Building Machine Learning Solutions with Tensorflow

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Building Machine Learning Solutions with Tensorflow

Authors: Jerry Kurata, Janani Ravi, Vitthal Srinivasan, Jon Flanders, Justin Flett

TensorFlow is an open-source machine learning software library developed Google. Since it was released in 2015, it has become one of the most widely-used machine learning... Read more

What You Will Learn

  • Design and implementation of machine learning solutions using TensorFlow with Python
  • Applying Tensorflow to common analytical problems, such as classification, clustering, and regression
  • Debugging Tensorflow projects
  • Deploying Tensorflow projects to the cloud
  • Applying Tensorflow to more advanced problems spaces, such as image recognition, language modeling, and predictive analytics.

Pre-requisites

  • Machine Learning Literacy
  • Python Programming

Beginner

Build your first TensorFlow project, and create regression, classification, and clustering models.

TensorFlow: Getting Started

by Jerry Kurata

May 3, 2017 / 2h 38m

2h 38m

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Description

Developing sophisticated machine learning solutions is a difficult task. There are many processing steps that must be performed, and how this processing is performed is a function of not only the code you write, but also the data you use. In this course, TensorFlow: Getting Started, you'll see how TensorFlow easily addresses these concerns by learning TensorFlow from the bottom up. First, you'll be introduced to the installation process, building simple and advanced models, and utilizing additional libraries that make development even easier. Along the way, you'll learn how the unique architecture in TensorFlow lets you perform your computing on systems as small as a Raspberry Pi, and as large as a data farm. Finally, you'll explore using TensorFlow with neural networks in general, and specifically with powerful deep neural networks. By the end of this course, you'll have a solid foundation on using TensorFlow, and have the knowledge to apply TensorFlow to create your own machine learning solutions.

Table of contents
  1. Course Overview
  2. Introduction
  3. Introducing TensorFlow
  4. Creating Neural Networks in TensorFlow
  5. Debugging and Monitoring
  6. Transfer Learning with TensorFlow
  7. Extending TensorFlow with Add-ons
  8. Summary

Understanding the Foundations of TensorFlow

by Janani Ravi

Jul 26, 2017 / 2h 44m

2h 44m

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Description

In this course, Understanding the Foundations of TensorFlow, you'll learn the TensorFlow library from very first principles. First, you'll start with the basics of machine learning using linear regression as an example and focuses on understanding fundamental concepts in TensorFlow. Next, you'll discover how to apply them to machine learning, the concept of a Tensor, the anatomy of a simple program, basic constructs such as constants, variables, placeholders, sessions, and the computation graph. Then, you'll be introduced to TensorBoard, the visualization tool used to view and debug the data flow graphs. You'll work with basic math operations and image transformations to see how common computations are performed. Finally, you'll solve a real world machine learning problem using the MNIST handwritten dataset and the k-nearest-neighbours algorithm. By the end of this course, you'll have a better understanding of the foundations of TensorFlow.

Table of contents
  1. Course Overview
  2. Introducing TensorFlow
  3. Introducing Computation Graphs
  4. Digging Deeper into Fundamentals
  5. Working with Images
  6. Solving Basic Math Functions

Building Regression Models Using TensorFlow

by Vitthal Srinivasan

Jul 10, 2017 / 2h 39m

2h 39m

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Description

TensorFlow is all about building neural networks that can "learn" functions, and linear regression can be learnt by the simplest possible neural network - of just 1 neuron! In contrast, the XOR function requires 3 neurons arranged in 2 layers, and smart image recognition can require thousands of neurons. In this course, Building Regression Models using TensorFlow, you'll learn how the neurons in neural networks learn non-linear functions. First, you'll begin by learning functions such as XOR, and how to train different gradient descent optimizers. Next, you'll dive into the implications of choosing activation functions, such as softmax and ReLU. Finally, you'll explore the use of built-in estimators in Tensorflow. By the end of this course, you'll have a better understanding of how neurons "learn", and how neural networks in TensorFlow are set up and trained to execute operations such as regression and classification.

Table of contents
  1. Course Overview
  2. Learning Using Neurons
  3. Building Linear Regression Models Using TensorFlow
  4. Building Logistic Regression Models Using TensorFlow
  5. Building Generalized Linear Models Using Estimators

Building Classification Models with TensorFlow

by Janani Ravi

Oct 19, 2017 / 3h 15m

3h 15m

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Description

TensorFlow is a great way to implement powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. In this course, Building Classification Models with TensorFlow, you'll learn a variety of different machine learning techniques to build classification models. First, you'll begin by covering metrics, such as accuracy, precision, and recall that can be used to evaluate classification models and determine which metric is the right one for your use case. Next, you'll delve into more traditional machine learning techniques such as logistic regression and the k-nearest neighbor methods for classification. Finally, you'll discover how to implement more powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. By the end of this course, you'll have a better understanding of how to build classification models with TensorFlow.

Table of contents
  1. Course Overview
  2. Overview of Classification Models
  3. Simple Classification Models in TensorFlow
  4. Convolutional Neural Networks for Classification in TensorFlow
  5. Recurrent Neural Networks for Classification in TensorFlow

Building Unsupervised Learning Models with TensorFlow

by Janani Ravi

Oct 24, 2017 / 3h 2m

3h 2m

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Description

Unsupervised learning techniques are powerful, but under utilized and often not well understood. In this course, Building Unsupervised Learning Models with TensorFlow, you'll learn the various characteristics and features of clustering models such as K-means clustering and hierarchical clustering. First, you'll dive into building a k-means clustering model in TensorFlow. Next, you'll discover autoencoders in detail, which are a type of artificial neural network used for unsupervised learning. Finally, you'll explore encodings or representation of data for dimensionality reduction of problems. By the end of this course, you'll have a better understanding of how you can work with unlabeled data using unsupervised learning techniques.

Table of contents
  1. Course Overview
  2. Introduction to Unsupervised Learning
  3. Clustering Using Unsupervised Learning
  4. Understanding Neurons and Neural Networks
  5. Autoencoders Using Unsupervised Learning

Intermediate

Debug and deploy your TensorFlow model.

Debugging and Monitoring TensorFlow Programs

by Janani Ravi

Mar 21, 2018 / 2h 17m

2h 17m

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Description

An important facet of building good ML models is the ability to debug TensorFlow code when your models do not converge. Traditional debuggers fall short in this regard which is why tfdbg and TensorBoard are important skills in your toolkit.

In this course, Debugging and Monitoring TensorFlow Programs, you will learn how you can adapt TensorFlow commands and library functions to help debug your programs in addition to learning specialized tools like tfdbg and Tensorboard.

First, you will go over TensorFlow's special features to debug your code. Partial graph executions, tf.Print() and tf.Assert() statements, traditional Python debuggers and the tf.py_func() to interpose arbitrary Python code into your computation graph all help debug the graph build phase.

Next, you will see that the specialized TensorFlow debugger tfdbg works very much like traditional Python debuggers but has the ability to step into session.run() statements and display the state of your computation graph at every step. It also has filters like the has_inf_or_nan which allows you to break at the exact point your model begins to diverge.

Finally, you will be shown Tensorboard, which is a browser-based tool that helps you visualize your computation graph and view how control flows through your code. In addition, it can be used to display execution metrics and the current state of your program.

After finishing this course, you will be closer to mastering TensorFlow through equipping you with important tools to build and debug robust machine learning models.

Table of contents
  1. Course Overview
  2. Introducing TensorFlow Debugging Methods
  3. Applying tfdbg to Common Use-cases
  4. Visualizing TensorFlow Using TensorBoard

Deploying TensorFlow Models to AWS, Azure, and the GCP

by Janani Ravi

Apr 30, 2018 / 2h 11m

2h 11m

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Description

Deploying and hosting your trained TensorFlow model locally or on your cloud platform of choice - Azure, AWS or, the GCP, can be challenging. In this course, Deploying TensorFlow Models to AWS, Azure, and the GCP, you will learn how to take your model to production on the platform of your choice. This course starts off by focusing on how you can save the model parameters of a trained model using the Saved Model interface, a universal interface for TensorFlow models. You will then learn how to scale the locally hosted model by packaging all dependencies in a Docker container. You will then get introduced to the AWS SageMaker service, the fully managed ML service offered by Amazon. Finally, you will get to work on deploying your model on the Google Cloud Platform using the Cloud ML Engine. At the end of the course, you will be familiar with how a production-ready TensorFlow model is set up as well as how to build and train your models end to end on your local machine and on the three major cloud platforms. Software required: TensorFlow, Python.

Table of contents
  1. Course Overview
  2. Using TensorFlow Serving
  3. Containerizing TensorFlow Models Using Docker on Microsoft Azure
  4. Deploying TensorFlow Models on Amazon AWS
  5. Deploying TensorFlow Models on the Google Cloud Platform

Advanced

Use TensorFlow across an array of high-level cognitive computing problems, such as sentiment analysis, language modeling, image recognition, and predictive analytics.

Language Modeling with Recurrent Neural Networks in TensorFlow

by Janani Ravi

Mar 30, 2018 / 2h 35m

2h 35m

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Description

Recurrent Neural Networks (RNN) performance and predictive abilities can be improved by using long memory cells such as the LSTM and the GRU cell.

In this course, Language Modeling with Recurrent Neural Networks in Tensorflow, you will learn how RNNs are a natural fit for language modeling because of their inherent ability to store state. RNN performance and predictive abilities can be improved by using long memory cells such as the LSTM and the GRU cell.

First, you will learn how to model OCR as a sequence labeling problem.

Next, you will explore how you can architect an RNN to predict the next character based on past sequences.

Finally, you will focus on understanding advanced functions that the TensorFlow library offers, such as bi-directional RNNs and the multi-RNN cell.

By the end of this course, you will know how to apply and architect RNNs for use-cases such as image recognition, character prediction, and text generation; and you will be comfortable with using TensorFlow libraries for advanced functionality, such as the bidirectional RNN and the multi-RNN cell.

Table of contents
  1. Course Overview
  2. Applying Bidirectional Recurrent Neural Networks to Word Recognition
  3. Implementing Character Recognition Using Bidirectional RNNs
  4. Applying RNNs to Character Prediction for Text Generation
  5. Implementing RNNs for Character Prediction Used to Generate Text

Implementing Image Recognition Systems with TensorFlow

by Jon Flanders

Feb 4, 2019 / 1h 56m

1h 56m

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Description

Running images through deep learning models is potentially the most typical scenario in which deep learning is used today. In this course, Implementing Image Recognition Systems with TensorFlow, you will learn the basics of how to implement a solution for the most typical deep learning imaging scenarios. First, you will learn how to pick a TensorFlow model architecture if you can implement your solution with pre-existing, pre-trained models. Next, you will learn how to extend such models using your own training images by taking advantage of transfer learning. Finally, you will see how to use more advanced solutions to do more advanced processing on images, like segmentation, and even learn how to implement a facial recognition solution. When you are finished with this course, you will have the skills and knowledge of TensorFlow and imaging in order to implement your own solutions successfully.

Table of contents
  1. Course Overview
  2. Introduction
  3. Picking and Using a Model
  4. Transfer Learning
  5. Localization and Segmentation
  6. Face Recognition

Implementing Predictive Analytics with TensorFlow

by Justin Flett

Dec 31, 2018 / 1h 20m

1h 20m

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Description

Data Science and Machine Learning are rapidly growing fields that use scientific methods and processes to extract useful knowledge and insights from data. In this course, Implementing Predictive Analytics with TensorFlow, you will learn foundational knowledge of solving real-world data science problems. First, you will explore the basics of implementing supervised learning problems including linear regression and neural networks. Next, you will discover how recommendation systems can be implemented using TensorFlow. Finally, you will learn how to understand and implement reinforcement learning systems. When you are finished with this course, you will have the skills and knowledge of TensorFlow needed to solve data science and machine learning problems.

Table of contents
  1. Course Overview
  2. Implementing Supervised Learning Systems
  3. Implementing Recommendation Systems
  4. Implementing Reinforcement Learning Systems

Sentiment Analysis with Recurrent Neural Networks in TensorFlow

by Janani Ravi

Dec 20, 2017 / 2h 54m

2h 54m

Start Course
Description

Sentiment analysis and natural language processing are common problems to solve using machine learning techniques. Having accurate and good answers to questions without trudging through reviews requires the application of deep learning techniques such as neural networks. In this course, Sentiment Analysis with Recurrent Neural Networks in TensorFlow, you'll learn how to utilize recurrent neural networks (RNNs) to classify movie reviews based on sentiment. First, you'll discover how to generate word embeddings using the skip-gram method in the word2vec model, and see how this neural network can be optimized by using a special loss function, the noise contrastive estimator. Next, you'll delve into understanding RNNs and how to implement an RNN to classify movie reviews, and compare and contrast the neural network implementation with a standard machine learning model, the Naive Bayes algorithm. Finally, you'll learn how to implement the same RNN but with pre-built word embeddings. By the end of this course, you'll be able to understand and implement word embedding algorithms to generate numeric representations of text, and know how to build a basic classification model with RNNs using these word embeddings.

Table of contents
  1. Course Overview
  2. Applying Word Vector Embeddings to Language Modeling
  3. Implementing Word Embeddings in TensorFlow
  4. Performing Sequence Classification with RNNs
  5. Implementing Sequence Classification Using RNNs in TensorFlow
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