Programming Books

Neural Network From Scratch In Python

Download free Introduction to Neural Networks from Scratch with Python for Beginners in PDF. This notes consists of Part A of a much larger, forth coming book “From o to Tensor Flow”. The aim of this much larger book is to get you up to speed with all you get to start on the deep learning journey.

From Starting To TensorFlow then Deep Learning. as we Know Deep learning is part of Machine learning Then the Basic Concept of Neural network and how we can create in python .

  This notes covers getting you up to speed up on the basic concept of neural networks and how to create them in python.

What is Artificial Neural Networks

Simple Definition of Artificial Neural network is software implementations of the neuronal structure of our brains. A neural network is like a computer program that learns from examples. It’s inspired by how our brains work. Imagine you want to teach a computer to recognize cats in pictures.

Notes  Neural Networks From Scratch in Python
Type  PDF 
Language English 
Addition  With Real-Time Examples 

Types of Artificial Neural Network 

You Learn These Topics From This Notes:


  • Explanation of neurons and layers
  • Intuition behind connections and weights
  • Input layer
  • Hidden layers
  • Output layer

2. Introduction to Neural Networks

  • The Structure of ANN
  • The Artificial Neuron
  • Nodes
  • The Bias

3. The Feed-Forward Pass

  • A Feed-Forward Example
  • Our First Attempt at a Feed-Forward Function
  • A More Efficient Implementation
  • Vectorization In Neural Network
  • Matrix Multiplication

4. Gradient Descent in Optimization

  • A Simple Example in Code
  • The Cost Function
  • Gradient Descent in Neural Network
  • A Two Dimensional Gradient Descent Example
  • Propagating in to the Hidden Layers 
  • Back Propagation in Depth
  • Implementation the Gradient Descent Step
  • The Final Gradient Descent Algorithm

5. Implementing the Neural Network in Python

  • Scaling Data
  • Creating Test and Training Datasets
  • Setting Up the Output Layers
  • Creating the Neural Network
  • Assessing the Accuracy of the Trained Model

6 . Fine-Tuning and Optimization

  • Experimenting with different hyperparameters
  • Implementing techniques like regularization, dropout, etc.




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