Mastering Python Deep Learning: Handbook for Beginner to Advanced

Mastering Python Deep Learning: Handbook for Beginner to Advanced


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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 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.


Product Details

ISBN-13: 9781733570657
Publication date: 02/06/2019
Pages: 290
Sales rank: 386,445
Product dimensions: 6.00(w) x 9.00(h) x 0.61(d)

About the Author

We are a group of experts, PhD students and young practitioners of Artificial Intelligence, Computer Science, Machine Learning and Statistics. Some of us work in big companies like Google, Facebook, Microsoft, KPMG, BCG and Mazars.
We decided to produce a series of books mainly dedicated to beginners and newcomers on the techniques and methods of Machine Learning, Statistics, Artificial Intelligence and Data Science. Initially, our objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory and without a long reading. Today we also publish more complete books on sometopics for a wider audience.

About our Books

Our books have had phenomenal success and they are today among the best sellers on Amazon. Our books have helped many people to progress and especially to understand these techniques, which are sometimes considered to be complicated rightly or wrongly.
The books we produce are short, very pleasant to read. These books focus on the essentials so that beginners can quickly understand and practice effectively. You will never regret having chosen one of our books.

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