Applied Neural Networks with TensorFlow 2
API Oriented Deep Learning with Python
(Sprache: Englisch)
Implement deep learning applications using TensorFlow while learning the "why" through in-depth conceptual explanations.
You'll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several...
You'll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several...
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Implement deep learning applications using TensorFlow while learning the "why" through in-depth conceptual explanations. You'll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy-others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers.
You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you'll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs.
Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, you'll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively.
What You'll Learn
- Compare competing technologies and see why TensorFlow is more popular
- Generate text, image, or sound with GANs
- Predict the rating or preference a user will give to an item
- Sequence data with recurrent neural networks
Data scientists and programmers new to the fields of deep learning and machine learning APIs.
Inhaltsverzeichnis zu „Applied Neural Networks with TensorFlow 2 “
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
What is Machine Learning?
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 b. Cross-Validationc. Overfittingd. 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
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
Chapter 4: Relevant Technologies Used for Machine Learning Numpy
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
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
... mehr
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)
Chapter 8: Recurrent Neural Networks (RNNs) Introduction to RNN Architectures
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
Chapter 10: Recommender Systems Introduction to Recommender Systems
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
Chapter 12: Generative Adversarial Networks (GANs) Introduction to Generative Adversarial Networks
Generator and Discriminator Structures Image Generation with GANs Text Generation with GANs
Chapter 13: Conclusion
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)
Chapter 8: Recurrent Neural Networks (RNNs) Introduction to RNN Architectures
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
Chapter 10: Recommender Systems Introduction to Recommender Systems
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
Chapter 12: Generative Adversarial Networks (GANs) Introduction to Generative Adversarial Networks
Generator and Discriminator Structures Image Generation with GANs Text Generation with GANs
Chapter 13: Conclusion
... weniger
Autoren-Porträt von Orhan Gazi Yalçin
Orhan Gazi Yalçin is a joint Ph.D. candidate at the University of Bologna & the Polytechnic University of Madrid. After completing his double major in business and law, he began his career in Istanbul, working for a city law firm, Allen & Overy, and a global entrepreneurship network, Endeavor. During his academic and professional career, he taught himself programming and excelled in machine learning. He currently conducts research on hotly debated law & AI topics such as explainable artificial intelligence and the right to explanation by combining his technical and legal skills. In his spare time, he enjoys free-diving, swimming, exercising as well as discovering new countries, cultures, and cuisines.
Bibliographische Angaben
- Autor: Orhan Gazi Yalçin
- 2020, 1st ed., XIX, 295 Seiten, Maße: 15,6 x 23,7 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1484265122
- ISBN-13: 9781484265123
Sprache:
Englisch
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