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https://github.com/camara94/introduction_to_tensorflow_ai
TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning! You’re joining thousands of learners currently enrolled in the course. I'm excited to have you in the class and look forward to your contributions to the learning community.
https://github.com/camara94/introduction_to_tensorflow_ai
artificial-intelligence deep-learning jupyter-notebook machine-learning machine-learning-algorithms python tensorflow
Last synced: 5 days ago
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TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning! You’re joining thousands of learners currently enrolled in the course. I'm excited to have you in the class and look forward to your contributions to the learning community.
- Host: GitHub
- URL: https://github.com/camara94/introduction_to_tensorflow_ai
- Owner: camara94
- License: mit
- Created: 2022-02-20T05:43:01.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-03-11T18:18:19.000Z (almost 3 years ago)
- Last Synced: 2024-12-23T09:45:44.695Z (about 2 months ago)
- Topics: artificial-intelligence, deep-learning, jupyter-notebook, machine-learning, machine-learning-algorithms, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 28 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning! You’re joining thousands of learners currently enrolled in the course. I'm excited to have you in the class and look forward to your contributions to the learning community.
## Where To Find The Notebooks For This Course
All notebooks in this course can be run in either Google Colab or Coursera Labs. **You don’t need a local environment set up to follow the coding exercises**. You can simply click the **Open in Colab** badge at the top of the ungraded labs while for the assignments, you will be taken automatically to Coursera Labs.
However, if you want to run them on your local machine, the ungraded labs and assignments for each week can be found in this [Github](https://github.com/https-deeplearning-ai/tensorflow-1-public/tree/main/C1) repository under the **C1** folder. If you already have git installed on your computer, you can clone it with this command:
git clone https://github.com/https-deeplearning-ai/tensorflow-1-public
If not, please follow the guides [here](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) to install git on your operating system. Once you’ve cloned the repo, you can do a git pull once in a while to make sure that you get the latest updates to the notebooks.
## Package In Use
You will need these packages if you will run the notebooks locally:
tensorflow==2.7.0
scikit-learn==1.0.1
pandas==1.1.5
matplotlib==3.2.2
seaborn==0.11.2
## Traductionnal paradigm Programming
* If you are programming app
* or app for gaming
* We can represent that with this diagram
## Machine Learning Paradiagm Compare To Traductionnal paradigm Programming
## Activity Recognition
## Activity Recognition For Machine Learning
## Machine Learning
The machine learning is all about a computer learning the patterns that distinguish things.
For example:
Well, the answer is Y equals 2X minus 1. So whenever you see a Y, it's twice the corresponding X minus 1. If you figured it out for yourself, well done, but how did you do that? How would you think you could figure this out? Maybe you can see that the Y increases by 2 every time the X increases by 1. So it probably looks like Y equals 2X plus or minus something. Then when you saw X equals 0 and Y equals minus 1, so you thought hey that the something is a minus 1, so the answer might be Y equals 2X minus 1.
## How Can Implement That In Tensorflow
## Intro To Google Colab
[https://www.youtube.com/watch?v=inN8seMm7UI](https://www.youtube.com/watch?v=inN8seMm7UI)
## FAQs about Google Colab (optional)
As mentioned earlier, you can use Google Colab to follow along with the coding exercises in this course. The previous video showed a quick intro on how to use this environment and here are some [**Frequently Asked Questions**](https://research.google.com/colaboratory/faq.html) in case you want to know more.
## Week 1 Resources
That brings you to the end of what you need to look at for Week 1. If you’re eager to learn more, before we go to Week 2, there are some great resources you can check out:
* AI For Everyone is a non-technical course that will help you understand many of the AI technologies we will discuss later in this course, and help you spot opportunities in applying this technology to solve your problems. [https://www.deeplearning.ai/ai-for-everyone/](https://www.deeplearning.ai/ai-for-everyone/s)
* TensorFlow is available at TensorFlow.org, and video updates from the TensorFlow team are at [youtube.com/tensorflow](https://www.youtube.com/tensorflow)
Play with a neural network right in the browser at [http://playground.tensorflow.org](http://playground.tensorflow.org). See if you can figure out the parameters to get the neural network to pattern match to the desired groups. The spiral is particularly challenging!
## How To Load Dataset In Python
## Responsible AI practices
That link is more useful
* [https://ai.google/responsibilities/responsible-ai-practices/](https://ai.google/responsibilities/responsible-ai-practices/)
## Understant Sequential Layer in Tensorflow
* [https://keras.io/api/models/sequential/](https://keras.io/api/models/sequential/)
## Understant Flatten Layer in Tensorflow
* [https://keras.io/api/layers/reshaping_layers/flatten/](https://keras.io/api/layers/reshaping_layers/flatten/)
## Understant Flatten Dense in Tensorflow
[https://keras.io/api/layers/core_layers/dense/](https://keras.io/api/layers/core_layers/dense/)
## Understant what Activations in in Tensorflow
* [https://keras.io/api/layers/activations/](https://keras.io/api/layers/activations/)
### Function ReLu
* [https://keras.io/api/layers/activations/#relu-function](https://keras.io/api/layers/activations/#relu-function)
### Function Softmax
* [https://keras.io/api/layers/activations/#softmax-function](https://keras.io/api/layers/activations/#softmax-function)
* [https://www.youtube.com/watch?v=LLux1SW--oM](https://www.youtube.com/watch?v=LLux1SW--oM)
## Arrêter l'Entainement de mon Modèle
Cette fonction me permet d'arrêter l'entrainement de mon modèle lorsque j'atteint la performance souhaitée.
### Comment l'Utiliser
### Resouce on_epoch_end Callback
* [https://www.tensorflow.org/guide/keras/custom_callback](https://www.tensorflow.org/guide/keras/custom_callback)
## What Convolution Network Does ?
## How To Do Convolution Neural Network
## More Details For Conv2D
[https://www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF](https://www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF)
## Useful Link
* [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D)
* [https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D)
## Link To Better Understanding The MaxPooling
* [https://lodev.org/cgtutor/filtering.html](https://lodev.org/cgtutor/filtering.html)
## Image Generator Architecture
## Feature Of The Image Generator
One feature of the image generator you can pointe in one particular directory or sub directory that you generate your image.
ImageDataGenerator can flow images from a directory and perform operations such as resizing them on the fly
## How To Import Image Generator
## How To Instanciate the Image Generator
You can instanciate the **ImageGenerator** like that.
* Parameter **rescale**: it allow us to scale the pixels of image
* method **flow_from_directory()**: it allow us to load the images from a directory the name of the sub-directory will the label of the images.
>The common error the people make is to pointe this method to sub-directory.
* parameter **target_size**: it is a tuple that allow us to resize the images they are not a same size.
* parameter **batch_size**: it allows us to train size by size.
* parameter **class_mode**: it allows us to indicate for example label wil **binary** tow **classes**
## Code To detection After Use Image Generator
> **Note** when you use **binary** classifier the best **activation function** is **sigmoid**
## How It Look Like After Training
## Train The Model
## To Understang How To improve Our Look At This Video
[https://youtu.be/zLRB4oupj6g](https://youtu.be/zLRB4oupj6g)
## Train Step
* **train_generator**: stream images from the train director
* **steps_per_epoch**: we set step per epoch.
* **epochs**: numbers of epochs to train for
* **validation_data**: here we specify the validation step
* **validation_epochs**: we set step per epoch.
* **verbose**: to display how must to display
## Example On Colab
### Binary Crossentropy
* [https://gombru.github.io/2018/05/23/cross_entropy_loss/](https://gombru.github.io/2018/05/23/cross_entropy_loss/)
### Optimizer
* [https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
* [https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/RMSprop](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/RMSprop)
## Binary Classification
* [https://www.youtube.com/watch?v=eqEc66RFY0I&t=6s](https://www.youtube.com/watch?v=eqEc66RFY0I&t=6s)