Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 2&2 faq

instructor Instructor: Daniel Bourke instructor-icon
duration Duration: 4.00 duration-icon

This course provides an introduction to TensorFlow and Deep Learning fundamentals with Python. In Part 2, learners will explore non-linearity, building their first neural network with a non-linear activation function. They will also learn about the importance of data pre-processing, the concept of overfitting, and how to use TensorFlow to build a deep learning model. Finally, learners will be able to evaluate their model and make predictions.

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Course Feature Course Overview Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Free

providerProvider:

Youtube

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

On-Demand

Course Overview

❗The content presented here is sourced directly from Youtube platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [February 21st, 2023]

What does this course tell?
(Please note that the following overview content is from the original platform)


- Intro/hello/have you watched part 1? If not, you should.
- 66. Non-linearity part 1 (straight lines and non-straight lines).
- 67. Non-linearity part 2 (building our first neural network with a non-linear activation function).
- 68. Non-linearity part 3 (upgrading our non-linear model with more layers).
- 69. Non-linearity part 4 (modelling our non-linear data).
- 70. Non-linearity part 5 (reproducing our non-linear functions from scratch).
- 71. Getting great results in less time by tweaking the learning rate.
- 72. Using the history object to plot a model’s loss curves.
- 73. Using callbacks to find a model’s ideal learning rate.
- 74. Training and evaluating a model with an ideal learning rate.
- [Keynote] 75. Introducing more classification methods.
- 76. Finding the accuracy of our model.
- 77. Creating our first confusion matrix.
- 78. Making our confusion matrix prettier.
- 79. Multi-class classification part 1 (preparing data).
- 80. Multi-class classification part 2 (becoming one with the data).
- 81. Multi-class classification part 3 (building a multi-class model).
- 82. Multi-class classification part 4 (improving our multi-class model).
- 83. Multi-class classification part 5 (normalised vs non-normalised).
- 84. Multi-class classification part 6 (finding the ideal learning rate).
- 85. Multi-class classification part 7 (evaluating our model).
- 86. Multi-class classification part 8 (creating a confusion matrix).
- 87. Multi-class classification part 9 (visualising random samples) .
- 88. What patterns is our model learning?.


We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
This course provides an introduction to TensorFlow and Deep Learning fundamentals with Python. Learners will learn how to build a non-linear model with a non-linear activation function, how to tweak the learning rate to get great results in less time, and how to use callbacks to find a model's ideal learning rate. They will also learn about multi-class classification and how to evaluate and visualise the results.

Possible Development Paths include becoming a data scientist, machine learning engineer, or deep learning engineer. Learners can also pursue further education in the field of artificial intelligence, computer science, or mathematics.

Learning Suggestions for learners include taking courses in related subjects such as linear algebra, calculus, and probability theory. Learners should also practice coding in Python and familiarise themselves with the TensorFlow library. Additionally, they should read up on the latest developments in the field of deep learning and artificial intelligence.

[Applications]
Upon completion of this course, learners should be able to apply the concepts of TensorFlow and Deep Learning fundamentals with Python to their own projects. Learners should be able to use non-linearity to build their own neural networks, tweak the learning rate to get great results in less time, use the history object to plot a model's loss curves, use callbacks to find a model's ideal learning rate, train and evaluate a model with an ideal learning rate, find the accuracy of a model, create a confusion matrix, make a confusion matrix prettier, prepare data for multi-class classification, build a multi-class model, improve a multi-class model, find the ideal learning rate for a multi-class model, evaluate a multi-class model, create a confusion matrix for a multi-class model, and visualise random samples to understand what patterns the model is learning.

[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use TensorFlow and other deep learning frameworks to build and optimize models for various tasks. They also need to be able to interpret and explain the results of their models. The demand for Machine Learning Engineers is growing rapidly as more companies are looking to leverage the power of AI and machine learning.

2. Data Scientist: Data Scientists use TensorFlow and other deep learning frameworks to analyze large datasets and uncover insights. They need to be able to interpret and explain the results of their models and develop strategies to improve the accuracy of their models. Data Scientists are in high demand as companies are looking to leverage the power of AI and machine learning to gain a competitive edge.

3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for developing and deploying AI-based solutions. They use TensorFlow and other deep learning frameworks to build and optimize models for various tasks. They also need to be able to interpret and explain the results of their models. The demand for Artificial Intelligence Engineers is growing rapidly as more companies are looking to leverage the power of AI and machine learning.

4. Deep Learning Researcher: Deep Learning Researchers are responsible for researching and developing new deep learning algorithms and architectures. They use TensorFlow and other deep learning frameworks to build and optimize models for various tasks. They also need to be able to interpret and explain the results of their models. The demand for Deep Learning Researchers is growing rapidly as more companies are looking to leverage the power of AI and machine learning.

Course Provider

Provider Youtube's Stats at AZClass

Over 100+ Best Educational YouTube Channels in 2023.
Best educational YouTube channels for college students, including Crash Course, Khan Academy, etc.
AZ Class hope that this free Youtube course can help your Tensorflow skills no matter in career or in further education. Even if you are only slightly interested, you can take Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 2&2 course with confidence!

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