{"product_id":"154915","title":"Machine Learning Q\u0026amp;A \u0026amp; AI ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDsmH2ST2wOU23n8l1SnQB6tDqZr.png?v=1765083015\" 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 Machine Learning Q\u0026amp;A \u0026amp; AI \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\/144225906\/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\u003eAsk machine learning, deep learning, and AI experts.\u003cbr\u003e\u003cbr\u003e “Sebastian Lasica, I’m curious about this!”\u003cbr\u003e Covering a wide range of advanced topics and the latest trends, with easy-to-understand illustrations and Q\u0026amp;A formats, in a simple, yet accessible and engaging way!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book is a unique Q\u0026amp;A style book written by Dr. Sebastian Rasica, a leader in the field of artificial intelligence, to answer frequently asked questions.\u003cbr\u003e The author's in-depth insights and logical explanations will help you gain a deeper understanding of various fields of artificial intelligence, including neural networks, deep learning, computer vision, natural language processing, generative AI, predictive performance, and model evaluation. \u003cbr\u003eBeyond basic theoretical explanations, it organizes technical knowledge, application methods, and practical explanations, introduces the latest research trends, and includes practice problems, answers to practice problems, and useful reference materials.\u003cbr\u003e Likewise, translator Haeseon Park, who is actively working in the domestic artificial intelligence field, wrote a Q\u0026amp;A at the end of the book answering 12 frequently asked questions from domestic readers while translating this book.\u003cbr\u003e I hope this will be helpful to those who want to upgrade their knowledge in the field of artificial intelligence in general.\u003cbr\u003e\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\u003ePart 1 | Neural Networks and Deep Learning\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 1: Embedding, Latent Space, and Representation\u003cbr\u003e 1.1 Embedding\u003cbr\u003e 1.2 Potential space\u003cbr\u003e 1.3 Expression\u003cbr\u003e 1.4 Practice Problems\u003cbr\u003e 1.5 Reference\u003cbr\u003e\u003cbr\u003e Chapter 2 Self-Directed Learning\u003cbr\u003e 2.1 Self-Supervised Learning vs. Transfer Learning\u003cbr\u003e 2.2 Using Unlabeled Data \u003cbr\u003e2.3 Self-predictive and contrastive self-supervised learning\u003cbr\u003e 2.4 Practice Problems\u003cbr\u003e 2.5 Note\u003cbr\u003e\u003cbr\u003e Chapter 3 Few-Shot Learning\u003cbr\u003e 3.1 Datasets and Terminology\u003cbr\u003e 3.2 Practice Problems\u003cbr\u003e\u003cbr\u003e Chapter 4: Lottery Ticket Hypothesis\u003cbr\u003e 4.1 Lottery Ticket Training Course\u003cbr\u003e 4.2 Practical implications and limitations\u003cbr\u003e 4.3 Practice Problems\u003cbr\u003e 4.4 Reference\u003cbr\u003e\u003cbr\u003e Chapter 5: Reducing Overfitting with Data\u003cbr\u003e 5.1 Representative methods\u003cbr\u003e __5.1.1 Additional Data Collection\u003cbr\u003e __5.1.2 Data Augmentation\u003cbr\u003e __5.1.3 Pre-training\u003cbr\u003e 5.2 Other methods\u003cbr\u003e 5.3 Practice Problems\u003cbr\u003e 5.4 Reference\u003cbr\u003e\u003cbr\u003e Chapter 6: Modifying the Model to Reduce Overfitting\u003cbr\u003e 6.1 General Method\u003cbr\u003e __6.1.1 Regulation\u003cbr\u003e __6.1.2 Small model\u003cbr\u003e __6.1.3 Notes on small models\u003cbr\u003e __6.1.4 Ensemble Methods\u003cbr\u003e 6.2 Other methods\u003cbr\u003e 6.3 Choosing a Regulatory Technique\u003cbr\u003e 6.4 Practice Problems\u003cbr\u003e 6.5 Reference\u003cbr\u003e\u003cbr\u003e Chapter 7 Multi-GPU Training Paradigm\u003cbr\u003e 7.1 Training Paradigm\u003cbr\u003e __7.1.1 Model Parallelism\u003cbr\u003e __7.1.2 Data Parallelism\u003cbr\u003e __7.1.3 Tensor Parallelism\u003cbr\u003e __7.1.4 Pipeline Parallelization\u003cbr\u003e __7.1.5 Sequence Parallelism\u003cbr\u003e 7.2 Recommendation\u003cbr\u003e 7.3 Practice Problems\u003cbr\u003e 7.4 Reference\u003cbr\u003e\u003cbr\u003e Chapter 8: The Success of Transformers\u003cbr\u003e 8.1 Attention Mechanism \u003cbr\u003e8.2 Pre-training through self-supervised learning\u003cbr\u003e 8.3 Large number of parameters\u003cbr\u003e 8.4 Easy Parallelization\u003cbr\u003e 8.5 Practice Problems\u003cbr\u003e See 8.6\u003cbr\u003e\u003cbr\u003e Chapter 9 Generative AI Models\u003cbr\u003e 9.1 Generative Modeling vs. Discriminative Modeling\u003cbr\u003e 9.2 Types of Deep Generative Models\u003cbr\u003e __9.2.1 Energy-based model\u003cbr\u003e __9.2.2 Variational Autoencoder\u003cbr\u003e __9.2.3 Generative Adversarial Networks\u003cbr\u003e __9.2.4 Flow-based model\u003cbr\u003e __9.2.5 Autoregressive model\u003cbr\u003e __9.2.6 Diffusion Model\u003cbr\u003e __9.2.7 Consistency Model\u003cbr\u003e 9.3 Recommended\u003cbr\u003e 9.4 Practice Problems\u003cbr\u003e See 9.5\u003cbr\u003e\u003cbr\u003e Chapter 10: Causes of Randomness\u003cbr\u003e 10.1 Initializing model weights\u003cbr\u003e 10.2 Dataset Sampling and Shuffling\u003cbr\u003e 10.3 Nondeterministic algorithms\u003cbr\u003e 10.4 Various Runtime Algorithms\u003cbr\u003e 10.5 Hardware and Drivers\u003cbr\u003e 10.6 Randomness and Generative AI\u003cbr\u003e 10.7 Practice Problems\u003cbr\u003e See 10.8\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePart 2 | Computer Vision\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 11 Number of parameters\u003cbr\u003e 11.1 How to calculate the number of parameters\u003cbr\u003e __11.1.1 Convolutional Layer\u003cbr\u003e __11.1.2 Fully connected layer\u003cbr\u003e 11.2 Practical reasons\u003cbr\u003e 11.3 Practice Problems\u003cbr\u003e\u003cbr\u003e Chapter 12 Fully Connected Layers and Convolutional Layers  \u003cbr\u003e12.1 When the kernel size and input size are the same\u003cbr\u003e 12.2 When the kernel size is 1\u003cbr\u003e 12.3 Recommended\u003cbr\u003e 12.4 Practice Problems\u003cbr\u003e\u003cbr\u003e Chapter 13: Large-Scale Training Sets for Vision Transformers\u003cbr\u003e 13.1 Inductive bias in CNNs\u003cbr\u003e 13.2 ViT can outperform CNN\u003cbr\u003e 13.3 Inductive Bias of ViT\u003cbr\u003e 13.4 Recommended\u003cbr\u003e 13.5 Practice Problems\u003cbr\u003e See 13.6\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePart 3 | Natural Language Processing\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 14 Distribution Hypothesis\u003cbr\u003e 14.1 Word2vec, BERT, GPT\u003cbr\u003e 14.2 Is the hypothesis valid?\u003cbr\u003e 14.3 Practice Problems\u003cbr\u003e 14.4 Reference\u003cbr\u003e\u003cbr\u003e Chapter 15: Text Data Augmentation\u003cbr\u003e 15.1 Replacing synonyms\u003cbr\u003e 15.2 Deleting words\u003cbr\u003e 15.3 Changing word positions\u003cbr\u003e 15.4 Sentence Mixing\u003cbr\u003e 15.5 Added noise\u003cbr\u003e 15.6 Reverse translation\u003cbr\u003e 15.7 Synthetic Data\u003cbr\u003e 15.8 Recommended\u003cbr\u003e 15.9 Practice Problems\u003cbr\u003e See 15.10\u003cbr\u003e\u003cbr\u003e Chapter 16: Self-Attention\u003cbr\u003e 16.1 Attention Mechanism of RNNs\u003cbr\u003e 16.2 Self-Attention Mechanism\u003cbr\u003e 16.3 Practice Problems\u003cbr\u003e See 16.4\u003cbr\u003e\u003cbr\u003e Chapter 17: Encoder-Based Transformers and Decoder-Based Transformers\u003cbr\u003e 17.1 Original Transformer\u003cbr\u003e 17.2 Encoder\u003cbr\u003e 17.3 Decoder\u003cbr\u003e 17.4 Encoder-Decoder\u003cbr\u003e 17.5 Note on Terminology\u003cbr\u003e 17.6 Latest Transformer Model  \u003cbr\u003e17.7 Practice Problems\u003cbr\u003e See 17.8\u003cbr\u003e\u003cbr\u003e Chapter 18: Using and Fine-Tuning Pre-Trained Transformer Models\u003cbr\u003e 18.1 Using Transformers for Classification Tasks\u003cbr\u003e 18.2 In-Context Learning, Indexing, and Prompt Tuning\u003cbr\u003e 18.3 PEFT\u003cbr\u003e 18.4 RLHF\u003cbr\u003e 18.5 Applying a Pretrained Model\u003cbr\u003e 18.6 Practice Problems\u003cbr\u003e See 18.7\u003cbr\u003e\u003cbr\u003e Chapter 19: Evaluating Generative Large-Scale Language Models\u003cbr\u003e 19.1 Evaluation Criteria for LLM\u003cbr\u003e 19.2 Congestion\u003cbr\u003e 19.3 BLEU score\u003cbr\u003e 19.4 ROUGE score\u003cbr\u003e 19.5 BERTScore\u003cbr\u003e 19.6 Proxy Evaluation Indicators\u003cbr\u003e 19.7 Practice Problems\u003cbr\u003e See 19.8\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePart 4 | Product Development and Distribution\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 20: Stateless and Stateful Training\u003cbr\u003e 20.1 Stateless Training\u003cbr\u003e 20.2 Stateful Training\u003cbr\u003e 20.3 Practice Problems\u003cbr\u003e\u003cbr\u003e Chapter 21: Data-Driven AI\u003cbr\u003e 21.1 Data-Driven AI vs. Model-Driven AI\u003cbr\u003e 21.2 Recommendations\u003cbr\u003e 21.3 Practice Problems\u003cbr\u003e See 21.4\u003cbr\u003e\u003cbr\u003e Chapter 22: Speeding Up Inference\u003cbr\u003e 22.1 Parallelization\u003cbr\u003e 22.2 Vectorization\u003cbr\u003e 22.3 Loop Tiling\u003cbr\u003e 22.4 Operator Fusion\u003cbr\u003e 22.5 Quantization\u003cbr\u003e 22.6 Practice Problems\u003cbr\u003e 22.7 Reference\u003cbr\u003e\u003cbr\u003e Chapter 23: Changes in Data Distribution\u003cbr\u003e 23.1 Covariate Changes\u003cbr\u003e 23.2 Label changes\u003cbr\u003e 23.3 Conceptual Change \u003cbr\u003e23.4 Domain Changes\u003cbr\u003e 23.5 Types of Data Distribution Changes\u003cbr\u003e 23.6 Practice Problems\u003cbr\u003e 23.7 Reference\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePart 5 | Predictive Performance and Model Evaluation\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 24: Poisson Regression and Ordinal Regression\u003cbr\u003e 24.1 Practice Problems\u003cbr\u003e\u003cbr\u003e Chapter 25 Confidence Intervals\u003cbr\u003e 25.1 Definition of confidence intervals\u003cbr\u003e 25.2 Method\u003cbr\u003e __25.2.1 Method 1: Normal approximation interval\u003cbr\u003e __25.2.2 Method 2: Bootstrapping the Training Set\u003cbr\u003e __25.2.3 Method 3: Bootstrapping Test Set Predictions\u003cbr\u003e __25.2.4 Method 4: Retraining the model with a different random seed\u003cbr\u003e 25.3 Recommendations\u003cbr\u003e 25.4 Practice Problems\u003cbr\u003e 25.5 Reference\u003cbr\u003e\u003cbr\u003e Chapter 26: Confidence Intervals vs. Conformal Predictions\u003cbr\u003e 26.1 Confidence intervals and prediction intervals\u003cbr\u003e 26.2 Prediction Intervals and Conformal Prediction\u003cbr\u003e 26.3 Prediction domain, prediction interval, and prediction set\u003cbr\u003e 26.4 Computing Conformal Predictions\u003cbr\u003e 26.5 Example of Conformal Prediction\u003cbr\u003e 26.6 Advantages of Conformal Prediction\u003cbr\u003e 26.7 Recommendations\u003cbr\u003e 26.8 Practice Problems\u003cbr\u003e See 26.9\u003cbr\u003e\u003cbr\u003e Chapter 27 Appropriate Measurement Indicators\u003cbr\u003e 27.1 Conditions\u003cbr\u003e 27.2 Mean square error\u003cbr\u003e 27.3 Cross-entropy loss\u003cbr\u003e 27.4 Practice Problems\u003cbr\u003e\u003cbr\u003e Chapter 28 k in k-fold cross-validation  \u003cbr\u003eTradeoffs in choosing the 28.1 k value\u003cbr\u003e 28.2 Determining an Appropriate Value of k\u003cbr\u003e 28.3 Practice Problems\u003cbr\u003e See 28.4\u003cbr\u003e\u003cbr\u003e Chapter 29: Mismatch between training and test sets\u003cbr\u003e 29.1 Practice Problems\u003cbr\u003e\u003cbr\u003e Chapter 30 Limited Label Data\u003cbr\u003e 30.1 Improving Model Performance with Limited Label Data\u003cbr\u003e __30.1.1 Labeling More Data\u003cbr\u003e __30.1.2 Data Bootstrapping\u003cbr\u003e __30.1.3 Transfer Learning\u003cbr\u003e __30.1.4 Self-Directed Learning\u003cbr\u003e __30.1.5 Active Learning\u003cbr\u003e __30.1.6 Few-Shot Learning\u003cbr\u003e __30.1.7 Meta-learning\u003cbr\u003e __30.1.8 Weakly Supervised Learning\u003cbr\u003e __30.1.9 Semi-supervised learning\u003cbr\u003e __30.1.10 Self-training\u003cbr\u003e __30.1.11 Multi-Task Learning\u003cbr\u003e __30.1.12 Multimodal Learning\u003cbr\u003e __30.1.13 Inductive Bias\u003cbr\u003e 30.2 Recommendations\u003cbr\u003e 30.3 Practice Problems\u003cbr\u003e See 30.4\u003cbr\u003e\u003cbr\u003e \u003cb\u003eAppendix Practice Problems Answers\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Search\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\/TopCate5236\/MidCate003\/523525616.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\u003e\"Beyond the Basics\" (Chris Albon, Director of Machine Learning, Wikimedia Foundation)\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eIf you're ready to delve deeper into machine learning, deep learning, and AI beyond the basics, this book, structured in a question-and-answer format, will help you get started quickly and easily.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e This book was born from questions frequently asked of author Sebastian Rasica.\u003cbr\u003e It approaches advanced topics in a direct, simple, and clear way, making them easier to understand and very interesting.\u003cbr\u003e Each chapter is concise and self-contained, addressing one fundamental question about AI.\u003cbr\u003e Answers your questions with clear explanations, diagrams, and practical exercises.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eWhat this book covers\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ㆍ Core Questions: Concisely answer core questions about AI and break down complex concepts into easy-to-understand chunks.\u003cbr\u003e ㆍ Broad range of topics: Covers a wide range of topics, from neural network architecture and model evaluation to computer vision and natural language processing. \u003cbr\u003eㆍ Practical Application: Learn techniques such as improving model performance and fine-tuning large-scale models.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eIt also contains the following:\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Managing various factors of randomness in neural network training\u003cbr\u003e Understanding the difference between encoder and decoder architectures in large-scale language models\u003cbr\u003e ㆍ Reduce overfitting by changing data and models\u003cbr\u003e ㆍ Construct confidence intervals for classification models and optimize models when label data is insufficient.\u003cbr\u003e ㆍ Choose from a variety of multi-GPU training paradigms and generative AI model types.\u003cbr\u003e Understanding Performance Metrics for Natural Language Processing\u003cbr\u003e Understanding Inductive Bias in Vision Transformers\u003cbr\u003e\u003cbr\u003e If you're looking for the perfect book to upgrade your knowledge of machine learning, \"Machine Learning Q\u0026amp;AI\" will easily expand your knowledge beyond the basics.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Author's Preface]\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eIn this book, the term machine learning is used in a comprehensive sense encompassing machine learning, deep learning, and AI.\u003cbr\u003e Using a unique Q\u0026amp;A style, each chapter is structured around questions related to key concepts in machine learning, deep learning, and AI.\u003cbr\u003e All questions are accompanied by explanations, various pictures and graphs, and practice problems to aid understanding.\u003cbr\u003e Many chapters also include reference material for further study.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e This book covers a wide range of topics.\u003cbr\u003e Gaining new insights into existing architectures, such as convolutional neural networks, will allow us to leverage this technology more effectively.\u003cbr\u003e We also cover advanced techniques such as large-scale language models (LLMs) and the inner workings of vision transformers.\u003cbr\u003e Even experienced machine learning researchers and technologists will find something new to add to their toolbox.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Translator's Preface]\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eAs the author states, this book is designed to help readers advance beyond the introductory text.\u003cbr\u003e While translating, I also learned many new things.\u003cbr\u003e Unlike typical machine learning books, it doesn't contain formulas or code, so anyone can read it with an easy mind.\u003cbr\u003e I hope this book will help you on your ML journey.\u003cbr\u003e\u003cbr\u003e The errata for this book will be posted on the blog (https:\/\/tensorflow.blog\/ml-q-and-ai).\u003cbr\u003e Please be sure to check before reading the book.\u003cbr\u003e Anything related to this book is welcome.\u003cbr\u003e Please let me know anytime via blog or email.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Beta Reader Review]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book is explained in a Q\u0026amp;A format with readers and through easy-to-understand illustrations. Readers, from those interested in AI to those with specialized knowledge, will be able to resolve numerous questions and gain a deeper understanding.\u003cbr\u003e\u003cbr\u003e - Shim Hyeong-gwang, Korean ID Information, Front-end \u0026amp; Back-end Developer\u003cbr\u003e \u003cbr\u003eThis book covers in-depth content and is suitable for those who have acquired the basics of machine learning and deep learning. It includes practice problems for self-directed learning and reference materials for additional study.\u003cbr\u003e Beyond simple theoretical explanations, it also introduces practical application cases and the latest research trends, giving readers the feeling of being at the forefront of the AI ​​field.\u003cbr\u003e Overall, this is a very useful book for anyone looking to advance their knowledge in the field of machine learning.\u003cbr\u003e - Sanggil Park, Software Engineer, Viv Studios\u003cbr\u003e\u003cbr\u003e It's fun and easy, just like reading a blog post.\u003cbr\u003e Instead of rigidly listing complex concepts, we explain them as if unfolding an interesting story.\u003cbr\u003e I especially liked that each topic was short and separate, so I could read the parts I was curious about first.\u003cbr\u003e - Hyunseok Jo, Software Engineer, Lableup Co., Ltd.\u003cbr\u003e \u003cbr\u003eIt covers not only the technical knowledge required to implement machine learning and deep learning models, but also how to utilize them from a service perspective. It also provides practical explanations of how algorithms are actually applied in industrial settings, making it a useful resource for both AI researchers and practitioners.\u003cbr\u003e In particular, it stands out for logically presenting the limitations that a particular technique may not always be the best choice.\u003cbr\u003e\u003cbr\u003e - Lee Ho-min, Freelance Machine Learning Engineer\u003cbr\u003e\u003cbr\u003e This book consists of Q\u0026amp;As on key concepts in machine learning, deep learning, and AI, and suggests implications for learning beyond basic concepts, moving beyond conventional formulas and code-centered learning.\u003cbr\u003e I highly recommend this book to anyone who wants to explore their learning path and expand on key concepts after getting started with machine learning.\u003cbr\u003e - Seungmin Lim_cslee, software engineer\u003cbr\u003e \u003cbr\u003eIt clearly organizes the essential information you need to know to properly study machine learning.\u003cbr\u003e I think it's essential to understand not just how to use AI as a tool, but also how it works and what benefits it brings.\u003cbr\u003e\u003cbr\u003e - Kang Min-jae, Department of Electrical and Electronic Engineering, Sungkyunkwan University\u003cbr\u003e\u003cbr\u003e Other computer-related books usually focus on coding, but this book is different.\u003cbr\u003e It was organized in a way that I could easily read the content I wanted to know, which was a great help in easily understanding unfamiliar concepts.\u003cbr\u003e\u003cbr\u003e - Kim Jin-hyeok, Dongyang High School, Information Teacher\u003cbr\u003e\u003cbr\u003e As a data analyst and data center-related business manager, I'm constantly reading books on machine learning and data modeling.\u003cbr\u003e For those working in the industry, I think this is a good book to refer to for the relevant concepts and practical application methods. \u003cbr\u003ePark Kyung-ho, Data Analyst, LS Electric \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 April 9, 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 344 pages | 183*235*12mm\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 9791140713073 \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":43893479997482,"sku":"154915","price":44.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/a92b1672eea9aeacfe5edcd47b1c0a42.jpg?v=1765403374","url":"https:\/\/librairie.coreenne.fr\/en\/products\/154915","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}