Tag: TensorFlow

Duration

14 hours (usually 2 days including breaks)

Requirements

• Python programming experience

Audience

• Data Scientists

Overview

TensorFlow is an open source machine learning library. TensorFlow provides users the ability to use and create artificial intelligence for detecting and predicting fraud.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data.

By the end of this training, participants will be able to:

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Course Outline

Introduction

TensorFlow Overview

• What is TensorFlow?
• TensorFlow features

What is AI

• Computational Psychology
• Computational Philosophy

Machine Learning

• Computational learning theory
• Computer algorithms for computational experience

Deep Learning

• Artificial neural networks
• Deep learning vs. machine learning

Preparing the Development Environment

• Installing and configuring TensorFlow

TensorFlow Quick Start

• Working with nodes
• Using the Keras API

Fraud Detection

• Reading and writing to data
• Preparing features
• Labeling data
• Normalizing data
• Splitting data into test data and training data
• Formatting input images

Predictions and Regressions

• Loading a model
• Visualizing predictions
• Creating regressions

Classifications

• Building and compiling a classifier model
• Training and testing the model

Summary and Conclusion

Duration

7 hours (usually 1 day including breaks)

Requirements

• Experience with TensorFlow
• Experience with the Linux command line

Audience

• Developers
• Data scientists

Overview

TensorFlow Serving is a system for serving machine learning (ML) models to production.

In this instructor-led, live training (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

By the end of this training, participants will be able to:

• Train, export and serve various TensorFlow models
• Test and deploy algorithms using a single architecture and set of APIs
• Extend TensorFlow Serving to serve other types of models beyond TensorFlow models

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Course Outline

TensorFlow Serving Overview

• What is TensorFlow Serving?
• TensorFlow Serving architecture
• Serving API and REST client API

Preparing the Development Environment

• Installing and configuring Docker
• Installing ModelServer with Docker

TensorFlow Server Quick Start

• Training and exporting a TensorFlow model
• Monitoring storage systems
• Loading exported model
• Building a TensorFlow ModelServer

Advanced Configuration

• Writing a config file
• Reloading Model Server configuration
• Configuring models
• Working with monitoring configuration

Testing the Application

• Testing and running the server

Debugging the Application

• Handling errors

TensorFlow Serving with Kubernetes

• Running in Docker containers
• Deploying serving clusters

Securing the Application

• Hiding data

Troubleshooting

Summary and Conclusion

Duration

28 hours (usually 4 days including breaks)

Requirements

Background in physics, mathematics and programming. Involvment in image processing activities.

Overview

This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.

Course Outline

TensorFlow Basics

• Creation, Initializing, Saving, and Restoring TensorFlow variables
• Feeding, Reading and Preloading TensorFlow Data
• How to use TensorFlow infrastructure to train models at scale
• Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics

• Inputs and Placeholders
• Build the GraphS
  • Inference
  • Loss
  • Training
• Train the Model
  • The Graph
  • The Session
  • Train Loop
• Evaluate the Model
  • Build the Eval Graph
  • Eval Output

The Perceptron

• Activation functions
• The perceptron learning algorithm
• Binary classification with the perceptron
• Document classification with the perceptron
• Limitations of the perceptron

From the Perceptron to Support Vector Machines

• Kernels and the kernel trick
• Maximum margin classification and support vectors

Artificial Neural Networks

• Nonlinear decision boundaries
• Feedforward and feedback artificial neural networks
• Multilayer perceptrons
• Minimizing the cost function
• Forward propagation
• Back propagation
• Improving the way neural networks learn

Convolutional Neural Networks

• Goals
• Model Architecture
• Principles
• Code Organization
• Launching and Training the Model
• Evaluating a Model

Duration

35 hours (usually 5 days including breaks)

Requirements

Working knowledge of python

Overview

TensorFlow™ is an open source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called “word embeddings”. Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.

Audience

This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, embedding terms, building graphs and logging

Course Outline

Getting Started

• Setup and Installation

TensorFlow Basics

• Creation, Initializing, Saving, and Restoring TensorFlow variables
• Feeding, Reading and Preloading TensorFlow Data
• How to use TensorFlow infrastructure to train models at scale
• Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics 101

• Prepare the Data
  • Download
  • Inputs and Placeholders
• Build the Graph
  • Inference
  • Loss
  • Training
• Train the Model
  • The Graph
  • The Session
  • Train Loop
• Evaluate the Model
  • Build the Eval Graph
  • Eval Output

Advanced Usage

• Threading and Queues
• Distributed TensorFlow
• Writing Documentation and Sharing your Model
• Customizing Data Readers
• Using GPUs
• Manipulating TensorFlow Model Files

TensorFlow Serving

• Introduction
• Basic Serving Tutorial
• Advanced Serving Tutorial
• Serving Inception Model Tutorial

Getting Started with SyntaxNet

• Parsing from Standard Input
• Annotating a Corpus
• Configuring the Python Scripts

Building an NLP Pipeline with SyntaxNet

• Obtaining Data
• Part-of-Speech Tagging
• Training the SyntaxNet POS Tagger
• Preprocessing with the Tagger
• Dependency Parsing: Transition-Based Parsing
• Training a Parser Step 1: Local Pretraining
• Training a Parser Step 2: Global Training

Vector Representations of Words

• Motivation: Why Learn word embeddings?
• Scaling up with Noise-Contrastive Training
• The Skip-gram Model
• Building the Graph
• Training the Model
• Visualizing the Learned Embeddings
• Evaluating Embeddings: Analogical Reasoning
• Optimizing the Implementation

Duration

28 hours (usually 4 days including breaks)

Requirements

• Python

Overview

This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition

Audience

This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition

After completing this course, delegates will be able to:

• understand TensorFlow’s structure and deployment mechanisms
• carry out installation / production environment / architecture tasks and configuration
• assess code quality, perform debugging, monitoring
• implement advanced production like training models, building graphs and logging

Course Outline

Machine Learning and Recursive Neural Networks (RNN) basics

• NN and RNN
• Backpropagation
• Long short-term memory (LSTM)

TensorFlow Basics

• Creation, Initializing, Saving, and Restoring TensorFlow variables
• Feeding, Reading and Preloading TensorFlow Data
• How to use TensorFlow infrastructure to train models at scale
• Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics 101

• Tutorial Files
• Prepare the Data
  • Download
  • Inputs and Placeholders
• Build the Graph
  • Inference
  • Loss
  • Training
• Train the Model
  • The Graph
  • The Session
  • Train Loop
• Evaluate the Model
  • Build the Eval Graph
  • Eval Output

Advanced Usage

• Threading and Queues
• Distributed TensorFlow
• Writing Documentation and Sharing your Model
• Customizing Data Readers
• Using GPUs¹
• Manipulating TensorFlow Model Files

TensorFlow Serving

• Introduction
• Basic Serving Tutorial
• Advanced Serving Tutorial
• Serving Inception Model Tutorial

Convolutional Neural Networks

• Overview
  • Goals
  • Highlights of the Tutorial
  • Model Architecture
• Code Organization
• CIFAR-10 Model
  • Model Inputs
  • Model Prediction
  • Model Training
• Launching and Training the Model
• Evaluating a Model
• Training a Model Using Multiple GPU Cards¹
  • Placing Variables and Operations on Devices
  • Launching and Training the Model on Multiple GPU cards

Deep Learning for MNIST

• Setup
• Load MNIST Data
• Start TensorFlow InteractiveSession
• Build a Softmax Regression Model
• Placeholders
• Variables
• Predicted Class and Cost Function
• Train the Model
• Evaluate the Model
• Build a Multilayer Convolutional Network
• Weight Initialization
• Convolution and Pooling
• First Convolutional Layer
• Second Convolutional Layer
• Densely Connected Layer
• Readout Layer
• Train and Evaluate the Model

Image Recognition

• Inception-v3
  • C++
  • Java

¹ Topics related to the use of GPUs are not available as a part of a remote course. They can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64-bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.

Duration

21 hours (usually 3 days including breaks)

Requirements

• Statistics
• Python
• (optional) A laptop with NVIDIA GPU that supports CUDA 8.0 and cuDNN 5.1, with 64-bit Linux installed

Overview

TensorFlow is a 2nd Generation API of Google’s open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, building graphs and logging

Course Outline

Machine Learning and Recursive Neural Networks (RNN) basics

• NN and RNN
• Backprogation
• Long short-term memory (LSTM)

TensorFlow Basics

• Creation, Initializing, Saving, and Restoring TensorFlow variables
• Feeding, Reading and Preloading TensorFlow Data
• How to use TensorFlow infrastructure to train models at scale
• Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics 101

• Prepare the Data
  • Download
  • Inputs and Placeholders
• Build the Graph
  • Inference
  • Loss
  • Training
• Train the Model
  • The Graph
  • The Session
  • Train Loop
• Evaluate the Model
  • Build the Eval Graph
  • Eval Output

Advanced Usage

• Threading and Queues
• Distributed TensorFlow
• Writing Documentation and Sharing your Model
• Customizing Data Readers
• Using GPUs¹
• Manipulating TensorFlow Model Files

TensorFlow Serving

• Introduction
• Basic Serving Tutorial
• Advanced Serving Tutorial
• Serving Inception Model Tutorial

¹ The Advanced Usage topic, “Using GPUs”, is not available as a part of a remote course. This module can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64-bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.

Understand the intuition behind Artificial Neural Networks

Build artificial neural networks with Tensorflow

Classify images, data using deep learning

Apply Convolutional Neural Networks in practice

Requirements

• Some prior coding experience with python is required.

Description

Neural networks are a family of machine learning algorithms that are generating a lot of excitement. They are a technique that is inspired by how the neurons in our brains function. They are based on a simple idea: given certain parameters, it is possible to combine them in order to predict a certain result. For example, if you know the number of pixels in an image, there are ways of knowing which number is written in the image. The data that enters passes through various “ layers” in which a series of adjusted learning rules are applied by a weighted function. After passing through the last layer, the results are compared with the “correct” results, and the parameters are adjusted.

Although the algorithms and the learning process in general are complex, one the network has learned, it can freeze the various weights and function in a memory or execution mode. Google uses these types of algorithms, for example, for image searches.

There is no single definition for the meaning of Deep Learning. In general, when we talk of Deep Learning, we are referring to a group of Machine Learning algorithms based on neural networks that, as we have seen, are characterized by cascade data processing. The entrance signal passes through the various stages, and in each one, they are subjected to a non-linear transformation. This helps to extract and transform the variable according to the determined parameters (weights or boundaries). There isn’t an established limit for the number of stages that a neural network must contain to be considered Deep Learning. However, it is thought that Deep Learning arose in the 80’s, using a model which had 5 or 6 layers. It was (and is) called the neocognitron and was created by the Japanese researcher Kunihiki Fukushima. Neural networks are very effective in identifying patterns.

An example worth highlighting of the application of Deep Learning is the project carried out by Google and the Universities of Stanford and Massachusetts. It aimed to improve the natural language processing techniques of a type of AI called Recurrent Neural Network Language Model (RNNLM). It’s used for automatic translations and creating subtitles, among other thing. Basically, it builds up phrases word by words, basing each word on the previous one and in this way, it can even write poems.

Module 1

1. Introduction to Deep Learning and TensorFlow

2. Basics of Neural Networks

3. Designing a shallow neural network (Scratch and python) (Project)

4. Deeper neural network using TensorFlow. (Project)

Who this course is for:

• Beginners In Python
• Beginners In Deep Learning
• Beginners In Machine Learning

Course content

What you’ll learn

RNN

LSTM

GRU

NLP

Seq2Seq

Attention

Time series

Requirements

• Python
• Numpy
• Tensorflow or keras
• Feed Forward Neural Networks
• Back Propagation

Description

This is a preview to the exciting Recurrent Neural Networks course that will be going live soon. Recurrent Networks are an exciting type of neural network that deal with data that come in the form of a sequence. Sequences are all around us such as sentences, music, videos, and stock market graphs. And dealing with them requires some type of memory element to remember the history of the sequences, this is where Recurrent Neural networks come in.

We will be covering topics such as RNNs, LSTMs, GRUs, NLP, Seq2Seq, attention networks and much much more.

You will also be building projects, such as a Time series Prediction, music generator, language translation, image captioning, spam detection, action recognition and much more.

Building these projects will impress even the most senior machine learning developers; and will prepare you to start tackling your own deep learning projects with real datasets to show off to your colleagues or even potential employers.

Sequential Networks are very exciting to work with and allow for the creation of very intelligent applications. If you’re interested in taking your machine learning skills to the next level, then this course is for you!

Who this course is for:

• machine learning developers
• Data Scientiests

Course content

Neural Network Fundamentals

Requirements

• Be able to code in Python with NumPy and Pandas

Description

Wanna understand deep learning and neural networks so well, you could code them from scratch? In this course, we’ll do exactly that.

The course starts by motivating and explaining perceptrons, and then gradually works its way toward deriving and coding a multiclass neural network with stochastic gradient descent that can recognize hand-written digits from the famous MNIST dataset.

Course Goals

This course is all about understanding the fundamentals of neural networks. So, it does not discuss TensorFlow, PyTorch, or any other neural network libraries. However, by the end of this course, you should understand neural networks so well that learning TensorFlow and PyTorch should be a breeze!

Challenges

In this course, I present a number of coding challenges inside the video lectures. The general approach is, we’ll discuss an idea and the theory behind it, and then you’re challenged to implement the idea / algorithm in Python. I’ll discuss my solution to every challenge, and my code is readily available on github.

Prerequisites

In this course, we’ll be using Python, NumPy, Pandas, and good bit of calculus. ..but don’t let the math scare you. I explain everything in great detail with examples and visuals.

If you’re rusty on your NumPy or Pandas, check out my free courses Python NumPy For Your Grandma and Python Pandas For Your Grandpa.

Who this course is for:

• People interested in learning how neural networks work

Course content

Basics of Image Processing for deep learning using tensorflow

Image Classification

Tensorflow

Machine Learning

Deep Learning

Neural Networks

Requirements

• Basics of Python 3 programming

Description

Want to dive into Deep Learning and can’t find a simple yet comprehensive course?

Don’t worry you have come to the right place.

We provide easily digestible lessons with plenty of programming question to fill your coding appetite. All topic are thoroughly explained and NO MATH BACKGROUND IS NEEDED. This class will give you a head start among your peers.

This class contains fundamentals of Image Classification with Tensorflow.

This course will teach you everything you need to get started.

Who this course is for:

• Data Scientists
• If you have some Knowledge about Python and want to explore Deep learning
• Beginner python developer curious about Data Science

Course content