Building A Neural Network From Scratch Using Python
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Building Neural Networks from Scratch with Python
Ready to throw your hat into the AI and machine learning ring? Get started right here, right now! Are you sick of these machine-learning guides that don't really teach you anything? Do you already know Python, but you're looking to expand your horizons and skills with the language? Do you want to dive into the amazing world of neural networks, but it just seems like it's... not for you? Artificial intelligence is progressing at a fantastic rate-every day, a new innovation hits the net, providing more and more opportunities for the advancement of society. In your everyday life, your job, and even in your passion projects, learning how to code a neural network can be game-changing. But it just seems... complicated. How do you learn everything that goes into such a complex topic without wanting to tear your own hair out? Well, it just got easier. Machine learning and neural networking don't have to be complicated-with the right resources, you can successfully code your very own neural network from scratch, minimal experience needed! In this all-encompassing guide to coding neural networks in Python, you'll uncover everything you need to go from zero to hero-transforming how you code and the scope of your knowledge right before your eyes. Here's just a portion of what you will discover in this guide: ● A comprehensive look at what a neural network is - including why you would use one and the benefits of including them in your repertoire ● All that pesky math dissuading you? Get right to the meat and potatoes of coding without all of those confusing equations getting you down ● Become a debugging master with these tips for handling code problems, maximizing your efficiency as a coder, and testing the data within your code ● Technological advancements galore! Learn how to keep up with all the latest trends in tech-and why doing so is important to you ● What in the world are layers and gradients? Detailed explanations of complex topics that will demystify neural networks, once and for all ● Dealing with underfitting, overfitting, and other oversights that many other resources overlook ● Several beginner-friendly neural network projects to put your newfound knowledge to the test And much more. Imagine a world where machine learning is more accessible, where neural networks and other complex topics are available to people just like you-people with a passion. Allowing for such technological advancements is going to truly change our world. It might seem hard, and you might be concerned based on other resources you've browsed-but this isn't an opportunity you can pass up on! By the end of this book, you'll have mastered neural networks confidently!
Ultimate Neural Network Programming with Python: Create Powerful Modern AI Systems by Harnessing Neural Networks with Python, Keras, and TensorFlow
Author: Vishal Rajput
language: en
Publisher: Orange Education Pvt Limited
Release Date: 2023-11-04
Master Neural Networks for Building Modern AI Systems. Key Features ● Comprehensive Coverage of Foundational AI Concepts and Theories. ● In-Depth Exploration of Maths Behind Neural Network Mathematics. ● Effective Strategies for Structuring Deep Learning Code. ● Real-world applications of AI Principles and Techniques. Book Description This book is a practical guide to the world of Artificial Intelligence (AI), unraveling the math and principles behind applications like Google Maps and Amazon. The book starts with an introduction to Python and AI, demystifies complex AI math, teaches you to implement AI concepts, and explores high-level AI libraries. Throughout the chapters, readers are engaged with the book through practice exercises and supplementary learning. The book then gradually moves to Neural Networks with Python before diving into constructing ANN models and real-world AI applications. It accommodates various learning styles, letting readers focus on hands-on implementation or mathematical understanding. This book isn't just about using AI tools; it's a compass in the world of AI resources, empowering readers to modify and create tools for complex AI systems. It ensures a journey of exploration, experimentation, and proficiency in AI, equipping readers with the skills needed to excel in the AI industry. What you will learn ● Leverage TensorFlow and Keras while building the foundation for creating AI pipelines. ● Explore advanced AI concepts, including dimensionality reduction, unsupervised learning, and optimization techniques. ● Master the intricacies of neural network construction from the ground up. ● Dive deeper into neural network development, covering derivatives, backpropagation, and optimization strategies. ● Harness the power of high-level AI libraries to develop production-ready code, allowing you to accelerate the development of AI applications. ● Stay up-to-date with the latest breakthroughs and advancements in the dynamic field of artificial intelligence. Who is this book for?This book serves as an ideal guide for software engineers eager to explore AI, offering a detailed exploration and practical application of AI concepts using Python. AI researchers will find this book enlightening, providing clear insights into the mathematical concepts underlying AI algorithms and aiding in writing production-level code. This book is designed to enhance your skills and knowledge to create sophisticated, AI-powered solutions and advance in the multifaceted field of AI. Table of Contents 1. Understanding AI History 2. Setting up Python Workflow for AI Development 3. Python Libraries for Data Scientists 4. Foundational Concepts for Effective Neural Network Training 5. Dimensionality Reduction, Unsupervised Learning and Optimizations 6. Building Deep Neural Networks from Scratch 7. Derivatives, Backpropagation, and Optimizers 8. Understanding Convolution and CNN Architectures 9. Understanding Basics of TensorFlow and Keras 10. Building End-to-end Image Segmentation Pipeline 11. Latest Advancements in AI Index
Neural Network Projects with Python
Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key FeaturesDiscover neural network architectures (like CNN and LSTM) that are driving recent advancements in AIBuild expert neural networks in Python using popular libraries such as KerasIncludes projects such as object detection, face identification, sentiment analysis, and moreBook Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio. What you will learnLearn various neural network architectures and its advancements in AIMaster deep learning in Python by building and training neural networkMaster neural networks for regression and classificationDiscover convolutional neural networks for image recognitionLearn sentiment analysis on textual data using Long Short-Term MemoryBuild and train a highly accurate facial recognition security systemWho this book is for This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.