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Are you thinking of mastering deep learning fundamentals?
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.
Target Users
Beginners who want to approach deep learning, but are too afraid of complex math to start
Newbies in data science and computer science techniques
Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
Students and academicians, especially those focusing on deep learning and machine learning
What's Inside This Book?
- 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
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, Kobo refund you if you aren't satisfied, for more information about the Kobo refund service please go to the Kobo help platform.
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