Mastering Deep Learning Fundamentals With Python
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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.!
Mastering Deep Learning Fundamentals
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Author : Ai Publishing
language : en
Publisher: AI Publishing
Release Date : 2019-06-09
Mastering Deep Learning Fundamentals written by Ai Publishing and has been published by AI Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-09 with categories.
** ONE HOUR FREE VIDEO COURSE IN DEEP LEARNING INCLUDED** **Get your copy now, the price will change soon**You are interested in deep learning, but don't know how to get startedLet us help youWho are the book for? Are a college student and want more than your university course offers Are you a student interested in a career in Data science? Are you a programmer who wants to make a career switch into data science and AI? Are you an engineer who wants to use new data science techniques at your current job? Are you an entrepreneur who dreams of a data science but do not yet know the basics? Are you a hobbyist who wants to build cool data science projects? Are you a data scientist practitioner and want to broaden your area of expertise? If the answer to any of the above questions is a YES, this book is for you.We have designed this book for beginners in mind and our goal is to prepare students with practical skills to solve real-world problems and to stand out in the job market.This book are not for shallow learners who simply want to copy-paste code. This book will require your time and commitment.Our book is different from other books?If you are searching for a step by step guide to learn deep learning and AI from scratch or are interested in the current updates of the AI world, our book is just the right one for you. This book paves beginners' road towards the path of deep learning concepts and algorithms without any traditional complexity of mathematical formulas.With the help of graphs and images, this books is the easiest to comprehend even by those who have no previous technological knowledge of machine learning. Moreover, our book, with its comprehensive content, prepares the readers for higher advanced courses.We strive to provide world-class data science and AI education at reasonable prices. To achieve that, we have put in a lot of planning and efforts to provide a rich learning experience for the students.What's Inside This Book? Part I: 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 Deep Learning Loss Functions Deep Learning Optimization Algorithms Convolutional Neural Network Recurrent Neural Networks LSTM Recursive Neural Networks Bonus Course Conclusion Part II: Deep Learning in Practice (In Jupyter notebooks) Python for Beginners Python Data Structures Python Function Object Oriented Programming in Python Best practices in Python and Zen of Python Installing Python Numpy, Pandas, Matplotlib and Scikit-learn Evaluating a model's performance Keras and Tensorflow Deep learning workstation: Jupyter Notebooks and Getting Binary Classification Building Deep Learning Model Convolutional Neural Networks in Keras Data Preparation Model Building Training and Testing Deep learning for text and sequences Brief introduction to Google Colab Data Preparation Data Wrangling and Analysis Recurrent Neural Network (RNN) ** MONEY BACK GUARANTEE BY AMAZON **If you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform or contact us (our email inside the book).
Mastering Ai And Machine Learning With Python
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Author : Anshuman Mishra
language : en
Publisher: Independently Published
Release Date : 2025-05-12
Mastering Ai And Machine Learning With Python written by Anshuman Mishra and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-12 with Computers categories.
This ambitious two-volume work, "Mastering AI and Machine Learning with Python: From Fundamentals to Advanced Deep Learning," aims to be a definitive guide for anyone seeking to understand, implement, and master the intricate world of Artificial Intelligence (AI) and Machine Learning (ML) using the versatile Python programming language. Spanning a projected 10,000 words across both volumes (with Volume 1 detailed below), this book meticulously progresses from foundational concepts to cutting-edge deep learning techniques, providing readers with a robust theoretical understanding coupled with practical implementation skills. Volume 1: Foundations and Core Machine Learning Techniques Volume 1 lays the essential groundwork for embarking on the journey of AI and ML. It is structured to take individuals with varying levels of prior knowledge - from complete beginners to those with some programming experience - and equip them with the core competencies required to understand and apply fundamental machine learning algorithms. Chapter 1: Introduction to AI and Machine Learning This introductory chapter serves as a compass, orienting the reader within the broad landscape of AI and its subfields. It begins by clearly delineating the concepts of Artificial Intelligence, Machine Learning, and Deep Learning, highlighting their relationships and distinctions. Understanding AI, Machine Learning, and Deep Learning: This section meticulously unpacks these often-interchangeable terms. It defines AI as the overarching field focused on creating intelligent agents capable of performing tasks that typically require human intelligence. Machine Learning is then presented as a subset of AI, where systems learn from data without being explicitly programmed. Finally, Deep Learning is introduced as a subfield of ML that utilizes artificial neural networks with multiple layers (deep neural networks) to extract complex patterns from large datasets. The chapter will use analogies and real-world examples to solidify these definitions, ensuring a clear understanding of the hierarchy and unique characteristics of each field. Real-World Applications of AI: To underscore the practical relevance and transformative power of AI, this section delves into a diverse range of real-world applications. It will explore how AI is revolutionizing industries such as healthcare (diagnosis, drug discovery), finance (fraud detection, algorithmic trading), transportation (autonomous vehicles), entertainment (recommendation systems), manufacturing (predictive maintenance), and customer service (chatbots). Each application will be briefly described, highlighting the specific AI techniques employed and the tangible benefits realized. This section aims to inspire the reader and contextualize the learning journey ahead. The Role of Python in AI Development: This crucial segment emphasizes why Python has emerged as the lingua franca of AI and ML. It will discuss Python's key advantages, including its clear and concise syntax, extensive ecosystem of powerful libraries (such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch), large and active community support, and its versatility for various stages of the AI development lifecycle - from data preprocessing to model deployment. The chapter will briefly introduce some of these key libraries, setting the stage for their detailed exploration in subsequent chapters. Overview of TensorFlow and PyTorch: As two of the most prominent deep learning frameworks, TensorFlow and PyTorch are introduced in this section. The chapter will provide a high-level overview of their functionalities, key features, and their respective strengths and weaknesses. It will touch upon their roles in building and training neural networks, their support for hardware acceleration (GPUs), and their growing adoption in both research and industry.
Mastering Machine Learning With Python In Six Steps
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Author : Manohar Swamynathan
language : en
Publisher: Apress
Release Date : 2017-06-07
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 2017-06-07 with Computers categories.
Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. You’ll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you’ll explore advanced text mining techniques, neural networks and deep 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 Examine the fundamentals of Python programming language Review machine Learning history and evolution Understand machine learning system development frameworks Implement supervised/unsupervised/reinforcement learning techniques with examples Explore fundamental to advanced text mining techniques Implement various deep learning frameworks Who This Book Is For Python developers or data engineers looking to expand their knowledge or career into machine learning area. Non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python. Novice machine learning practitioners looking to learn advanced topics, such as hyperparameter tuning, various ensemble techniques, natural language processing (NLP), deep learning, and basics of reinforcement learning.
Generative Deep Learning With Python
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Author : Cuantum Technologies LLC
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-06-12
Generative Deep Learning With Python written by Cuantum Technologies LLC 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 2024-06-12 with Computers categories.
Dive into the world of Generative Deep Learning with Python, mastering GANs, VAEs, & autoregressive models through projects & advanced topics. Gain practical skills & theoretical knowledge to create groundbreaking AI applications. Key Features Comprehensive coverage of deep learning and generative models. In-depth exploration of GANs, VAEs, & autoregressive models & advanced topics in generative AI. Practical coding exercises & interactive assignments to build your own generative models. Book DescriptionGenerative Deep Learning with Python opens the door to the fascinating world of AI where machines create. This course begins with an introduction to deep learning, establishing the essential concepts and techniques. You will then delve into generative models, exploring their theoretical foundations and practical applications. As you progress, you will gain a deep understanding of Generative Adversarial Networks (GANs), learning how they function and how to implement them for tasks like face generation. The course's hands-on projects, such as creating GANs for face generation and using Variational Autoencoders (VAEs) for handwritten digit generation, provide practical experience that reinforces your learning. You'll also explore autoregressive models for text generation, allowing you to see the versatility of generative models across different types of data. Advanced topics will prepare you for cutting-edge developments in the field. Throughout your journey, you will gain insights into the future landscape of generative deep learning, equipping you with the skills to innovate and lead in this rapidly evolving field. By the end of the course, you will have a solid foundation in generative deep learning and be ready to apply these techniques to real-world challenges, driving advancements in AI and machine learning.What you will learn Develop a detailed understanding of deep learning fundamentals Implement and train Generative Adversarial Networks (GANs) Create & utilize Variational Autoencoders for data generation Apply autoregressive models for text generation Explore advanced topics & stay ahead in the field of generative AI Analyze and optimize the performance of generative models Who this book is for This course is designed for technical professionals, data scientists, and AI enthusiasts who have a foundational understanding of deep learning and Python programming. It is ideal for those looking to deepen their expertise in generative models and apply these techniques to innovative projects. Prior experience with neural networks and machine learning concepts is recommended to maximize the learning experience. Additionally, research professionals and advanced practitioners in AI seeking to explore generative deep learning applications will find this course highly beneficial.
Python Programming And Machine Learning
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Author : John S Code
language : en
Publisher:
Release Date : 2020-04-24
Python Programming And Machine Learning written by John S Code and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-24 with categories.
Are you new to machine learning? Do you want to learn how to do machine learning with Python? Have you been thinking of learning Python as your first programming language?Artificial intelligent, Data analysis, Coding languages are subjects you need to start a super career today. The use of machine learning offers incredible opportunities!This ultimate book will give you the opportunity to understand coding languages and analysing big data to help the decision makers into meaningful information.Why with Python? Because Python is a powerful interpreted language and the best programming language to start with.Python is a complete language and platform where you can apply both research and development production. This book includes: Python Programming for Beginners This book can be your easy guide to understand coding language, Python programming, and data analysis with tricks and tools. It comes with 11 chapters that will teach you about python programming. Python Machine Learning It can be your essential book to know about artificial intelligence, neural network, mastering, and deep learning about the fundamentals of ML with Python. It consists of 12 chapters that will help you hone your skills and knowledge about machine learning. Improve your coding skills starting with an easy guide and master the fundamentals of machine learning with Python. You do not need any experience to change your career, just learn this book. So, what are you waiting for? Purchase yours today!
Deep Learning Fundamentals With Python
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Author : Jaden Cross
language : en
Publisher: Independently Published
Release Date : 2024-12-11
Deep Learning Fundamentals With Python written by Jaden Cross and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-11 with Computers categories.
"Deep Learning Fundamentals with Python" is your essential guide to understanding the powerful techniques behind artificial intelligence and machine learning. This book explains deep learning in a simple, approachable way, focusing on how neural networks mimic the human brain to make sense of vast amounts of data. You'll explore the core concepts behind deep neural networks, backpropagation, and the remarkable ability of deep learning systems to recognize patterns and make decisions autonomously. Whether you're a beginner or looking to refine your knowledge, this book offers hands-on examples and practical exercises using Python to help you master the art of building deep learning models. Start your journey to building intelligent systems that are reshaping industries like never before.
Machine Learning
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Author : Samuel Hack
language : en
Publisher:
Release Date : 2021-01-07
Machine Learning written by Samuel Hack and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-07 with Computers categories.
Master the world of Python and Machine Learning with this incredible 4-in-1 bundle. Are you interested in becoming a Python pro?Do you want to learn more about the incredible world of machine learning, and what it can do for you? Then keep reading. Created with the beginner in mind, this powerful bundle delves into the fundamentals behind Python and Machine Learning, from basic code and mathematical formulas to complex neural networks and ensemble modeling. Inside, you'll discover everything you need to know to get started with Python and Machine Learning, and begin your journey to success! In book one - MACHINE LEARNING FOR BEGINNERS, you'll learn: What is Artificial Intelligence Really, and Why is it So Powerful? Choosing the Right Kind of Machine Learning Model for You An Introduction to Statistics Reinforcement Learning and Ensemble Modeling "Random Forests" and Decision Trees In book two - MACHINE LEARNING MATHEMATICS, you will: Learn the Fundamental Concepts of Machine Learning Algorithms Understand The Four Fundamental Types of Machine Learning Algorithm Master the Concept of "Statistical Learning" Learn Everything You Need to Know about Neural Networks and Data Pipelines Master the Concept of "General Setting of Learning" In book three - LEARNING PYTHON, you'll discover: How to Install, Run, and Understand Python on Any Operating System A Comprehensive Introduction to Python Python Basics and Writing Code Writing Loops, Conditional Statements, Exceptions and More Python Expressions and The Beauty of Inheritances And in book four - PYTHON MACHINE LEARNING, you will: Learn the Fundamentals of Machine Learning Master the Nuances of 12 of the Most Popular and Widely-Used Machine Learning Algorithms Become Familiar with Data Science Technology Dive Into the Functioning of Scikit-Learn Library and Develop Machine Learning Models Uncover the Secrets of the Most Critical Aspect of Developing a Machine Learning Model - Data Pre-Processing and Training/Testing Subsets Whether you're a complete beginner or a programmer looking to improve your skillset, this bundle is your all-in-one solution to mastering the world of Python and Machine Learning. So don't wait - it's never been easier to learn. Buy Now to Become a Master of Python and Machine Learning Today!
Deep Learning With Python
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Author : GREYSON. CHESTERFIELD
language : en
Publisher: Independently Published
Release Date : 2025-03-16
Deep Learning With Python written by GREYSON. CHESTERFIELD and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-16 with Computers categories.
Deep Learning with Python: Build Neural Networks and AI Models from Scratch is a comprehensive, hands-on guide to mastering deep learning and neural network training using Python. Whether you're a beginner looking to dive into AI or an experienced practitioner seeking to improve your skills, this book will walk you through the concepts and tools needed to build deep learning models from scratch. Focusing on frameworks like TensorFlow and PyTorch, this book provides practical insights into developing, training, and deploying powerful neural networks. With clear explanations, step-by-step instructions, and real-world examples, you'll learn how to implement advanced AI models that can be applied to a wide range of problems in industries such as healthcare, finance, and more. Inside, you'll discover: Introduction to Deep Learning and Neural Networks: Learn the fundamentals of deep learning, neural networks, and the key components involved in building AI models. Understand the differences between shallow learning and deep learning, and the advantages of using deep neural networks for complex tasks. Setting Up Python for Deep Learning: Get started with the necessary tools and libraries, including TensorFlow, PyTorch, and Keras. Learn how to install and configure the tools, and understand the basics of Python for machine learning and deep learning. Building Your First Neural Network: Learn how to design and implement a simple feedforward neural network using TensorFlow and PyTorch. Discover how to train your network using backpropagation and gradient descent techniques. Activation Functions and Optimization: Explore the role of activation functions like ReLU, Sigmoid, and Tanh in neural networks, and learn how to optimize your models with techniques such as stochastic gradient descent, Adam, and more. Convolutional Neural Networks (CNNs): Dive into CNNs and learn how they are used for image recognition and computer vision tasks. Implement a CNN for tasks like object detection and image classification using TensorFlow and PyTorch. Recurrent Neural Networks (RNNs) and LSTMs: Understand how RNNs and Long Short-Term Memory (LSTM) networks are used for sequence data, such as time series forecasting and natural language processing. Learn how to implement and train these models for tasks like sentiment analysis and speech recognition. Transfer Learning and Pre-trained Models: Discover the power of transfer learning and how to leverage pre-trained models to build deep learning applications with less data and faster training times. Learn how to fine-tune models like VGG16, ResNet, and BERT for your specific needs. Regularization and Avoiding Overfitting: Learn techniques like dropout, batch normalization, and early stopping to prevent overfitting in your models. Understand how to improve the generalization of your neural networks for real-world applications. Model Evaluation and Fine-Tuning: Master the art of model evaluation using metrics like accuracy, precision, recall, and F1-score. Learn how to tune hyperparameters and optimize your deep learning models for better performance. Deploying Deep Learning Models: Learn how to deploy your trained deep learning models into production environments. Explore techniques for model saving, serving, and using cloud platforms like AWS and Google Cloud for model deployment. Practical Applications of Deep Learning: Gain hands-on experience with real-world deep learning applications, including image classification, sentiment analysis, stock price prediction, and healthcare diagnostics. By the end of this book, you'll have the skills to build and train complex neural networks and AI models from scratch. You'll be ready to apply deep learning to solve real-world problems and explore new AI possibilities.
Python Programming And Machine Learning
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Author : JOHN S. CODE
language : en
Publisher:
Release Date : 2020
Python Programming And Machine Learning written by JOHN S. CODE and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.