{"product_id":"138401","title":"Deep learning through self-study ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/uOonoiy99XKFLhgUvoC2v0wy6lL.png?v=1765062881\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Deep learning through self-study \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/146006911\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003e“If you’re learning deep learning and wondering where you can apply it?”\u003cbr\u003e Have fun building your skills by implementing various deep learning models yourself, such as classifying puppy photos, analyzing the sentiment of movie review text, and creating a GPT model!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e From early computer vision models representing the field of deep learning to cutting-edge models like large-scale language models like GPT, Llama, and Gemma, experience the fascinating evolution of artificial intelligence and the latest technologies by implementing various deep learning models.\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents. \u003cbr\u003e\u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eChapter 01.\u003cbr\u003e Classifying Fashion Product Images with Convolutional Neural Networks (CNNs)\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 01-1.\u003cbr\u003e Setting up a deep learning development environment\u003cbr\u003e __Preparing for Deep Learning, Google Colab\u003cbr\u003e __Colab's screen composition\u003cbr\u003e __Preparing for the lab with Colab\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e 01-2.\u003cbr\u003e Understanding Convolutional Neural Network (CNN) Models\u003cbr\u003e __The first CNN model - LeNet\u003cbr\u003e __Convolutional layer - Conv2D\u003cbr\u003e __Pooling layer and dense layer - AveragePooling2D, Dense\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e 01-3.\u003cbr\u003e Classifying Fashion Product Images\u003cbr\u003e __Building the LeNet model\u003cbr\u003e Training the __LeNet model\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 02. Classifying Dog and Cat Photos with a Pre-Trained CNN Model\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 02-1.\u003cbr\u003e Building an Image Classification CNN Model\u003cbr\u003e The First CNN Model to Win the ImageNet Competition - AlexNet\u003cbr\u003e __Pre-trained CNN model - VGGNet\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e 02-2.\u003cbr\u003e Categorizing dog and cat photos\u003cbr\u003e __Loading the VGGNet model \u003cbr\u003e__Categorizing dog and cat photos\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e 02-3.\u003cbr\u003e Improving the Performance of Dog and Cat Photo Classification Models\u003cbr\u003e __CNN model that improves training performance - ResNet\u003cbr\u003e __Building a ResNet model\u003cbr\u003e __Categorizing dog and cat photos\u003cbr\u003e __[Learn more] GoogLeNet\u003cbr\u003e __[Mini Project] Classifying Dog and Cat Photos with GoogLeNet\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 03.\u003cbr\u003e Classifying Images with Advanced CNN Models and Transfer Learning\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 03-1.\u003cbr\u003e Optimizing the Efficiency of Image Classification Models\u003cbr\u003e __ResNet's extension model - DenseNet\u003cbr\u003e __Mobile Environment (Lightweight) Model - MobileNet\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e 03-2.\u003cbr\u003e Optimizing the Performance of Image Classification Models\u003cbr\u003e __Highest-Performing Model - EfficientNet\u003cbr\u003e __Building an EfficientNet model\u003cbr\u003e Classifying Dog Photos with the EfficientNet Model\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e 03-3.\u003cbr\u003e Classifying Pistachio Images with Transfer Learning \u003cbr\u003eClassifying Dog Photos with TensorFlow Hub\u003cbr\u003e __Categorizing dog photos with Hugging Face\u003cbr\u003e Classifying Pistachio Varieties with Transfer Learning\u003cbr\u003e __[Mini Project] Classifying Pistachio Varieties with Kaggle Models\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 04.\u003cbr\u003e Sentiment Classification of Movie Review Text Using the Transformer Encoder Model\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 04-1.\u003cbr\u003e Understanding the Transformer Encoder Model\u003cbr\u003e __Attention mechanism\u003cbr\u003e __Positional encoding and layer normalization\u003cbr\u003e __Building a Transformer Encoder Model\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e 04-2.\u003cbr\u003e Classifying Sentiment in Movie Review Text Using Transfer Learning\u003cbr\u003e __Transformer Encoder-Based Language Understanding Model - BERT\u003cbr\u003e __Classifying the Sentiment of Movie Review Text with KerasNLP\u003cbr\u003e __Classifying the Emotions of Movie Review Texts with Hugging Face\u003cbr\u003e __[Learn More] Sentiment Analysis with Fine-Tuned Models\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e \u003cbr\u003e04-3. Classifying Sentiment in Movie Review Text with a BERT Follow-up Model\u003cbr\u003e __BERT's Performance Improvement Model - RoBERTa\u003cbr\u003e __BERT's lightweight model - DistilBERT\u003cbr\u003e __[Mini Project] Building a DistilBERT Model with KerasNLP\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 05.\u003cbr\u003e Generating Text with the Transformer Decoder Model\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 05-1. Generating Text with the GPT-2 Model\u003cbr\u003e __Masked multi-head attention\u003cbr\u003e __Building a Transformer Decoder Module\u003cbr\u003e Generating diverse texts with the GPT-2 model\u003cbr\u003e __Creating various texts with Hugging Face\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e 05-2.\u003cbr\u003e Generating text with the Llama model\u003cbr\u003e __Understanding the Llama Model\u003cbr\u003e __Building the Llama-2 Model with KerasNLP\u003cbr\u003e Generating text with the __Llama-2 model\u003cbr\u003e Generating text with the __Llama-3 model\u003cbr\u003e __[Learn more] Llama-3.1 and Llama-3.2\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e 05-3.\u003cbr\u003e Generating Text with the Gemma Model\u003cbr\u003e __Understanding the Gemma Model\u003cbr\u003e __Building the Gemma Model with KerasNLP \u003cbr\u003eGenerating text with the __Gemma model\u003cbr\u003e Generating Text with the Gemma-2 Model\u003cbr\u003e __[Mini Project] Building a Llama-3 Model with KerasNLP\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 06.\u003cbr\u003e Summarizing Text with a Transformer Encoder-Decoder Model\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 06-1. Summarizing Text with the BART Model\u003cbr\u003e __Building a Transformer Encoder-Decoder Model\u003cbr\u003e Summarizing text with the __BART model\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e 06-2.\u003cbr\u003e Summarizing text with the T5 model\u003cbr\u003e Understanding the __T5 Model\u003cbr\u003e Summarizing text with the __T5 model\u003cbr\u003e Summarizing text with the __T5-1.1 model\u003cbr\u003e __[Mini Project] Creating a T5-1.1 small model\u003cbr\u003e Key points organized by __keywords\u003cbr\u003e\u003cbr\u003e 06-3.\u003cbr\u003e Epilogue\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate5311\/MidCate006\/531058972.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eA book for self-taught students who want to develop deep learning skills through practice rather than grammar.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ● The first convolutional neural network (CNN) model - classifying fashion product images \u003cbr\u003ePre-trained CNN model - Classifying dog and cat photos\u003cbr\u003e Advanced CNN Models and Transfer Learning - Optimizing Model Efficiency \u0026amp; TensorFlow Hub and HuggingFace\u003cbr\u003e Transformer Encoder Model - Sentiment Classification of Movie Review Text\u003cbr\u003e ● Transformer Decoder Model - Generating Text with GPT, Llama, and Gemma Models\u003cbr\u003e ● Transformer Encoder-Decoder Model - Summarizing Text with BART and T5 Models\u003cbr\u003e\u003cbr\u003e The \"Study by Making on Your Own\" series was designed for readers who want to apply their acquired knowledge to real-world situations.\u003cbr\u003e The core goal of this series is to go beyond theory and grammar learning and to complete projects that are practically useful in daily life and work.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e By going beyond simply implementing a single model and following its evolution, you'll gain the adaptability and survival skills to new deep learning technologies.\u003cbr\u003e \u003cbr\u003e\u003cb\u003e● Who is this book for?\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Beginners who have completed the introductory book on deep learning and want to gain implementation experience\u003cbr\u003e - Those who want to move on to the next step after studying 『Machine Learning + Deep Learning (Revised Edition)』\u003cbr\u003e - Those who understand the basic knowledge but are wondering, “So what can I do with a deep learning model?”\u003cbr\u003e Deep learning learners interested in cutting-edge technologies\u003cbr\u003e - Anyone interested in the latest technologies in computer vision and large-scale language models\u003cbr\u003e - Those who want to use the latest deep learning models such as GPT, Llama, and Gemma\u003cbr\u003e\u003cbr\u003e \u003cb\u003e● Book Features\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e One, a friendly guide that allows you to learn on your own until the end.\u003cbr\u003e\u003cbr\u003e Don't worry if you get stuck while practicing.\u003cbr\u003e Systematic learning elements guide readers so that they can follow along and understand the material on their own. \u003cbr\u003e\"Grammar Check\" that covers the necessary concepts before writing code, \"Learn by Following\" that includes execution results and code explanations, and \"Mini Project\" that allows you to apply what you've learned on your own are all available to Honman readers.\u003cbr\u003e\u003cbr\u003e Two, sometimes alone, sometimes together! Support for author-direct YouTube lectures and learning sites.\u003cbr\u003e http:\/\/hongong.hanbit.co.kr\u003cbr\u003e If you have any questions while reading the book, please feel free to ask.\u003cbr\u003e We operate a KakaoTalk open chatroom and a learning site Q\u0026amp;A where the author personally answers questions.\u003cbr\u003e You can also download example files and watch video lectures at any time from the Honman Readers Community.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e 3. NO INSTALLATION! A deep learning hands-on course that runs online without the hassle of installation.\u003cbr\u003e All examples in \"Study Deep Learning by Doing It Yourself\" are practiced in Google Colab, an online environment. \u003cbr\u003eWhile it's best to try out the code yourself in Colab, all the code covered in the book is available on GitHub.\u003cbr\u003e Check the execution results with the Jupyter notebook on GitHub.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eA word from a beta reader about the \"Studying by Making on Your Own\" series\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e If there is anyone who is unable to even start because of the worry, “Can I really do it?”, take the first step with this book.\u003cbr\u003e - Beta leader Kim Jae-eun\u003cbr\u003e “The author’s meticulous attention to detail in designing the book to accommodate everyone from beginners to intermediate learners is outstanding.\u003cbr\u003e “It provides a learning experience that is easy and enjoyable to follow with just one book.” - Beta Reader Namju Kwak \u003cbr\u003e“For those who have only learned grammar, the method of studying by creating things on their own may be unfamiliar, but I was able to experience making it truly mine by creating and organizing the code myself.” - Beta reader Lee Ha-rang\u003cbr\u003e\u003cbr\u003e \u003cb\u003eAuthor-direct YouTube lectures + open chat provided\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e If you have any questions while reading the book, please feel free to ask.\u003cbr\u003e We operate a KakaoTalk open chatroom and a learning site Q\u0026amp;A where the author personally answers questions.\u003cbr\u003e You can also download example files and watch video lectures at any time on Hanbit Media's learning site. \u003cbr\u003e\n\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e May 12, 2025\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 444 pages | 898g | 188*257*21mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791169213714\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1169213715 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893236531242,"sku":"138401","price":39.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/80e3f73efd329edb4695ff6c2432e8ba.jpg?v=1765393263","url":"https:\/\/librairie.coreenne.fr\/en\/products\/138401","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}