Deep Learning with Python: Neural Networks from Scratch

AI Is Math and Data, Not Magic

Deep learning seems like sorcery from outside. Inside, it is linear algebra, calculus, statistics, and lots of data. You do not need a PhD to start.

TensorFlow/Keras Example

import tensorflow as tf
from tensorflow import keras

# Load MNIST digits
(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()
X_train = X_train.astype("float32") / 255.0

# Build model
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation="relu"),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(10, activation="softmax")
])

model.compile(optimizer="adam",
              loss="sparse_categorical_crossentropy",
              metrics=["accuracy"])

model.fit(X_train, y_train, epochs=5, validation_split=0.1)
test_loss, test_acc = model.evaluate(X_test, y_test)
print(f"Test accuracy: {test_acc:.2%}")

Neural Network Types

Type	Best For
Dense/MLP	Tabular data, classification
CNN	Images, computer vision
RNN/LSTM	Text, time series
Transformer	NLP, GPT, BERT

Learning Path

1. Python + NumPy basics
2. Statistics fundamentals
3. Scikit-learn (traditional ML)
4. Andrew Ng Deep Learning Specialization
5. TensorFlow or PyTorch projects
6. Kaggle competitions for practice