diff --git a/.idea/PyTorch.iml b/.idea/PyTorch.iml
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diff --git a/.idea/editor.xml b/.idea/editor.xml
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diff --git a/NoTorch/BookExamples/BasicExample.py b/NoTorch/BookExamples/BasicExample.py
new file mode 100644
index 0000000..c4bc043
--- /dev/null
+++ b/NoTorch/BookExamples/BasicExample.py
@@ -0,0 +1,29 @@
+import numpy as np
+
+#inputs * weights + biases = predictions
+inputs = [[1.0, 2.0, 3.0, 2.5],
+ [2.0, 5.0, -1.0, 2.0],
+ [-1.5, 2.7, 3.3, -0.8]]
+
+weights = [[0.2, 0.8, -0.5, 1.0],
+ [0.5, -0.91, 0.26, -0.5],
+ [-0.26, -0.27, 0.17, 0.87]]
+
+biases = [2.0, 3.0, 0.5]
+
+#adding more sets of weights and biases creates more layers for our neural network
+weights2 = [[0.1, -0.14, 0.5],
+ [-0.5, 0.12, -0.33],
+ [-0.44, 0.73, -0.13]]
+
+biases2 = [-1, 2, -0.5]
+
+#take dot product of the two matrices and add biases (transpose so that we can do matrix multiplication)
+layer_outputs = np.dot(inputs, np.array(weights).T) + biases
+
+#repeat dot product but instead with the output matrix from layer1 and the second layer of weights and biases
+layer_outputs2 = np.dot(layer_outputs, np.array(weights2).T) + biases2
+print(layer_outputs2)
+
+
+
diff --git a/NoTorch/BookExamples/SpiralDataExample.py b/NoTorch/BookExamples/SpiralDataExample.py
new file mode 100644
index 0000000..a6d5e5f
--- /dev/null
+++ b/NoTorch/BookExamples/SpiralDataExample.py
@@ -0,0 +1,22 @@
+from nnfs.datasets import spiral_data
+import numpy as np
+import nnfs
+import matplotlib.pyplot as plt
+
+#sets random seed to zero, float 32 dtype default, overrides original dot product from numpy
+nnfs.init()
+
+class Layer_Dense:
+
+ #layer initialization
+ def __init__(self, n_inputs, n_neurons):
+ #random initialization of weights and biases
+ self.weights = 0.01 * np.random.randn(n_inputs, n_neurons)
+ self.biases = np.zeros((1, n_neurons))
+
+
+ #forward pass
+ def forward(self, inputs):
+ #calculate outputs from inputs weights and biases
+ self.output = np.dot(inputs, self.weights) + self.biases
+
diff --git a/NoTorch/main.py b/NoTorch/main.py
deleted file mode 100644
index e69de29..0000000