then scroll up to the top and hit that BUY BUTTON!
Mastering Deep Learning Fundamentals With Python
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Author : Richard Wilson
language : en
Publisher: Independently Published
Release Date : 2019-07-14
Mastering Deep Learning Fundamentals With Python written by Richard Wilson and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-14 with categories.
★★Buy the Paperback Version of this Book and get the Kindle Book version for FREE ★★ Step into the fascinating world of data science.. You to participate in the revolution that brings artificial intelligence back to the heart of our society, thanks to data scientists. Data science consists in translating problems of any other nature into quantitative modeling problems, solved by processing algorithms. This book, designed for anyone wishing to learn Deep Learning. This book presents the main techniques: deep neural networks, able to model all kinds of data, convolution networks, able to classify images, segment them and discover the objects or people who are there, recurring networks, it contains sample code so that the reader can easily test and run the programs. On the program: Deep learning Neural Networks and Deep Learning Deep Learning Parameters and Hyper-parameters Deep Neural Networks Layers Deep Learning Activation Functions Convolutional Neural Network Python Data Structures Best practices in Python and Zen of Python Installing Python Python These are some of the topics covered in this book: fundamentals of deep learning fundamentals of probability fundamentals of statistics fundamentals of linear algebra introduction to machine learning and deep learning fundamentals of machine learning fundamentals of neural networks and deep learning deep learning parameters and hyper-parameters deep neural networks layers deep learning activation functions convolutional neural network Deep learning in practice (in jupyter notebooks) python data structures best practices in python and zen of python installing python The following are the objectives of this book: To help you understand deep learning in detail To help you know how to get started with deep learning in Python by setting up the coding environment. To help you transition from a deep learning Beginner to a Professional. To help you learn how to develop a complete and functional artificial neural network model in Python on your own. And more Get this book now to learn more about -- Deep learning in Python by setting up the coding environment.!
Python Made Easy Your Step By Step Guide To Learning Python
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Author : Ayman Elmassarawy
language : en
Publisher: Ayman Elmassarawy
Release Date : 2025-02-08
Python Made Easy Your Step By Step Guide To Learning Python written by Ayman Elmassarawy and has been published by Ayman Elmassarawy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-08 with Computers categories.
Python has become one of the most widely used and versatile programming languages, known for its simplicity, readability, and power. "Python Made Easy: Your Step-by-Step Guide to Learning Python" is designed to help absolute beginners and aspiring programmers build a solid foundation in Python programming, guiding them from fundamental concepts to real-world applications. This book provides a structured, hands-on approach, breaking down complex topics into clear and digestible lessons. It introduces key programming concepts such as data types, variables, control flow, functions, object-oriented programming, file handling, and working with external libraries. With practical examples, coding exercises, and case studies, readers will gain experience in writing efficient and error-free Python programs. Beyond the basics, this book also covers advanced topics such as debugging techniques, automation, data handling, and command-line arguments, ensuring readers develop a deeper understanding of Python's capabilities. Whether you are interested in automation, web development, data science, or software engineering, this guide equips you with the tools to start coding with confidence. By the end of this book, readers will have not only learned Python syntax and best practices but also developed problem-solving skills essential for real-world programming. With Python Made Easy, learning to code has never been more accessible or engaging. Many beginners find programming intimidating, but Python Made Easy simplifies the learning process with: ✅ Step-by-Step Explanations – Each chapter builds on the previous one, ensuring a smooth learning curve. ✅ Hands-On Exercises – Practical coding exercises help reinforce key concepts. ✅ Real-World Applications – Learn how Python is used in various industries. ✅ Clear and Concise Explanations – Technical concepts are broken down into simple, digestible lessons. ✅ Troubleshooting Tips – Common errors and their solutions are covered throughout the book. Whether you want to automate tasks, build web applications, analyze data, or simply understand how coding works, this book provides the foundational knowledge you need. What You Will Learn: This book is designed to be a complete learning guide for Python beginners. Below is an overview of the topics covered: Introduction to Python and why it is widely used. Chapter 2: Python Basics Chapter 3: Control Flow and Loops Chapter 4: Functions and Modules Chapter 5: Data Structures Chapter 6: Object-Oriented Programming (OOP) Chapter 7: File Handling and Working with Data Chapter 8: Error Handling and Debugging Chapter 9: Working with External Libraries Chapter 10: Building Real-World Python Projects Chapter 11: Next Steps in Python How to Use This Book: This book is structured to be beginner-friendly, but also useful for those with some programming background. You can follow it from start to finish or jump to specific chapters that interest you.
Deep Learning With Python
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Author : Francois Chollet
language : en
Publisher: Simon and Schuster
Release Date : 2017-11-30
Deep Learning With Python written by Francois Chollet and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-30 with Computers categories.
Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. 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. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Mastering Machine Learning With Python In Six Steps
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Author : Manohar Swamynathan
language : en
Publisher: Apress
Release Date : 2019-10-01
Mastering Machine Learning With Python In Six Steps written by Manohar Swamynathan and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-01 with Computers categories.
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages. You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage. What You'll Learn Understand machine learning development and frameworks Assess model diagnosis and tuning in machine learning Examine text mining, natuarl language processing (NLP), and recommender systems Review reinforcement learning and CNN Who This Book Is For Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
The Deep Learning Architect S Handbook
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Author : Ee Kin Chin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-12-29
The Deep Learning Architect S Handbook written by Ee Kin Chin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-29 with Computers categories.
Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle Key Features Interpret your models’ decision-making process, ensuring transparency and trust in your AI-powered solutions Gain hands-on experience in every step of the deep learning life cycle Explore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerations Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDeep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives. This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency. As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications. By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.What you will learn Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs) Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model Deal with multi-modal data drift in a production environment Evaluate the quality and bias of your models Explore techniques to protect your model from adversarial attacks Get to grips with deploying a model with DataRobot AutoML Who this book is for This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.
Machine Learning For Beginners 2025 Step By Step Guide To Master Ml Algorithms Real World Applications
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Author : J. Paaul
language : en
Publisher: Code Academy
Release Date : 2025-05-07
Machine Learning For Beginners 2025 Step By Step Guide To Master Ml Algorithms Real World Applications written by J. Paaul and has been published by Code Academy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-07 with Computers categories.
Machine Learning for Beginners 2025 is the perfect guide for anyone looking to dive into the world of machine learning. This book breaks down complex concepts into easy-to-understand explanations and hands-on examples. Covering the fundamentals of ML algorithms, data preprocessing, model evaluation, and real-world applications, this book is ideal for newcomers to the field. With practical projects and step-by-step tutorials, readers will gain the skills to implement machine learning models using Python and popular libraries like Scikit-learn and TensorFlow, making this a comprehensive resource for aspiring data scientists.
Mastering Probabilistic Graphical Models Using Python
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Author : Ankur Ankan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-08-03
Mastering Probabilistic Graphical Models Using Python written by Ankur Ankan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-03 with Computers categories.
Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python About This Book Gain in-depth knowledge of Probabilistic Graphical Models Model time-series problems using Dynamic Bayesian Networks A practical guide to help you apply PGMs to real-world problems Who This Book Is For If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian Learning or Probabilistic Graphical Models, this book will help you to understand the details of Graphical Models and use it in your data science problems. This book will also help you select the appropriate model as well as the appropriate algorithm for your problem. What You Will Learn Get to know the basics of Probability theory and Graph Theory Work with Markov Networks Implement Bayesian Networks Exact Inference Techniques in Graphical Models such as the Variable Elimination Algorithm Understand approximate Inference Techniques in Graphical Models such as Message Passing Algorithms Sample algorithms in Graphical Models Grasp details of Naive Bayes with real-world examples Deploy PGMs using various libraries in Python Gain working details of Hidden Markov Models with real-world examples In Detail Probabilistic Graphical Models is a technique in machine learning that uses the concepts of graph theory to compactly represent and optimally predict values in our data problems. In real world problems, it's often difficult to select the appropriate graphical model as well as the appropriate inference algorithm, which can make a huge difference in computation time and accuracy. Thus, it is crucial to know the working details of these algorithms. This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also to run different inference algorithms on them. There is a complete chapter devoted to the most widely used networks Naive Bayes Model and Hidden Markov Models (HMMs). These models have been thoroughly discussed using real-world examples. Style and approach An easy-to-follow guide to help you understand Probabilistic Graphical Models using simple examples and numerous code examples, with an emphasis on more widely used models.