Deep Learning with Python, TensorFlow, and Keras

This course provides fundamentals of deep learning concepts and models are provided by means of the Keras and TensorFlow and it details how deep learning models work and which tools can be used for deep learning development. Throughout the course, students will learn the applications of deep learning models in the areas of supervised learning, transfer learning, reinforcement learning and unsupervised learning. 

Entry-level programming knowledge is required for participation in the course. 

Delegates will learn
Basic Deep Learning Architecture
Convolutional Neural Networks
Recurrent Neural Networks
Transfer Learning - Reinforcement Learning
Unsupervised Learning

Basic Deep Learning Architecture

Why everybody talks about Deep Learning and AI? 

Amazing Examples of Deep Learning 

Overview of Machine Learning Basics 

Perceptron and Biological Inspirations  

What is Artificial Neural Network? 

Feedforward Artificial Neural Network Architecture 

Deep Artificial Neural Network Architectures 

Nonlinear Activation Functions 

Loss functions 

Lion Tamer: Backpropagation and Gradient Descent 

Overview of Keras and TensorFlow 

Keras basics: Layers and Sequential Models 

Visualization with TensorBoard 

Convolutional Neural Networks

Representation Learning and ANN 

Image Data and Computer Vision Difficulties 

Feature Engineering and Deep Learning Models 

Convolution Process 

Deep Convolutional Neural Network (CNN) Architectures 

Overfitting and Regularization 

Overfitting and Regularization of Convolutional Neural Networks 

MaxPooling, Dropout  

Model Training Techniques: Stochastic Gradient Descent and Mini Batch 

What does CNN learn? CNN Visualization 

CNN Implementation with Keras 

Recurrent Neural Networks

ANN, CNN and Sequence Data 

Time as a sequence 

Recurrent Neural Network Architectures 

Vanishing and Exploding Gradient Problem

Long-Short Term Memory (LSTM) Models 

What does LSTM learn? 

Catastrophic Forgetting 

Alternative RNN Models 

Text Translation and Encoder - Decoder Structure 

LSTM Implementation with Keras 

Transfer Learning - Reinforcement Learning

Deep Learning Models and Data Finding Problem 

Artificial Intelligence and Removing Human Intervention 

What is Transfer Learning? 

Transfer Learning Application in Image Processing with Keras 

Transfer Learning Application in Text Classification with Keras 

Towards Artificial General Intelligence 

What is Reinforcement Learning? 

Deep Learning Models and Reinforcement Learning 

Dynamic Programming and Q-Learning 

Q-Learning Application with Keras and TensorFlow (We play Atari!) 

Unsupervised Learning

Deep Learning as Unsupervised Modeling Tool  

Autoencoder and Dimension Reduction 

Autoencoder and Data Denoising 

Variational Autoencoder 

Variational Autoencoder with Keras 

Game Theory and Generative Adversarial Networks (GAN) 

GAN and Statistical Distribution Modeling 

GAN and Artistic Deep Learning Applications 

GAN Application (Generating Human face) with Keras and TensorFlow

Entry-level programming knowledge is required for participation in the course.

Program Details
Duration 4 Days
Capacity Max 12 Persons
Training Type Classroom / Virtual Classroom

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