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· 제목 : Applied Neural Networks with Tensorflow 2: API Oriented Deep Learning with Python (Paperback)
· 분류 : 외국도서 > 컴퓨터 > 인공지능(AI)
· ISBN : 9781484265123
· 쪽수 : 295쪽
· 출판일 : 2020-11-30
· 분류 : 외국도서 > 컴퓨터 > 인공지능(AI)
· ISBN : 9781484265123
· 쪽수 : 295쪽
· 출판일 : 2020-11-30
목차
Chapter 1: Introduction
- How to Make the Most out of this Book
- What is Tensorflow?
- What’s New in Tensorflow 2.0
- Google Colab and Jupyter Notebook
- Installation and Environment Setup
Chapter 2: Machine Learning
● What is Machine Learning?
● Types of Machine Learning
a. Supervised Learning: Regression, Classification (Binary or Multiclass)
● Types of Machine Learning
a. Supervised Learning: Regression, Classification (Binary or Multiclass)
b. Unsupervised Learning
c. Semi-Supervised Learning
d. Reinforcement Learning
● Machine Learning Terms:
a. Data and Datasets: Train, Test, and Validation
c. Semi-Supervised Learning
d. Reinforcement Learning
● Machine Learning Terms:
a. Data and Datasets: Train, Test, and Validation
b. Cross-Validation
c. Overfitting
d. Bias & Variance,
e. Fine-Tuning
f. Performance Terms: Accuracy, Recall, Precision, F1 Score, Confusion Matrix
● Introduction to and Comparison of ML Models:
a. Regression (Linear and Logistic), Decision Trees, K-Nearest Neighbors, Support
Vector Machines, K-Means Clustering, Principal Component Analysis
● Steps of Machine Learning: Data Cleaning, Model Building, Dataset Split: Training, Testing,
and Validation, and Performance Evaluation
e. Fine-Tuning
f. Performance Terms: Accuracy, Recall, Precision, F1 Score, Confusion Matrix
● Introduction to and Comparison of ML Models:
a. Regression (Linear and Logistic), Decision Trees, K-Nearest Neighbors, Support
Vector Machines, K-Means Clustering, Principal Component Analysis
● Steps of Machine Learning: Data Cleaning, Model Building, Dataset Split: Training, Testing,
and Validation, and Performance Evaluation
Chapter 3: Deep Learning
● Introduction to Deep Learning
● Introduction to Perceptron
● Activation Functions
● Cost (Loss) Function
● Gradient Descent Backpropagation
● Normalization and Standardization
● Loss Function and Optimization Functions
● Optimizer
● Introduction to Perceptron
● Activation Functions
● Cost (Loss) Function
● Gradient Descent Backpropagation
● Normalization and Standardization
● Loss Function and Optimization Functions
● Optimizer
Chapter 4: Relevant Technologies Used for Machine Learning
● Numpy
● Matplotlib
● Pandas
● Scikit Learn
● Deployment with Flask
● Matplotlib
● Pandas
● Scikit Learn
● Deployment with Flask
Chapter 5: TensorFlow 2.0
● Tensorflow vs. Other Deep Learning Libraries
● Keras API vs. Estimator
● Keras API Syntax
● Hardware Options and Performance Evaluation: CPUs vs. GPUs vs. TPUs
● Keras API vs. Estimator
● Keras API Syntax
● Hardware Options and Performance Evaluation: CPUs vs. GPUs vs. TPUs
Chapter 6: Artificial Neural Networks (ANNs)
● Introduction to ANNs
● Perceptron Model
● Linear (Shallow) Neural Networks
● Deep Neural Networks
● ANN Application Example with TF 2.0 Keras API
● Perceptron Model
● Linear (Shallow) Neural Networks
● Deep Neural Networks
● ANN Application Example with TF 2.0 Keras API
Chapter 7: Convolutional Neural Networks (CNNs)
● Introduction to CNN Architecture
● CNN Basics: Strides and Filtering
● Dealing with Image Data
● Batch Normalization
● Data Augmentation
● CNN for Fashion MNIST with TF 2.0 Keras API
● CNN for CIFAR10 with TF 2.0 Keras API (Pre-Trained Model)
● CNN with Imagenet with TF 2.0 Keras API (Pre-Trained Model)
● CNN Basics: Strides and Filtering
● Dealing with Image Data
● Batch Normalization
● Data Augmentation
● CNN for Fashion MNIST with TF 2.0 Keras API
● CNN for CIFAR10 with TF 2.0 Keras API (Pre-Trained Model)
● CNN with Imagenet with TF 2.0 Keras API (Pre-Trained Model)
Chapter 8: Recurrent Neural Networks (RNNs)
● Introduction to RNN Architectures
● Sequence Data (incl. Time Series)
● Data Preparation
● Simple RNN Architecture
● Sequence Data (incl. Time Series)
● Data Preparation
● Simple RNN Architecture
● Gated Recurrent Unit (GRU) Architecture
● Long-Short Term Memory (LSTM) Architecture
● Simple RNN, GRU, and LSTM Comparison
Chapter 9: Natural Language Processing (RNN and CNN applications)
● Introduction to Natural Language Processing
● Text Processing
● NLP Application with RNN
● NLP Application with CNN
● Text Generation
● Text Processing
● NLP Application with RNN
● NLP Application with CNN
● Text Generation
Chapter 10: Recommender Systems
● Introduction to Recommender Systems
● Recommender System Using MovieLens Dataset
● Recommender System Using Jester Dataset
● Recommender System Using MovieLens Dataset
● Recommender System Using Jester Dataset
Chapter 11: Auto-Encoders
● Introduction to Auto-Encoders
● Dimensionality Reduction
● Noise Removal
● Auto-Encoder for Images
● Dimensionality Reduction
● Noise Removal
● Auto-Encoder for Images
Chapter 12: Generative Adversarial Networks (GANs)
● Introduction to Generative Adversarial Networks
● Generator and Discriminator Structures
● Generator and Discriminator Structures
● Image Generation with GANs
● Text Generation with GANs
Chapter 13: Conclusion
