Packt – – Master Deep Learning with TensorFlow 2.0 in Python
English | Size: 2.30 GB
Category: Python
Build deep learning algorithms with TensorFlow 2.0, dive into neural networks, and apply your skills in a business case.
Key Features
Build deep learning algorithms from scratch in Python using NumPy and TensorFlow
Set yourself apart from the competition with hands-on deep- and machine-learning experience
Grasp the math behind deep learning algorithms
What You Will Learn
Gain a strong understanding of TensorFlow – Google’s cutting-edge deep learning framework
Understand backpropagation, Stochastic Gradient Descent, batching, momentum, and learning rate schedules
Master the ins and outs of underfitting, overfitting, training, validation, testing, early stopping, and initialization
Competently carry out pre-processing, standardization, normalization, and one-hot encoding
Description
In this course, we’ll teach you to master Deep Learning. We start with the basics and take you step by step toward building your very first (or second, or third…) deep learning algorithm; we program everything in Python and explain each line of code. We do this early on to give you the confidence to progress to the more complex topics we cover.
All sophisticated concepts we teach are explained intuitively. You’ll get fully acquainted with TensorFlow and NumPy, two tools that are essential for creating and understanding Deep Learning algorithms. You’ll explore layers, their building blocks, and activations – sigmoid, tanh, ReLu, softmax, and more.
You’ll understand the backpropagation process, intuitively and mathematically. You’ll be able to spot and prevent overfitting, one of the biggest issues in machine and deep learning. You’ll master state-of-the-art initialization methods. Don’t know what initialization is? We explain that, too. you’ll learn how to build deep neural networks using real data, implemented by real companies in the real world-templates included! Also, you will create your very own deep learning algorithm.
Take the first step toward a satisfying data science career and becoming a Master of Deep Learning.
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