Book Image

Keras Deep Learning Projects [Video]

By : Tsvetoslav Tsekov
Book Image

Keras Deep Learning Projects [Video]

By: Tsvetoslav Tsekov

Overview of this book

<p><span id="description" class="sugar_field">Keras is a deep learning library for fast, efficient training of deep learning models, and can also work with Tensorflow and Theano. Because it is lightweight and very easy to use, Keras has gained quite a lot of popularity in a very short time.</span></p> <p><span id="description" class="sugar_field">This course will show you how to leverage the power of Keras to build and train high performance, high accuracy deep learning models, by implementing practical projects in real-world domains.Spanning over three hours, this course will help you master even the most advanced concepts in deep learning and how to implement them with Keras. You will train CNNs, RNNs, LSTMs, Autoencoders and Generative Adversarial Networks using real-world training datasets. </span></p> <p><span id="description" class="sugar_field">These datasets will be from domains such as Image Processing and Computer Vision, Natural Language Processing, Reinforcement Learning and more.By the end of this highly practical course, you will be well-versed with deep learning and its implementation with Keras.</span></p> <p><span id="description" class="sugar_field">By the end of this course, you will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.</span></p> <h2><span class="sugar_field">Style and Approach</span></h2> <p><span class="sugar_field"><span id="trade_selling_points_c" class="sugar_field">The course aims to explains the Deep Learning concepts in a simple, easy to understand manner and provides intuitive knowledge of the subjects. After you have grasped the concepts of a model, you will learn how to implement it with Keras.</span></span></p>
Table of Contents (6 chapters)
Chapter 5
Recurrent Neural Network for Machine Translation
Content Locked
Section 5
Training the Model
Learn how to use our data to train the RNN model. - Provide our training data in the fit() method - Provide the max length of our labels as a parameter - Provide the length of the tokenizer dictionaries as parameters