Deep Learning with TensorFlow and Keras Course

Course Summary

The Deep Learning with Tensorflow and Keras training course provides an overview of Deep Learning along with hands-on exercises using the popular Deep Learning tools, Tensorflow, and Keras.

The course begins with students building a binary perceptron and a multi-layer perceptron. Next students will build a convolutional neural network in order to perform image analysis and classification, as well as image attribution. The course concludes by covering recurrent neural networks and transfer learning.

This course is designed for developers who want to understand and use Deep Learning. This course can be customized to utilize PyTorch, H20 or DeepLearning4j.

Purpose

Learn Deep Learning concepts and popular tools.

Audience

Developers wanting to learn how to work with some of the most powerful and well-documented Machine/Deep Learning technologies.

Role

Data Engineer - Data Scientist - Software Developer

Skill Level

Intermediate

Style

Fast Track - Targeted Topic - Workshops

Duration

2 Days

Related Technologies

Artificial Intelligence

Tensorflow

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Productivity Objectives:

Explain the basics of Deep Learning.
Build a binary perceptron model for classification and a multi-layer perceptron for image classification.
Implement convolutional neural networks.
Utilize Pre-trained Models.

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about our training

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
Expert Practitioners

Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

What You'll Learn

In the Deep Learning with TensorFlow and Keras training course, you'll learn:

Introducing Deep Learning as a Tool
- Advantages and Disadvantages
- Frameworks and Languages
- Introducing Tensorflow
- Introducing Keras
Developing a Linear Single-Layer Perceptron for Binary Classification and Regression
- Decision Boundaries (Linear / Non-Linear)
- Weights / Bias
- Gradient Descent Optimization and Backpropagation
- Loss Functions (SSE, Cross-Entropy)
- Activation Functions (sigmoid, tanh, relu, leaky relu)
- Performing Regression Output
Performing Multi-Class Classification
- Introducing the Softmax Function
- How to Use Softmax Function for Multi-class Classification
- Sizing Input and Output layers for Multi-class Classification
Multi-Layer Perceptron Models (MLP)
- Hidden Layers and Non-Linear Decision Boundaries
- How to Size Multiple Decision Layers
- Curse of Dimensionality and Limitation of Feedforward Networks
Using Image Data with MLP
- How to Classify Image Data using MLP
- Limitations of LMP for Image Data
- L How to Perform Image Attribution in Deep Learning
- Relating Images with Tags
- Identifying Elements within Images
Feature Transformations for Image Processing
- Using OpenCV to Perform Front-End Image Processing
- Edge Detection Processing
- Dimensionality Reduction
- Image Preprocessing
CNNs and Image Recognition
- Convolutional Layers and Max Pooling Layers
- How Convolutional Layers Help Solve the Curse of Dimensionality
- Using CNNs to Improve Image Classifier
- Lab: CNNs and Image Classification
Transfer Learning
- Using Pre-trained Models (inception_v3 and Imagenet5)
- Adapting Pre-Trained Models to New Situations by Adding Layers
Attribution
RNNs and LSTMs
- How RNNs allow us to Model Dynamic Systems
- Feedback Loops and Stability in RNNs
- LSTM Architectures and Memory Units
- Sequential Learning