{"title":"Ahmed Fawzy Gad","description":null,"products":[{"product_id":"practical-computer-vision-applications-using-deep-learning-with-cnns-book-ahmed-fawzy-gad-9781484241660","title":"Practical Computer Vision Applications Using Deep Learning with CNNs","description":"Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms.   For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model. After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads. This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production.   What You Will Learn    Understand how ANNs and CNNs work  Create computer vision applications and CNNs from scratch using Python Follow a deep learning project from conception to production using TensorFlow Use NumPy with Kivy to build cross-platform data science applications   Who This Book Is ForData scientists, machine learning and deep learning engineers, software developers.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ GARDNERS","offer_id":49734069256465,"sku":"NGR9781484241660","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ GOOD \/ SBYB","offer_id":50022842761489,"sku":"CIN1484241665G","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52593065820433,"sku":"NLS9781484241660","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ VERY_GOOD \/ INTERNAL","offer_id":52631475454225,"sku":"GOR014570790","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":52748959482129,"sku":"NIN9781484241660","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1484241665.jpg?v=1751085514"},{"product_id":"introduction-to-deep-learning-and-neural-networks-with-python-tm-book-ahmed-fawzy-gad-9780323909334","title":"Introduction to Deep Learning and Neural Networks with Python (TM)","description":"Introduction to Deep Learning and Neural Networks with Python™: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and Python™ code examples to clarify neural network calculations, by book’s end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and Python™ examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52680977547537,"sku":"NLS9780323909334","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9780323909334.jpg?v=1762314439"},{"product_id":"transformers-and-large-language-models-book-ahmed-fawzy-gad-9798868827846","title":"Transformers and Large Language Models","description":"This book is a hands-on guide to understanding the foundations, architectures, and real-world applications of transformers and large language models in modern AI.   The book begins by laying the foundations of generative AI architectures,  tokenization, encoding, and classical modeling techniques. Initial chapters address the evolution from feed-forward networks and recurrent neural networks to long short-term memory (LSTM), setting the stage for the revolutionary transformer architecture. The core of the book focuses on transformers, introducing the encoder-decoder framework, attention mechanisms, positional encodings, and the internal workings of multi-head attention, normalization, and multi-layer perceptrons. Readers gain insight into advanced techniques such as rotary positional embeddings (RoPE), mixture of experts (MoE), and knowledge distillation, alongside practical training strategies like self-supervised learning, fine-tuning, and reinforcement learning with human feedback. Popular models from OpenAI, DeepSeek, and other vendors are examined to highlight the evolution of the LLM landscape. Building on these foundations, the text explores methods for model customization, including parameter-efficient fine-tuning (LoRA, adapters), text generation strategies, prompt engineering, and quantization. Retrieval-Augmented Generation (RAG) is introduced as a critical innovation for grounding LLMs in external knowledge, with detailed evaluation techniques for retrieval and generation. Finally, the book ventures into Agentic AI, demonstrating protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A) interactions with practical coding examples.   In conclusion, this book serves as both a practical guide, equipping readers with the technical depth and applied strategies needed to design, fine-tune, and deploy cutting-edge transformers and large language models for real-world applications.   What we will learn:   Ø  Understand the foundations of AI, ML pipelines, tokenization, encoding, and early neural architectures.   Ø  Explore transformers in depth—encoder-decoder design, attention mechanisms, and advanced embedding methods.   Ø  Learn modern LLM advancements like RoPE, MoE, SLMs, fine-tuning strategies, and evaluation techniques.   Ø  Master practical customization through prompt engineering, PEFT methods, quantization, and text generation.   nWho this book is for:   Data scientists, ML engineers, AI researchers, and developers exploring Transformers and large language models.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":53728360726801,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":53728360792337,"sku":"NGR9798868827846","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9798868827846.jpg?v=1782992859"}],"url":"https:\/\/www.worldofbooks.com\/en-au\/collections\/author-books-by-ahmed-fawzy-gad.oembed","provider":"World of Books ","version":"1.0","type":"link"}