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Become a TensorFlow Certified Professional Developer

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Become a TensorFlow Certified Professional Developer, available at $199.99, has an average rating of 5, with 155 lectures, based on 1 reviews, and has 88 subscribers.

You will learn about Understand Deep Learning Fundamentals Construct three different deep learning models using TensorFlow and Keras Classify images using convolutional neural networks (CNNs) in TensorFlow. Apply image augmentation and transfer learning to enhance model performance. Utilize strategies to prevent overfitting, including augmentation and dropout. Process text through tokenization and sentence vector representation. Apply Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks to NLP tasks Create Device-Based Models with TensorFlow Lite This course is ideal for individuals who are Data Scientists who simply want to learn how to use TensorFlow at an advanced level. or Data Scientists who want to pass the TensorFlow Developer Certification. or AI Practitioners who want to build more powerful AI models using TensorFlow. or Tech enthusiasts who are passionate about AI and want to gain real-world practical experience with TensorFlow. It is particularly useful for Data Scientists who simply want to learn how to use TensorFlow at an advanced level. or Data Scientists who want to pass the TensorFlow Developer Certification. or AI Practitioners who want to build more powerful AI models using TensorFlow. or Tech enthusiasts who are passionate about AI and want to gain real-world practical experience with TensorFlow.

Enroll now: Become a TensorFlow Certified Professional Developer

Summary

Title: Become a TensorFlow Certified Professional Developer

Price: $199.99

Average Rating: 5

Number of Lectures: 155

Number of Published Lectures: 154

Number of Curriculum Items: 155

Number of Published Curriculum Objects: 154

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Understand Deep Learning Fundamentals
  • Construct three different deep learning models using TensorFlow and Keras
  • Classify images using convolutional neural networks (CNNs) in TensorFlow.
  • Apply image augmentation and transfer learning to enhance model performance.
  • Utilize strategies to prevent overfitting, including augmentation and dropout.
  • Process text through tokenization and sentence vector representation.
  • Apply Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks to NLP tasks
  • Create Device-Based Models with TensorFlow Lite

Who Should Attend

  • Data Scientists who simply want to learn how to use TensorFlow at an advanced level.
  • Data Scientists who want to pass the TensorFlow Developer Certification.
  • AI Practitioners who want to build more powerful AI models using TensorFlow.
  • Tech enthusiasts who are passionate about AI and want to gain real-world practical experience with TensorFlow.

Target Audiences

  • Data Scientists who simply want to learn how to use TensorFlow at an advanced level.
  • Data Scientists who want to pass the TensorFlow Developer Certification.
  • AI Practitioners who want to build more powerful AI models using TensorFlow.
  • Tech enthusiasts who are passionate about AI and want to gain real-world practical experience with TensorFlow.

In this course you will learn everything you need to know to master the TensorFlow Developer Certification.

We will start by studying Deep Learning in depth so that you can understand how artificial neural networks work and learn. And while covering the Deep Learning theory we will also build together three different Deep Learning models in TensorFlow and Keras, from scratch, step by step, and coding every single line of code together.

Then, we will move on to Computer Vision, where you will learn how to classify images using convolutions with TensorFlow. You will also learn some techniques such as image augmentation and transfer learning to get even more performance in your computer vision tasks. And we will practice all this on real-world image data, while exploring strategies to prevent overfitting, including augmentation and dropout.

Then, you will learn how to use JavaScript, in order to train and run inference in a browser, handle data in a browser, and even build an object classification and recognition model using a webcam.

Then you will learn how to do Natural Language Processing using TensorFlow. Here we will build natural language processing systems, process text including tokenization and representing sentences as vectors, apply RNNs, GRUs, and LSTMs in TensorFlow, and train LSTMs on existing text to create original poetry and more.

And finally, you will also learn how to build Device-based Models with TensorFlow Lite. In this last part we will prepare models for battery-operated devices, execute models on Android and iOS platforms, and deploy models on embedded systems like Raspberry Pi and microcontrollers.

Who this course is for:

The course is targeted towards AI practitioners, aspiring data scientists, Tech enthusiasts, and consultants wanting to pass the TensorFlow Developer Certification. Here’s a list of who is this course for:

  • Data Scientists who simply want to learn how to use TensorFlow at an advanced level.

  • Data Scientists who want to pass the TensorFlow Developer Certification.

  • AI Practitioners who want to build more powerful AI models using TensorFlow.

  • Tech enthusiasts who are passionate about AI and want to gain real-world practical experience with TensorFlow.

Course Prerequisites:

Basic knowledge of programming is recommended. Some experience in Machine Learning is also preferable. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to anyone with basic programming knowledge. Students who enrol in this course will master data science fundamentals and directly apply these skills to solve real world challenging business problems.

*Terms & Conditions of Exam Guarantee:

Ligency Ventures Pty Ltd, U.K provides the following guarantee for the TensorFlow Developer Professional Certificate Course:

If you take your TensorFlow Developer Certificate exam within 30 days of enrolling and completing this course 100% and you sit the exam and receive a score above zero, but below the minimum score required to pass the exam, then Ligency Ventures Pty Ltd, U.K will pay for your second exam attempt provided the following conditions are met: you paid at least $1 for this course and it was not refunded, AND before sitting the exam, you diligently watched and followed along with all of the tutorials in the course (completed all case studies and have all codes under your Google Colab account), AND you completed all practical activities including but not limited to challenges within the sections, quizzes, homework exercises and all provided practice exams.

Ligency Ventures Pty Ltd may request evidence of fulfilling the above conditions, thereby it’s important that you save your work when taking the course and doing the practical assignments.

Course Curriculum

Chapter 1: Part 0: Introduction To The Course

Lecture 1: Introduction to the Course

Lecture 2: Contact and Questions

Chapter 2: Part 1: Artificial Neural Networks

Lecture 1: Intro

Lecture 2: Get course materials

Lecture 3: Plan of Attack

Lecture 4: Functioning of the Human Neuron

Lecture 5: How Neural Networks Work?

Lecture 6: Activation Function

Lecture 7: How Neural Networks Learn?

Lecture 8: Gradient Descent

Lecture 9: Stochastic Gradient Descent

Lecture 10: Back-Propagation

Lecture 11: Build an ANN with TensorFlow in 5 Steps From Scratch – Step 1

Lecture 12: Build an ANN with TensorFlow in 5 Steps From Scratch – Step 2

Lecture 13: Build an ANN with TensorFlow in 5 Steps From Scratch – Step 3

Lecture 14: Build an ANN with TensorFlow in 5 Steps From Scratch – Step 4

Lecture 15: Build an ANN with TensorFlow in 5 Steps From Scratch – Step 5

Chapter 3: Part 2: Convolutional Neural Networks

Lecture 1: Intro

Lecture 2: Plan of Attack

Lecture 3: What are Convolutional Neural Networks

Lecture 4: Step 1: The Convolution Operation

Lecture 5: Step 1 (Part B): ReLU Layer

Lecture 6: Step 2: Pooling

Lecture 7: Step 3: Flattening

Lecture 8: Step 4: Full Connection

Lecture 9: Summary

Lecture 10: Softmax Activation Function & Cross-Entropy Loss Function

Lecture 11: Build a CNN with TensorFlow in 5 Steps From Scratch – Step 1

Lecture 12: Build a CNN with TensorFlow in 5 Steps From Scratch – Step 2

Lecture 13: Build a CNN with TensorFlow in 5 Steps From Scratch – Step 3

Lecture 14: Build a CNN with TensorFlow in 5 Steps From Scratch – Step 4

Lecture 15: Build a CNN with TensorFlow in 5 Steps From Scratch – Step 5

Lecture 16: Demo

Chapter 4: Part 3: Recurrent Neural Networks

Lecture 1: Intro

Lecture 2: Plan of Attack

Lecture 3: Recurrent Neural Networks

Lecture 4: Vanishing Gradient Problem

Lecture 5: LSTMs and How They Work

Lecture 6: Practical Intuition

Lecture 7: LSTM Variations

Lecture 8: Build a RNN with TensorFlow in 15 steps from scratch – Step 1

Lecture 9: Build a RNN with TensorFlow in 15 steps from scratch – Step 2

Lecture 10: Build a RNN with TensorFlow in 15 steps from scratch – Step 3

Lecture 11: Build a RNN with TensorFlow in 15 steps from scratch – Step 4

Lecture 12: Build a RNN with TensorFlow in 15 steps from scratch – Step 5

Lecture 13: Build a RNN with TensorFlow in 15 steps from scratch – Step 6

Lecture 14: Build a RNN with TensorFlow in 15 steps from scratch – Step 7

Lecture 15: Build a RNN with TensorFlow in 15 steps from scratch – Step 8

Lecture 16: Build a RNN with TensorFlow in 15 steps from scratch – Step 9

Lecture 17: Build a RNN with TensorFlow in 15 steps from scratch – Step 10

Lecture 18: Build a RNN with TensorFlow in 15 steps from scratch – Step 11

Lecture 19: Build a RNN with TensorFlow in 15 steps from scratch – Step 12

Lecture 20: Build a RNN with TensorFlow in 15 steps from scratch – Step 13

Lecture 21: Build a RNN with TensorFlow in 15 steps from scratch – Step 14

Lecture 22: Build a RNN with TensorFlow in 15 steps from scratch – Step 15

Chapter 5: Part 4: Intro to Computer Vision

Lecture 1: Intro

Lecture 2: Introduction to Computer Vision

Lecture 3: Code to Load Training Data For a Computer Vision Task

Lecture 4: Code a First Computer Vision Neural Network

Lecture 5: How to Use Callbacks to Control The Training

Chapter 6: Part 5: Mastering Convolutions

Lecture 1: Intro

Lecture 2: Dive deeper into convolutions

Lecture 3: Fashion classifier with more advanced convolutions

Lecture 4: New dataset with same more advanced convolutions and further improvement through

Chapter 7: Part 6: More Complex Images

Lecture 1: Intro

Lecture 2: ImageGenerator

Lecture 3: ConvNet to use on complex images and how to train it with fit_generator

Chapter 8: Part 7: More Real-World Images

Lecture 1: Intro

Lecture 2: Build and train the ConvNet for Real-World Images

Lecture 3: Automatic validation to test and improve the accuracy, as well as the impact of

Chapter 9: Part 8: Image Augmentation

Lecture 1: Intro

Lecture 2: Dive deeper into image augmentation

Lecture 3: Code gain the augmentation technique with ImageDataGenerator

Lecture 4: Add that to the cats vs. dogs dataset

Lecture 5: Do the same on the horses vs. humans dataset

Chapter 10: Part 9: Transfer Learning

Lecture 1: Intro

Lecture 2: Concept of transfer learning

Lecture 3: Transfer learning from the inception mode and use dropouts to reduce overfitting

Lecture 4: Code our own model by using transferred features

Chapter 11: Part 10: Multi-Class Classification

Lecture 1: Intro

Lecture 2: Moving from binary to multi-class classification and the Rock Paper Scissors dat

Lecture 3: Train a classifier with Rock Paper Scissors and test that same classifier

Chapter 12: Part 11: Computer Vision in JavaScript

Lecture 1: Intro

Lecture 2: Create a Convolutional Net with JavaScript

Lecture 3: Visualize the Training Process

Lecture 4: How to use the Sprite Sheet, and then tf.tidy() to Save Memory

Chapter 13: Part 12: Reusing Existing Models in JavaScript

Lecture 1: Intro

Instructors

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Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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