Mastering Python Deep Learning
If you are looking for a complete introduction to deep learning, this book is for you.
If you have just heard about deep learning and data science, this book is the right place to start. And if you are amazed by cool projects built by others and you want to build one of those yourself, this book is definitely for you. Not only that, if you're going to start an exciting new career which can provide you with both financial and intellectual satisfaction, this book will assist you to reach that goal.
Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations.
Q: Does this book include everything I need to become a deep learning expert?
A: Unfortunately, no. This book is designed for readers taking their first steps in machine learning and deep learning further learning will be required beyond this book to master all aspects.
Q: Can I have a refund if this book doesn't fit for me?
A: Yes, Amazon refund you if you aren't satisfied, for more information about the B&N refund service please go to the B&N help platform.
1130550466
***** BUY NOW (will soon return to 18.95 $) *****
Are you thinking of mastering deep learning using Python (Pandas, Numpy, Scikit-learn, Keras and TensorFlow)?
If you are looking for a complete introduction to deep learning, this book is for you.
If you have just heard about deep learning and data science, this book is the right place to start. And if you are amazed by cool projects built by others and you want to build one of those yourself, this book is definitely for you. Not only that, if you're going to start an exciting new career which can provide you with both financial and intellectual satisfaction, this book will assist you to reach that goal.
From AI Sciences Publisher
Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations.
What's Inside This Book?
Part I: Deep Learning fundamentals
- Data Science and Deep Learning
- Machine Learning and Deep Learning Introduction
- Artificial Intelligence, Machine Learning and Deep Learning
- A brief history of Machine Learning
- What is Deep Learning?
- Why Deep Learning?
- The Math behind Machine Learning
- Data representation for neural networks
- Probability, Conditional Probability and Distributions
- Fundamentals of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Self-supervised Learning
- Reinforcement Learning
- Machine learning algorithms
- Linear Regression
- Logistic Regression
- Support Vector Machine
- K-means Clustering
- Evaluation of machine learning models
- Overfitting and Underfitting
- Foundations of Neural Networks and Deep Learning
- Artificial Neural Networks
- The Biological Neuron
- The Perceptron
- Multilayer Feed-Forward Networks
- Anatomy of a neural network
- Neural Networks tensor operations
- Training Neural Networks
- First Neural networks example
- Feed Forward Neural Network
- Common Deep Network Components
- Practical Considerations in Deep Learning
- Regularization
- Major Architectures of Deep Networks
- Convolutional Neural Network
- Recurrent Neural Network
- Recursive Neural Network
- Sources & References
Part II : Deep Learning in Practice
- Python for Beginners
- Python Data Structures
- Python Function
- Object Oriented Programming in Python
- Best practices in Python and Zen of Python
- Installing Python
- Numpy, Pandas, Matplotlib and Scikit-learn
- Evaluating a model's performance
- Keras and Tensorflow
- Deep learning workstation: Jupyter Notebooks and Getting Binary Classification
- Building Deep Learning Model
- Convolutional Neural Networks in Keras
- Data Preparation
- Model Building
- Training and Testing
- Deep learning for text and sequences
- Brief introduction to Google Colab
- Data Preparation
- Data Wrangling and Analysis
- Recurrent Neural Network (RNN)
Frequently Asked Questions
Q: Does this book include everything I need to become a deep learning expert?
A: Unfortunately, no. This book is designed for readers taking their first steps in machine learning and deep learning further learning will be required beyond this book to master all aspects.
Q: Can I have a refund if this book doesn't fit for me?
A: Yes, Amazon refund you if you aren't satisfied, for more information about the B&N refund service please go to the B&N help platform.
***** MONEY BACK GUARANTEE BY B&N*****
Mastering Python Deep Learning
If you are looking for a complete introduction to deep learning, this book is for you.
If you have just heard about deep learning and data science, this book is the right place to start. And if you are amazed by cool projects built by others and you want to build one of those yourself, this book is definitely for you. Not only that, if you're going to start an exciting new career which can provide you with both financial and intellectual satisfaction, this book will assist you to reach that goal.
Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations.
Q: Does this book include everything I need to become a deep learning expert?
A: Unfortunately, no. This book is designed for readers taking their first steps in machine learning and deep learning further learning will be required beyond this book to master all aspects.
Q: Can I have a refund if this book doesn't fit for me?
A: Yes, Amazon refund you if you aren't satisfied, for more information about the B&N refund service please go to the B&N help platform.
***** BUY NOW (will soon return to 18.95 $) *****
Are you thinking of mastering deep learning using Python (Pandas, Numpy, Scikit-learn, Keras and TensorFlow)?
If you are looking for a complete introduction to deep learning, this book is for you.
If you have just heard about deep learning and data science, this book is the right place to start. And if you are amazed by cool projects built by others and you want to build one of those yourself, this book is definitely for you. Not only that, if you're going to start an exciting new career which can provide you with both financial and intellectual satisfaction, this book will assist you to reach that goal.
From AI Sciences Publisher
Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations.
What's Inside This Book?
Part I: Deep Learning fundamentals
- Data Science and Deep Learning
- Machine Learning and Deep Learning Introduction
- Artificial Intelligence, Machine Learning and Deep Learning
- A brief history of Machine Learning
- What is Deep Learning?
- Why Deep Learning?
- The Math behind Machine Learning
- Data representation for neural networks
- Probability, Conditional Probability and Distributions
- Fundamentals of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Self-supervised Learning
- Reinforcement Learning
- Machine learning algorithms
- Linear Regression
- Logistic Regression
- Support Vector Machine
- K-means Clustering
- Evaluation of machine learning models
- Overfitting and Underfitting
- Foundations of Neural Networks and Deep Learning
- Artificial Neural Networks
- The Biological Neuron
- The Perceptron
- Multilayer Feed-Forward Networks
- Anatomy of a neural network
- Neural Networks tensor operations
- Training Neural Networks
- First Neural networks example
- Feed Forward Neural Network
- Common Deep Network Components
- Practical Considerations in Deep Learning
- Regularization
- Major Architectures of Deep Networks
- Convolutional Neural Network
- Recurrent Neural Network
- Recursive Neural Network
- Sources & References
Part II : Deep Learning in Practice
- Python for Beginners
- Python Data Structures
- Python Function
- Object Oriented Programming in Python
- Best practices in Python and Zen of Python
- Installing Python
- Numpy, Pandas, Matplotlib and Scikit-learn
- Evaluating a model's performance
- Keras and Tensorflow
- Deep learning workstation: Jupyter Notebooks and Getting Binary Classification
- Building Deep Learning Model
- Convolutional Neural Networks in Keras
- Data Preparation
- Model Building
- Training and Testing
- Deep learning for text and sequences
- Brief introduction to Google Colab
- Data Preparation
- Data Wrangling and Analysis
- Recurrent Neural Network (RNN)
Frequently Asked Questions
Q: Does this book include everything I need to become a deep learning expert?
A: Unfortunately, no. This book is designed for readers taking their first steps in machine learning and deep learning further learning will be required beyond this book to master all aspects.
Q: Can I have a refund if this book doesn't fit for me?
A: Yes, Amazon refund you if you aren't satisfied, for more information about the B&N refund service please go to the B&N help platform.
***** MONEY BACK GUARANTEE BY B&N*****
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Mastering Python Deep Learning
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Mastering Python Deep Learning
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Product Details
BN ID: | 2940161229385 |
---|---|
Publisher: | AI SCIENCES LLC |
Publication date: | 02/10/2019 |
Sold by: | Barnes & Noble |
Format: | eBook |
File size: | 7 MB |
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