🐍 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.
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!
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.
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.
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.
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
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