{"product_id":"154665","title":"artificial intelligence ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDPLQhwlT95f7wmtp5MotF0wH10F.png?v=1765081242\" 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 artificial intelligence \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\/117813841\/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 \"Artificial Intelligence 2nd Edition\" is filled with a variety of illustrations and practical exercises that even beginners to artificial intelligence can easily follow.\u003cbr\u003e Especially in the field of deep learning, the TensorFlow Playground site provided by Google was used to make it easier to understand the concepts. \u003cbr\u003eEach chapter's text is followed by appropriate exercises based on the learner's progress. By following these exercises, you will acquire various theories of artificial intelligence that can be applied in real-world situations.\u003cbr\u003e At the end of each chapter, practice problems are provided to allow students to further study.\u003cbr\u003e\u003cbr\u003e The changes in this revised edition are as follows:\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e - The latest trends were reflected in the introduction to artificial intelligence.\u003cbr\u003e\u003cbr\u003e - Modified and supplemented the Python code for exploration.\u003cbr\u003e\u003cbr\u003e - Added decision trees, a traditional machine learning theory.\u003cbr\u003e\u003cbr\u003e - Added content such as image recognition deep learning, reinforcement learning, and generative models that were not in the existing book, and supplemented the reinforcement learning practice code.\u003cbr\u003e\n\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 Introduction to Artificial Intelligence\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 The Age of Artificial Intelligence\u003cbr\u003e The impact of artificial intelligence\u003cbr\u003e Artificial intelligence permeates our lives\u003cbr\u003e Artificial intelligence and humans \u003cbr\u003e02 Definition of Artificial Intelligence\u003cbr\u003e intelligent agent\u003cbr\u003e Artificial Intelligence vs. Machine Learning vs. Deep Learning\u003cbr\u003e 03 Turing Test\u003cbr\u003e ELIZA\u003cbr\u003e The Chinese Room\u003cbr\u003e Eugene Goostman in 2014\u003cbr\u003e Problems with the Turing Test\u003cbr\u003e 04 History of Artificial Intelligence\u003cbr\u003e The Birth of Artificial Intelligence (1943-1956)\u003cbr\u003e Golden Age (1956-1974)\u003cbr\u003e The First AI Winter (1974-1980)\u003cbr\u003e Golden Age (1980-1987)\u003cbr\u003e The Second AI Winter (1987–1993)\u003cbr\u003e The Resurgence of Artificial Intelligence (1993-2011)\u003cbr\u003e Deep Learning, Big Data, and Artificial Intelligence (2011-present)\u003cbr\u003e 05 Where is artificial intelligence needed?\u003cbr\u003e self-driving cars\u003cbr\u003e Video recommendation system\u003cbr\u003e advertising system\u003cbr\u003e Chatbot\u003cbr\u003e Medical field\u003cbr\u003e Art creation\u003cbr\u003e New drug development and biology\u003cbr\u003e Super-large AI\u003cbr\u003e Mini Project: Trying Out Google's Deep Dream\u003cbr\u003e Try Mini Project ChatGPT\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 02 Exploration\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 Explore\u003cbr\u003e 02 State space exploration problem\u003cbr\u003e State space exploration problem\u003cbr\u003e LAB path finding problem\u003cbr\u003e LAB N-queen problem\u003cbr\u003e 03 Navigation Tree\u003cbr\u003e LAB 4-queen problem search tree\u003cbr\u003e 04 Basic Search Techniques \u003cbr\u003eMeasuring navigation performance\u003cbr\u003e 05 Depth-first search\u003cbr\u003e Analysis of depth-first search\u003cbr\u003e 06 Breadth-first search\u003cbr\u003e Analysis of breadth-first search\u003cbr\u003e 07 Depth-limited exploration\u003cbr\u003e Pros and Cons of IDDFS\u003cbr\u003e 08 FS and DFS 8-puzzle programs\u003cbr\u003e How to represent the board?\u003cbr\u003e What will be used to implement open and closed queues?\u003cbr\u003e How to create child nodes?\u003cbr\u003e BFS full source code\u003cbr\u003e DFS program\u003cbr\u003e 09 Empirical Exploration Methods\u003cbr\u003e 10 Hill Climbing Techniques\u003cbr\u003e Algorithm\u003cbr\u003e Local biggest problem\u003cbr\u003e 11 Top Priority Search\u003cbr\u003e 12 A* algorithm\u003cbr\u003e LAB A* algorithm simulation\u003cbr\u003e 13 Python implementation of the A* algorithm\u003cbr\u003e Search algorithm for the LAB N-queen problem\u003cbr\u003e Mini Project TSP\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 03 Game Tree\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 Game Program\u003cbr\u003e Game definition\u003cbr\u003e Game tree for Tic-Tac-Toe\u003cbr\u003e 02 Minimax Algorithm\u003cbr\u003e Applying the Minimax Algorithm to the Tic-Tac-Toe Game\u003cbr\u003e LAB Minimax Algorithm Practice\u003cbr\u003e Pseudocode of the minimax algorithm\u003cbr\u003e Minimax Performance Analysis\u003cbr\u003e 03 Tic-Tac-Toe Game Programming \u003cbr\u003e04 Alphabeta Pruning\u003cbr\u003e AlphaBeta algorithm\u003cbr\u003e AlphaBeta Algorithm Practice\u003cbr\u003e 05 Incomplete Decision\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 04 Expert Systems\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 Expert System\u003cbr\u003e History of expert systems\u003cbr\u003e 02 Components of an expert system\u003cbr\u003e Knowledge Base\u003cbr\u003e Inference Engine\u003cbr\u003e User interface\u003cbr\u003e 03 Knowledge and Artificial Intelligence\u003cbr\u003e Data, information, knowledge\u003cbr\u003e Rule 04\u003cbr\u003e You can use AND or OR in rules.\u003cbr\u003e 05 Inference in Expert Systems\u003cbr\u003e Forward inference\u003cbr\u003e backward reasoning\u003cbr\u003e LAB Inference Practice\u003cbr\u003e LAB Fire Treatment System\u003cbr\u003e 06 Conflict Resolution\u003cbr\u003e 07 Advantages and Disadvantages of Expert Systems\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 05 Knowledge Representation\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 Knowledge Representation\u003cbr\u003e Rule 02\u003cbr\u003e 03 Semantic Network\u003cbr\u003e 04 Frame\u003cbr\u003e Advantages of Frames\u003cbr\u003e Frames and Object-Oriented Programming\u003cbr\u003e Frames and Inheritance\u003cbr\u003e Semantic Web and Frames\u003cbr\u003e 05 Logic\u003cbr\u003e 06 Propositional logic\u003cbr\u003e Inference in propositional logic\u003cbr\u003e Modus Ponens\u003cbr\u003e Modus Tollens\u003cbr\u003e Syllogism\u003cbr\u003e 07 Predicate logic\u003cbr\u003e 08 Inference in predicate logic \u003cbr\u003eJeong Hyeong-sik\u003cbr\u003e Logic Fusion (Resolution)\u003cbr\u003e Proof by logical fusion\u003cbr\u003e 09 Introduction to the Semantic Web and Ontology\u003cbr\u003e Problems with the existing web\u003cbr\u003e Semantic Web\u003cbr\u003e Examples of the Semantic Web\u003cbr\u003e Elements of Semantic Web Technology\u003cbr\u003e Ontology\u003cbr\u003e 10 Prologue\u003cbr\u003e LAB Logic Convergence Lab #1\u003cbr\u003e LAB Logic Convergence Practice #2\u003cbr\u003e LAB Logic Convergence Practice #3\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 06 Fuzzy Logic\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 What is fuzzy logic?\u003cbr\u003e Areas where fuzzy logic can be used\u003cbr\u003e 02 Crisp sets and fuzzy sets\u003cbr\u003e Crisp set\u003cbr\u003e fuzzy sets\u003cbr\u003e Notation of fuzzy sets\u003cbr\u003e Example of LAB fuzzy sets\u003cbr\u003e 03 Operators in fuzzy sets\u003cbr\u003e Centralized operators CON and DIL\u003cbr\u003e LAB fuzzy set operator\u003cbr\u003e 04 Fuzzy Inference\u003cbr\u003e fuzzy rules\u003cbr\u003e Fundamentals of Fuzzy Inference\u003cbr\u003e The process of fuzzy inference\u003cbr\u003e If there are multiple rules\u003cbr\u003e Problems giving LAB tips\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 07 Uncertainty\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 Uncertainty\u003cbr\u003e Examples of uncertainty\u003cbr\u003e Why does uncertainty arise?\u003cbr\u003e Handling Uncertainty in Artificial Intelligence Systems\u003cbr\u003e 02 Handling uncertainty using probability \u003cbr\u003ePrior and posterior probabilities\u003cbr\u003e Bayes' theorem\u003cbr\u003e LAB Could I be infected with the Z-virus?\u003cbr\u003e LAB Fruit Problem\u003cbr\u003e LAB Card Game\u003cbr\u003e 03 Bayes' Theorem and Inference\u003cbr\u003e When there is multiple evidence and hypotheses\u003cbr\u003e Disadvantages of Bayes' theorem\u003cbr\u003e Calculating the probability of a rule using LAB Bayes' theorem\u003cbr\u003e LAB Spam Filtering\u003cbr\u003e LAB Monty Hall Problem\u003cbr\u003e 04 Confidence\u003cbr\u003e Definition of certainty\u003cbr\u003e Confidence in rules with uncertain evidence\u003cbr\u003e When a rule has multiple premises\u003cbr\u003e LAB Confidence Practice\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 08 Genetic Algorithms\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 Evolution in Nature\u003cbr\u003e 02 Genetic Algorithm\u003cbr\u003e Chromosomes, encodings, and evaluation functions\u003cbr\u003e Pseudocode for genetic algorithm\u003cbr\u003e Selection operator\u003cbr\u003e Crossover operator\u003cbr\u003e mutation operator\u003cbr\u003e genetic algorithm\u003cbr\u003e 03 Genetic Algorithm Example\u003cbr\u003e 04 Genetic Algorithm Program\u003cbr\u003e LAB 8-queen problem\u003cbr\u003e LAB TSP Problem\u003cbr\u003e 05 Advantages and Disadvantages of Genetic Algorithms\u003cbr\u003e 06 Genetic Programming\u003cbr\u003e How to express the program?\u003cbr\u003e Basic operations\u003cbr\u003e GP algorithm\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e \u003cbr\u003e\u003cb\u003eChapter 09: Introduction to Machine Learning\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 What is machine learning?\u003cbr\u003e Differences Between Machine Learning and Traditional Programming\u003cbr\u003e Artificial intelligence, machine learning, deep learning\u003cbr\u003e History of Machine Learning\u003cbr\u003e Types of machine learning\u003cbr\u003e 02 Machine Learning Terminology\u003cbr\u003e Feature\u003cbr\u003e Function Approximation and Machine Learning\u003cbr\u003e Label\u003cbr\u003e sample\u003cbr\u003e Learning and Prediction\u003cbr\u003e Training data and test data\u003cbr\u003e 03 Supervised Learning\u003cbr\u003e Regression\u003cbr\u003e Classification\u003cbr\u003e Mini Project: Experience machine learning using teachable machines.\u003cbr\u003e 04 Unsupervised Learning\u003cbr\u003e 05 Reinforcement Learning\u003cbr\u003e 06 Machine Learning Process\u003cbr\u003e Data collection\u003cbr\u003e Training data and test data\u003cbr\u003e Model selection\u003cbr\u003e learning\u003cbr\u003e evaluation\u003cbr\u003e prediction\u003cbr\u003e 07 Performance Evaluation of Machine Learning Algorithms\u003cbr\u003e Accuracy\u003cbr\u003e confusion matrix\u003cbr\u003e Mini Project: Experience Machine Learning\u003cbr\u003e 08 Uses of Machine Learning\u003cbr\u003e Where is machine learning used?\u003cbr\u003e The Practical Value of Machine Learning for Programmers\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 10 Linear Regression\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 Linear Regression\u003cbr\u003e Introduction to Linear Regression\u003cbr\u003e Types of linear regression\u003cbr\u003e Principles of linear regression\u003cbr\u003e Learning and Loss \u003cbr\u003e02 How to minimize the loss function in linear regression\u003cbr\u003e Analytical method\u003cbr\u003e Gradient Descent Method\u003cbr\u003e Gradient descent in linear regression\u003cbr\u003e 03 Linear Regression Python Implementation #1\u003cbr\u003e 04 Linear Regression Python Implementation #2\u003cbr\u003e Let's graph linear regression\u003cbr\u003e LAB Linear Regression Practice\u003cbr\u003e 05 Overfitting vs. Underfitting\u003cbr\u003e LAB Diabetes Example\u003cbr\u003e Predicting house prices based on LAB area\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 11 kNN, K-means, and Decision Trees\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 kNN algorithm\u003cbr\u003e Summary of the kNN algorithm\u003cbr\u003e Modified kNN algorithm\u003cbr\u003e Advantages and Disadvantages of the kNN Algorithm\u003cbr\u003e 02 Example: Iris Classification Using kNN\u003cbr\u003e Features and Labels\u003cbr\u003e Drawing a graph\u003cbr\u003e Learning kNN\u003cbr\u003e Let's predict\u003cbr\u003e 03 Example: Classifying Handwritten Images with kNN\u003cbr\u003e Reading the dataset\u003cbr\u003e Training data and test data\u003cbr\u003e model\u003cbr\u003e learning\u003cbr\u003e Prediction and Evaluation\u003cbr\u003e 04 Performance Evaluation of Machine Learning Algorithms\u003cbr\u003e Let's print the confusion matrix\u003cbr\u003e Classification Report\u003cbr\u003e 05 K-means clustering\u003cbr\u003e Example of K-means clustering\u003cbr\u003e K-means clustering \u003cbr\u003e06 K-means clustering using sklearn\u003cbr\u003e Include the library\u003cbr\u003e Prepare the data\u003cbr\u003e Data visualization\u003cbr\u003e Create a cluster\u003cbr\u003e How to determine k\u003cbr\u003e Implementation of the elbow method\u003cbr\u003e LAB K-means Clustering Practice\u003cbr\u003e 07 Decision Tree\u003cbr\u003e Principles of decision trees\u003cbr\u003e How do you build a tree?\u003cbr\u003e entropy\u003cbr\u003e Decision trees using sklearn\u003cbr\u003e Example 08: Classifying Irises Using Decision Trees\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 12 Perceptron\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 Neural Network\u003cbr\u003e Advantages of neural networks\u003cbr\u003e Mathematical model of neurons\u003cbr\u003e 02 Perceptron\u003cbr\u003e Can perceptrons learn logical operations?\u003cbr\u003e 03 Perceptron Learning Algorithm\u003cbr\u003e Example\u003cbr\u003e Practicing Perceptrons with Sklearn\u003cbr\u003e 04 Limitations of the Perceptron\u003cbr\u003e Learning XOR operation\u003cbr\u003e Linearly classifiable problems\u003cbr\u003e Solving the XOR problem with a multilayer perceptron\u003cbr\u003e Classified as LAB perceptron\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 13 Multilayer Perceptron (MLP)\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 Multilayer Perceptron\u003cbr\u003e Activation function\u003cbr\u003e step function\u003cbr\u003e sigmoid function \u003cbr\u003eReLU function\u003cbr\u003e Hyperbolic tangent function\u003cbr\u003e 02 Forward Pass\u003cbr\u003e Let's express it as a matrix\u003cbr\u003e 03 Error calculation\u003cbr\u003e What is a loss function?\u003cbr\u003e A concrete example of a loss function\u003cbr\u003e Example of calculating a loss function\u003cbr\u003e 04 Reverse Pass\u003cbr\u003e What do you need to know?\u003cbr\u003e gradient descent\u003cbr\u003e Reintroducing Gradient Descent\u003cbr\u003e gradient\u003cbr\u003e LAB Gradient Descent Practice\u003cbr\u003e 05 Backpropagation Algorithm\u003cbr\u003e Application of chain rules\u003cbr\u003e Hidden layer nodes\u003cbr\u003e Let's conclude\u003cbr\u003e Backpropagation learning algorithm\u003cbr\u003e LAB Backpropagation Algorithm Animation\u003cbr\u003e 06 MLP Implementation Using NumPy\u003cbr\u003e 1) Forward propagation\u003cbr\u003e 2) Perform error backpropagation\u003cbr\u003e 3) Write a function to test after learning is complete.\u003cbr\u003e 4) Call the training function and test function sequentially.\u003cbr\u003e 07 Practice using Google Playground\u003cbr\u003e Epoch\u003cbr\u003e learning rate\u003cbr\u003e Choosing an activation function\u003cbr\u003e Problem type\u003cbr\u003e Ratio of training data to test data\u003cbr\u003e Select input features\u003cbr\u003e Adding a hidden layer\u003cbr\u003e Start learning\u003cbr\u003e Classification practice without hidden layers\u003cbr\u003e Practice with added hidden layer\u003cbr\u003e 08 Google's TensorFlow \u003cbr\u003eInstalling TensorFlow\u003cbr\u003e Keras examples\u003cbr\u003e MNIST Digit Recognition Using LAB MLP\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 14: Deep Neural Networks and Deep Learning\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 Deep Learning\u003cbr\u003e The role of the hidden layer\u003cbr\u003e 02 Vanishing gradient problem\u003cbr\u003e New activation function\u003cbr\u003e Mini Project Activation Function Experiment\u003cbr\u003e 03 Loss function problem\u003cbr\u003e Mean square error\u003cbr\u003e Softmax activation function\u003cbr\u003e Cross entropy loss function\u003cbr\u003e Calculating LAB cross entropy\u003cbr\u003e 04 Loss Functions in Keras\u003cbr\u003e BinaryCrossentropy\u003cbr\u003e CategoricalCrossentropy\u003cbr\u003e SparseCategoricalCrossentropy\u003cbr\u003e MeanSquaredError\u003cbr\u003e 05 Weight Initialization Problem\u003cbr\u003e Weight initialization method\u003cbr\u003e Mini Project Weight Initialization Experiment\u003cbr\u003e 06 Mini Batch\u003cbr\u003e 07 Data Normalization\u003cbr\u003e 08 Data Encoding Techniques\u003cbr\u003e 09 Learning rate and momentum\u003cbr\u003e 10 Handling overfitting\u003cbr\u003e How to detect overfitting\u003cbr\u003e How to handle overfitting\u003cbr\u003e Early termination\u003cbr\u003e Weight regulation method\u003cbr\u003e Data Augmentation\u003cbr\u003e Dropout\u003cbr\u003e ensemble\u003cbr\u003e Mini Project Batch Size, Learning Rate, Regularization Term\u003cbr\u003e 11 Example: MNIST Digit Recognition\u003cbr\u003e Import numeric data \u003cbr\u003eBuilding a Model\u003cbr\u003e Teaching\u003cbr\u003e Example 12: MNIST Fashion Item Classification\u003cbr\u003e Using a fully connected neural network\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 15 Convolutional Neural Networks\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 Introduction to Convolutional Neural Networks\u003cbr\u003e The Origins of Convolutional Neural Networks\u003cbr\u003e Neocognitron\u003cbr\u003e The Importance of Convolutional Neural Networks\u003cbr\u003e Mini Project: Experience Convolutional Networks\u003cbr\u003e 02 Convolution operation\u003cbr\u003e Structure of a convolutional neural network\u003cbr\u003e Convolution operation in image processing\u003cbr\u003e Convolution operations in convolutional neural networks\u003cbr\u003e stride\u003cbr\u003e padding\u003cbr\u003e Number of kernels\u003cbr\u003e 03 Pooling (subsampling)\u003cbr\u003e 04 Example: MNIST Fashion Item Classification\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 16 Image Recognition\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 What is image recognition?\u003cbr\u003e Mini Project: Experience Image Recognition Neural Networks\u003cbr\u003e 02 Traditional image recognition\u003cbr\u003e 03 Image recognition using deep neural networks\u003cbr\u003e 04 Example: Classifying CIFAR-10 Images\u003cbr\u003e CIFAR-10 dataset\u003cbr\u003e CIFAR-10 classification program using convolutional neural networks\u003cbr\u003e 05 Data Augmentation\u003cbr\u003e Example 06: Distinguishing between dogs and cats\u003cbr\u003e Dog and Cat Dataset \u003cbr\u003eInstalling the library\u003cbr\u003e Image output\u003cbr\u003e Creating a neural network model\u003cbr\u003e Image preprocessing\u003cbr\u003e learning\u003cbr\u003e Display learning results graph\u003cbr\u003e 07 Weight Storage and Transfer Learning\u003cbr\u003e Saving and loading learned weights\u003cbr\u003e transfer learning\u003cbr\u003e Pre-trained neural network model\u003cbr\u003e Redefine a pretrained model for your project\u003cbr\u003e Example #1: Writing a Dog Recognition Program\u003cbr\u003e Example #2: Using a pretrained model as a feature extractor preprocessor.\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 17 Reinforcement Learning\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 What is reinforcement learning?\u003cbr\u003e Principles of reinforcement learning\u003cbr\u003e Reinforcement Learning Framework\u003cbr\u003e Reinforcement Learning and Deep Learning\u003cbr\u003e 02 Ice Lake Game\u003cbr\u003e OpenAI Foundation\u003cbr\u003e Frozen Lake game\u003cbr\u003e Ice Lake Game Version #1\u003cbr\u003e Analysis of execution results\u003cbr\u003e 03 Q-Learning #1\u003cbr\u003e compensation\u003cbr\u003e Q-function\u003cbr\u003e Policy\u003cbr\u003e Q-value circular relationship\u003cbr\u003e Let's actually calculate the Q-value in the ice lake problem.\u003cbr\u003e Ice Lake Game Version #2\u003cbr\u003e 04 Q-Learning #2\u003cbr\u003e Exploration and Exploitation\u003cbr\u003e Discounted rewards\u003cbr\u003e learning rate\u003cbr\u003e Ice Lake Game Version #3\u003cbr\u003e 05 Deep Q-Learning \u003cbr\u003eWhy use neural networks?\u003cbr\u003e DQN (Deep Q Network)\u003cbr\u003e Q-Learning vs. Deep Q-Learning\u003cbr\u003e How to learn\u003cbr\u003e Algorithm\u003cbr\u003e Practical application examples\u003cbr\u003e Problem\u003cbr\u003e Disadvantages of Deep Q-Learning\u003cbr\u003e Summary\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 18 Generative Models\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 01 What is a generative model?\u003cbr\u003e Generative models identify rules for generating training data.\u003cbr\u003e Differences between discriminative and generative models\u003cbr\u003e 02 What is GAN?\u003cbr\u003e GAN structure\u003cbr\u003e GAN training process\u003cbr\u003e Discriminator training\u003cbr\u003e Generator training\u003cbr\u003e loss function\u003cbr\u003e 03 Example: Generating Digit Images with GAN\u003cbr\u003e Summary\u003cbr\u003e Practice problems\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\/TopCate4123\/MidCate010\/412290464.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 The 2016 Go event in which AlphaGo defeated Lee Sedol shocked people around the world, and artificial intelligence became one of the most exciting fields in computer science. \u003cbr\u003eSince the development of deep learning algorithms and the objective demonstration of their superiority in an image recognition competition held in 2012, almost every country has been developing artificial intelligence technology as a core technology of the future.\u003cbr\u003e Having overcome the AI ​​winter that has occurred twice in the history of AI, a new era of AI is dawning, based on the science of extensive big data collection and analysis, highly advanced semiconductor integration, and ultra-high-speed processing technology.\u003cbr\u003e\u003cbr\u003e This book is a must-read for those preparing for the new era of artificial intelligence. It allows both engineering students majoring in convergence technologies such as computers, electronics, information and communication, and machinery, as well as non-computer science majors new to artificial intelligence, to learn the basic concepts and theories of artificial intelligence through various examples and practical exercises. \u003cbr\u003eIt covers all the fundamental theories of artificial intelligence and provides detailed explanations of machine learning, including deep learning, across 10 chapters.\u003cbr\u003e In the field of deep learning, you can practice using Keras, a high-level library based on Google's TensorFlow, and experience it using Google's TensorFlow Playground. \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 March 10, 2023\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 592 pages | 1,331g | 188*257*35mm\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 9791192373164\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 1192373162 \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":43893441626154,"sku":"154665","price":53.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/3d1f3a1e8a5e257b67af309fa338ef17.jpg?v=1765402273","url":"https:\/\/librairie.coreenne.fr\/en\/products\/154665","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}