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https://github.com/idouble/deep-learning-machine-learning-ai-tensorflow-python

🐍 A Collection of Notes for Learning & Understanding Deep Learning / Machine Learning / Artificial Intelligence (AI) with TensorFlow 🐍
https://github.com/idouble/deep-learning-machine-learning-ai-tensorflow-python

ai artificial artificial-intelligence artificial-neural-networks biases deep-learning deep-neural-networks inputs machine-learning machine-learning-algorithms multidimensional-arrays neural-networks outputs python summation tensor tensorflow tensorflow-examples understanding-neural-networks weights

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🐍 A Collection of Notes for Learning & Understanding Deep Learning / Machine Learning / Artificial Intelligence (AI) with TensorFlow 🐍

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# 🐍 Deep Learning / Machine Learning / Artificial Intelligence (AI) TensorFlow Python 🐍

🐍 **Deep Learning / Machine Learning / Artificial Intelligence (AI)** with **TensorFlow** 🐍



Learning & Understanding **Deep Learning / Machine Learning / Artificial Intelligence (AI)**.

I tried to keep it as short as possible, but Truth needs to be told, **Deep Learning / Machine Learning and Artificial Intelligence (AI)** are big topics. 

In this Repository, the focus is mainly on **TensorFlow** and **Deep Learning** with **neural networks**.





  




## What is a neural network? 🌐



A basic **neural network** consists of an **input layer**, which is just **your data, in numerical form**. After your **input layer**, you will have some number of what are called **"hidden" layers**. **A hidden layer** is just in between your input and output layers. ***One hidden layer means you just have a neural network. Two or more hidden layers? you've got a deep neural network!***



![neural network](Images/artificial-neural-network-model.png)



## What is a Tensor? 🔢



Each operation takes a **Tensor** as an Input and outputs a **Tensor**. A **Tensor** is how Data is represented in **TensorFlow**.

A **Tensor is a multidimensional array** ex: 

[0.245,0.618,0.382]

This would be a **normalized one-way-tensor**.

[[0.245,0.618,0.382], [0.618,0.382,0.245], [0.382,0.245,0.618]] 

This would be a **normalized two-way-tensor**.

[[[0.245,0.618,0.382],[0.618,0.382,0.245],[0.382,0.245,0.618]], [[0.245,0.618,0.382],[0.618,0.382,0.245],[0.382,0.245,0.618]], [[0.245,0.618,0.382],[0.618,0.382,0.245],[0.382,0.245,0.618]]] 

This would be a **normalized three-way-tensor**.

**normalized** in **TensorFlow** means that the numbers are converted to a value between 0 and 1. The Data needs to be **normalized**, to be actually useable in **TensorFlow**.



![neural network](Images/tensor.png)



## Hyper Parameters 🔡



**Hyperparameters** contain the data that govern the training process itself. 



As an ex. if the **learning rate** is too big, our model may skip the optimal solution, if the **learning rate** is too small we may need to many iterations to get the best result, so we try to find a **learning rate** that fits for our purpose.



![hyper parameters](Images/learning_rate.png)



## What are Weights and Biases? 🔤



**Weights** and **Biases** are the **learnable parameters of your model**. As well as **neural networks**, they appear with the same names in related models such as linear regression. Most machine learning algorithms include some **learnable parameters** like this.



![explained picture machine learning](Images/Overview_Explained_Example.png)



## 📝 Example Code with Comments 📝

```

import tensorflow as tf  # deep learning library. Tensors are just multi-dimensional arrays



mnist = tf.keras.datasets.mnist  # mnist is a dataset of 28x28 images of handwritten digits and their labels

(x_train, y_train),(x_test, y_test) = mnist.load_data()  # unpacks images to x_train/x_test and labels to y_train/y_test



x_train = tf.keras.utils.normalize(x_train, axis=1)  # scales data between 0 and 1

x_test = tf.keras.utils.normalize(x_test, axis=1)  # scales data between 0 and 1



model = tf.keras.models.Sequential()  # a basic feed-forward model

model.add(tf.keras.layers.Flatten())  # takes our 28x28 and makes it 1x784

model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))  # a simple fully-connected layer, 128 units, relu activation

model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))  # a simple fully-connected layer, 128 units, relu activation

model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))  # our output layer. 10 units for 10 classes. Softmax for probability distribution



model.compile(optimizer='adam',  # Good default optimizer to start with

              loss='sparse_categorical_crossentropy',  # how will we calculate our "error." Neural network aims to minimize loss.

              metrics=['accuracy'])  # what to track



model.fit(x_train, y_train, epochs=3)  # train the model



val_loss, val_acc = model.evaluate(x_test, y_test)  # evaluate the out of sample data with model

print(val_loss)  # model's loss (error)

print(val_acc) # model's accuracy

```

## Resources & Links: ⛓

https://www.tensorflow.org/ 

https://ai.google/education/ 

Deep Learning: https://pythonprogramming.net/introduction-deep-learning-python-tensorflow-keras/ 

TensorFlow Overview: https://www.youtube.com/watch?v=2FmcHiLCwTU 

AI vs Machine Learning vs Deep Learning: https://www.youtube.com/watch?v=WSbgixdC9g8 

https://www.quora.com/What-do-the-terms-Weights-and-Biases-mean-in-Google-TensorFlow 

https://datascience.stackexchange.com/questions/19099/what-is-weight-and-bias-in-deep-learning



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