Home » Best Tensorflow Courses on Pluralsight

Best Tensorflow Courses on Pluralsight

Spread the love
Best Tensorflow Courses on Pluralsight
Best Tensorflow Courses on Pluralsight

Technology teams are only as successful as their skills are relevant. With access to expert-authored courses and content, skill assessments and analytics, Pluralsight Skills gives you the most effective path to building business-critical skills.

When you sign up, you’ll immediately have unlimited viewing of thousands of expert courses, paths to guide your learning, tools to measure your skills and hands-on resources like exercise files. There’s no limit on what you can watch unlimited courses, learn and you can cancel at any time within 10 days of free trial.

Getting Started with Tensorflow 2.0

This course focuses on introducing the TensorFlow 2.0 framework – exploring the features and functionality that it offers for building and training neural networks. This course discusses how TensorFlow 2.0 differs from TensorFlow 1.x and how the use of the Keras high-level API and eager execution makes TensorFlow 2.0 very easy to work with even for complex models.

First, you will explore the basic features of TensorFlow 2.0 and how its programming model differs from TensorFlow 1.x versions. You will understand the basic working of a neural network and its active learning unit, the neuron.

Next, you will compare and contrast static and dynamic computation graphs and understand the advantages and disadvantages of working with each kind of graph. You will get hands-on exploring execution in TensorFlow 2.0 in eager execution mode and harness the performance efficiencies of static graphs by using the tf.function decorator to decorate ordinary Python functions.

You will then learn how a neural network is trained using gradient descent optimization and how the GradientTape() library in TensorFlow calculates gradients automatically during the training phase of your neural network model.

Finally, you will learn how different APIs in Keras lend themselves to different use-cases. Sequential models consisting of layers stacked one on top of the other are simple and have long been supported by Keras. You will also explore the Functional API and model subclassing in Keras and then use these APIs to build regression as well as classification models

When you’re finished with this course, you will have the skills and knowledge to harness the computational power of the TensorFlow 2.0 framework and choose between the different model-building strategies available in Keras.

Best for: Beginners | Total Duration: 3:10 hrs | Provider: Pluralsight

Pricing: Free for 10 days, $17/month

Info: Visit this Course and Complete this course with your free trial in 3hours

Build, Train, and Deploy Your First Neural Network with TensorFlow 2

In this course, you will learn the basic principles of machine learning and neural networks so you can quickly create, train, and deploy a neural network with TensorFlow.

In this course, Build, Train, and Deploy Your First Neural Network with TensorFlow 2, you will learn the foundational knowledge needed to create your own neural networks. First, you will explore the basic principles of how machine learning lets us create models that learn from data.

Next, you will discover how to apply these principles to neural networks and create a model that predicts the class of clothing in an image. Then, you will delve into how TensorFlow makes it easy to evaluate and improve the performance of neural networks with built-in tools like TensorBoard.

Finally, you will learn how to deploy your neural network and make its predictive power available to client applications. When you are finished with this course, you will have the skills and knowledge of machine learning and TensorFlow needed to create, train, and deploy a predictive neural network.

Best for: Beginners | Total Duration: 2 hr:46mins | Provider: Pluralsight

Pricing: Free for 10 days, $17/month

Info: View this course in your free trial of 10 days

Implementing Image Recognition Systems with TensorFlow 1

In this course, Implementing Image Recognition Systems with TensorFlow 1, 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.

Best for: INtermediate | Total Duration: 1 hr:56mins | Provider: Pluralsight

Pricing: Free for 10 days, $17/month

Info: Visit this course and complete this Course on your free trial time free of cost

Designing Data Pipelines with TensorFlow 2.0

TensorFlow 2.0 has made it easier to manage data pipelines with tf.data through their simplified and unified interface. In this course, Designing Data Pipelines with TensorFlow 2.0, you’ll learn to leverage the performance improvements from the TensorFlow data module.

First, you’ll discover how to load data into TensorFlow.

Next, you’ll explore prepping data for model training and feature engineering.

Finally, you’ll learn how to leverage the performance optimizations of the data pipeline. When you’re finished with this course, you’ll have the skills and knowledge of building data pipelines needed to have data ready for model training in TensorFlow.

Best for: Intermediate | Total Duration: 1 hr:53mins | Provider: Pluralsight

Pricing: Free for 10 days, $17/month

Info: Visit this course and view this course for a free trial of 10 days

Debugging and Monitoring TensorFlow Programs

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.

Best for: Intermediate | Total Duration: 2hr:17mins | Provider: Pluralsight

Pricing: Free for 10 days, $17/month

Info: Visit this Course and View this course for free with your free trial of 10 days.

Build a Machine Learning Workflow with Keras TensorFlow 2.0

In this course, Build a Machine Learning Workflow with Keras Tensorflow 2.0, you will see how to harness the combination of the Keras APIs and the underlying power of TensorFlow 2.0

First, you will learn how different APIs in Keras lend themselves to different use cases, like sequential models consisting of stacked layers, high-level APIs contained in tf.keras, and the first-class support for TensorFlow-specific functionality.

Next, you will discover how more complex types of models can be constructed using the functional API which is designed to create callable models – a change from the usual, object-oriented paradigm underlying most deep learning models.

Finally, you will explore how model subclassing is implemented in Keras – which is a great way of implementing the forward pass of a model imperatively, how custom layers work – which offer a high level of flexibility and can be used to define layers that hold state, and best practices that will help you get the most out of your custom layers.

When you are finished with this course, you will have the skills and knowledge to choose between the many different model-building strategies available in Keras, and to use the appropriate strategy to build a robust model that leverages the underlying power of TensorFlow 2.0.

Best for: Intermediate | Total Duration: 3hr:15mins | Provider: Pluralsight

Pricing: Free for 10 days, $17/month

Info: Visit this Course and view this course wiht your free trial time for 10 days

1 thought on “Best Tensorflow Courses on Pluralsight”

  1. Pingback: Best Tensorflow Courses on Udemy Beginners, Certification, Advance -

Leave a Reply

Your email address will not be published.