### Course Description

The abundance of data and affordable cloud scale has led to an explosion of interest in Deep Learning. Google has released an excellent open-source library called TensorFlow. This library allows for state-of-the-art machine learning at scale with GPU-based acceleration. This course explores algorithms, machine learning, data mining concepts, and how TensorFlow implements them.

## Course Outline

**Machine Learning and Deep Learning Overview**

- Mathematical Concepts
- ML Overview
- DL Overview

**TensorFlow: Overview and Basics**

- TensorFlow: What is it? History and Background
- Use cases and Key Applications
- Machine Learning and Deep Learning Basics
- Environment, Configuration Settings and Installation
- TensorFlow Primitives
- Declaring Tensors
- Declaring Placeholders and Variables
- Working with Matrices
- Declaring Operations
- Operations in Computational Graph
- Nested Operations
- Multiple Layers
- Implementing Loss Functions
- Implementing Back Propagation

**Machine Learning With TensorFlow**

- Linear Regression Review
- Linear Regression Using TensorFlow
- Support Vector Machines (SVM) Review
- SVM using TensorFlow
- Nearest Neighbor Method Review
- Nearest Neighbor Method using TensorFlow

**Neural Networks With TensorFlow**

- Neural Networks Review
- Optimization and Operational Gates
- Working with Activation Functions
- Implementing One-Layer Neural Network
- Implementing Different Layers
- Implementing Multilayer Neural Networks

**Deep Neural Networks With TensorFlow**

- Models and Overview
- Convolutional Neural Network Overview and Implementation
- CNN Architecture
- Recurrent Neural Network Overview and Implementation
- RNN Architecture

**Additional Topics**

- TensorFlow Extensions
- Scikit Flow
- TFLearn
- TF-Slim
- TensorLayer
- Keras
- Unit Testing
- Taking your implementation to production

## Course Objectives

Join an engaging hands-on learning environment, where you'll learn:

- Core Deep Learning and Machine Learning math essentials
- TensorFlow Overview and Basics
- TensorFlow Operations
- Neural Networks With TensorFlow
- Deep Learning With TensorFlow

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.

## Course Prerequisites

Before attending this course, you should have:

- Strong Python Skills
- Strong foundational mathematics in Linear Algebra and Probability; Matrix Transformation, Regressions, Standard Deviation, Statistics, Classification, etc.
- Basic knowledge of machine learning and deep learning algorithms

### Course Information

Length: **2 day**

Format: **Lecture**

Delivery Method: **n/a**

Max. Capacity:
**16**

Schedule

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