Course Outline


Overview of Neural Networks

Understanding Convolutional Networks

Setting up Keras

Overview of Keras Features and Architecture

Overview of Keras Syntax

Understanding How a Keras Model Organize Layers

Configuring the Keras Backend (TensorFlow or Theano)

Implementing an Unsupervised Learning Model

Analyzing Images with a Convolutional Neural Network (CNN)

Preprocessing Data

Training the Model

Training on CPU vs GPU vs TPU

Evaluating the Model

Using a Pre-trained Deep Learning Model

Setting up a Recurrent Neural Network (RNN)

Debugging the Model

Saving the Model

Deploying the Model

Monitoring a Keras Model with TensorBoard


Summary and Conclusion


  • Python Programming experience.
  • Experience with the Linux command line.


  • Developers
  • Data scientists
 21 Hours

Number of participants

Price per participant

Testimonials (5)

Related Courses

Advanced Deep Learning with Keras and Python

14 Hours

Deep Learning for Self Driving Cars

21 Hours

Artificial Neural Networks, Machine Learning, Deep Thinking

21 Hours

Introduction to Deep Learning

21 Hours

Advanced Deep Learning

28 Hours

Deep Learning for Vision with Caffe

21 Hours

Deep Learning for Vision

21 Hours

Artificial Intelligence (AI) in Automotive

14 Hours

Machine Learning and Deep Learning

21 Hours

OpenNN: Implementing Neural Networks

14 Hours

OpenNMT: Setting Up a Neural Machine Translation System

7 Hours

Introduction Deep Learning & Réseaux de neurones pour l’ingénieur

21 Hours


21 Hours

OpenFace: Creating Facial Recognition Systems

14 Hours

Advanced Machine Learning with Python

21 Hours

Related Categories