Shopping Cart

Become a Instructor

Keras Deep Learning Projects

164 minutes of course content

Review course syllabus

$19.99 (taxes calculated at checkout)

100% money-back guarantee  |  Click here to read more

Share this course:
Facebook Google LinkedIn Pinterest Twitter

Course Description

Learn to build cutting-edge Deep Learning models in a simple, easy to understand way.

About This Video

  • Covers practical projects on building and training deep learning models with Keras
  • Combines theory and practice, giving you a solid foundation to build your own Deep Leaning models.
  • Implement state of the art CNNs, RNNs, Autoencoders and Generative Adversarial Models

In Detail

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.

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.

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.

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.

      After taking this course, you will be able to:

      • Build convolutional neural networks and recurrent neural networks

      • Use concepts, intuitive understating and applications of autoencoders and generative adversarial networks
      • Use transfer learning to greatly increase CNN performance
      • Build auto encoders and generative adversarial networks

      This course is for professionals within the following business functions:

      • Everyone is welcome in this course.

          1. Introduction to Jupyter Notebooks and Data Shapes - 10 Minutes

            1. The Course Overview
            2. Jupyter Notebook Basics
            3. Jupyter Notebook Basics
          2. Neural Network for House Price Prediction - 51 Minutes

            1. Neural Network for House Price Prediction
            2. Building Connected Layers and Applying Activation Functions
            3. Applying Loss Functions and Optimizers for Backpropagation
            4. Advanced Implementation with Keras
            5. Training the Model
            6. Testing the Model
            7. Metrics and Improving Performance
          3. Convolutional Neural Network for Image Classification - 40 Minutes

            1. Concepts of CNNs
            2. Concepts of CNNs Part 2
            3. Basic Implementation with Keras
            4. Leaky Rectified Linear Units
            5. Dropout
            6. Advanced Implementation with Keras
            7. Training the Model
            8. Training the Model and Metrics
            9. Testing the Model and Metrics
          4. Convolutional Autoencoder for Image Denoising - 17 Minutes

            1. Concepts and Applications of Autoencoders
            2. Concepts and Applications of Autoencoders
            3. Advanced Implementation with Keras
            4. Convolutional Autoencoder with Keras
            5. Training the Model
            6. Testing the Model
          5. Recurrent Neural Network for Machine Translation - 27 Minutes

            1. Concepts of RNNs, LSTM Cells, and GRU Cells
            2. Data Preprocessing
            3. Building a Simple RNN Model in Keras
            4. Advanced Implementation with Keras
            5. Training the Model
            6. Testing the Model
          6. Convolutional GAN for Image Generation - 19 Minutes

            1. Concepts and Applications of GANs
            2. Batch Normalization
            3. Convolutional GAN with Keras
            4. Training the Model
            5. Testing the Model

          About the Expert



          Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work. With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now. From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer. Packt courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.
          Read more