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Mastering Python Deep learning: Handbook for Beginners to Advanced
***** 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?
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
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 Ne
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 Ne
Kindle Edition
Published February 2, 2019
2 people want to read
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