The Math behind Neural Networks | Forward Pass simplified for beginners | Deep Learning basics

👋 Welcome to our hands-on tutorial on neural networks! In this video, we dive into the math behind the forward pass of a neural network. 🎓✨

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📋 What We Cover:

Simple Neural Network Setup:
We start with a straightforward neural network architecture designed for a binary classification problem. Our network consists of an input layer with 3 neurons, a hidden layer with 4 neurons, and an output layer with a single neuron. The output can be either 0 or 1, representing our binary classification.

Weights & Representations:
We explain how weights are initialized and represented in a neural network. Each connection between neurons is associated with a weight, and understanding these weights is crucial for grasping how neural networks learn and make predictions.

Matrix Multiplication:
We explain the core calculations of the forward pass, starting with matrix multiplication. First, we perform matrix multiplication between the input values and the weights connecting the input layer to the hidden layer. We break down the process step-by-step to ensure clarity.
Next, we move on to the hidden layer and explain how the outputs from this layer are combined with weights to produce the final input to the output neuron. This involves another round of matrix multiplication, and we illustrate each step visually.

Activation Functions:
Activation functions play a critical role in neural networks, introducing non-linearity into the model. We apply the ReLU (Rectified Linear Unit) activation function at the hidden layer, explaining how it works.
At the output layer, we use the Sigmoid activation function, ideal for binary classification problems. We demonstrate how the Sigmoid function squashes the output into a range between 0 and 1, making it interpretable as a probability.

Prediction:
After computing the final output through the network, we generate a predicted value (ŷ). We then compare this predicted value with the actual label (y), which is 1 in our example. This comparison sets the stage for evaluating the performance of our network and making necessary adjustments during training.
🎨 The entire tutorial is delivered in a visually appealing way, with clear visuals and step-by-step explanations to make complex concepts more accessible. 🖼️👨‍🏫

👉 Next Part:
In the next part of this video series, we will cover the backpropagation process, showing how the weights are updated to improve the network's performance. Stay tuned for more insights and practical demonstrations! 🔄💡

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