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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 Tensor?ow
Deep learning workstation: Jupyter Notebooks and Getting Binary Classi?cation
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 amazon refund service please go to the amazon help platform.
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