Deep Learning Fundamentals: From Basics to Implementation

This blog article summarizes a YouTube video transcript on deep learning fundamentals, covering essential concepts and preparing you for interviews. It explores the differences between AI, ML, DL, and Data Science, delves into core deep learning components, and provides practical insights.

Agenda: Deep Learning Fundamentals

This article will cover the following topics:

• Understanding Deep Learning
• Perceptron
• AI vs ML vs DL vs Data Science
• Forward Propagation and Backward Propagation
• Loss Function
• Activation Functions
• Optimizers
• Projects (covered in later sessions)

Prerequisites: Basic Python programming, familiarity with at least one Machine Learning algorithm, and a basic understanding of statistics.

AI vs. ML vs. DL vs. Data Science

Artificial Intelligence (AI)

Consider AI as the overarching field. It focuses on creating applications that can perform tasks autonomously, without human intervention. Examples include Netflix recommendations, selfdriving cars, and Amazon's shopping suggestions.

AI applications often integrate AI modules with existing software to enhance user experience. The ultimate goal, regardless of your role (computer vision developer, data scientist, deep learning developer), is to create an AI application.

Machine Learning (ML)

Machine Learning is a subset of AI. It provides statistical tools to analyze and visualize data, enabling predictions, forecasting, clustering, and more. Think of Power BI as a tool that uses ML internally to help analyze data.

Essentially, ML algorithms allow computers to learn from data without being explicitly programmed.

Deep Learning (DL)

Deep Learning is a subset of Machine Learning. Researchers have been working on deep learning since 1958, but it has become prominent recently due to the massive amount of available data and advancements in GPU hardware (thanks to NVIDIA).

Deep Learning focuses on multilayered neural networks. The main aim of deep learning is to mimic the human brain, allowing machines to learn in a similar way.

Data Science (DS)

Data Science can be considered a part of AI, ML, and DL. Data scientists work with machine learning algorithms, data analysis, and potentially deep learning to ultimately create AI applications.

Why is Deep Learning Becoming Popular?

Deep learning's popularity is soaring due to two primary factors:

1. Exponential Data Generation: The rise of social media platforms like Facebook, Instagram, Twitter, and WhatsApp has resulted in an explosion of data. Companies need ways to analyze and utilize this data to improve their products and services.

   For example, Netflix uses your viewing data to recommend movies and TV shows. Panasonic uses data from their products (like ACs) to build smarter devices that reduce energy consumption.

2. Hardware Advancement: NVIDIA's advancements in GPUs (Graphics Processing Units) have made it possible to train complex, multilayered neural networks much faster. The decreasing cost of GPUs due to technological advancements is further fueling deep learning's growth.

Perceptron: Understanding the Building Block

Let's explore the perceptron, the fundamental unit of a neural network. Consider an example dataset with student study hours, play hours, sleep hours, and whether they pass or fail.

Single Layer Neural Network (Perceptron)

A singlelayer neural network consists of an input layer, a hidden layer with one neuron (in a perceptron), and an output layer.

Input Layer: Receives the input data (e.g., study hours, play hours, sleep hours).

Hidden Layer: Contains a neuron that processes the input. Each input is multiplied by a weight. The neuron applies a function to this weighted sum.

Output Layer: Produces the final prediction (e.g., pass or fail).

Weights: Weights determine how much influence each input has on the output. Higher weights mean that input is more important. Weight can be initialized to zero but that has issues.

Bias: Bias is an additional parameter added to each neuron to prevent the weight of the neuron from becoming zero.

Analogy to the Human Brain: Think of your eyes as the input layer, your neurons as the hidden layer, and your brain as the output layer. The neuron in the hidden layer preprocesses data and signal.

Processing Inside a Neuron

1. Weighted Sum: Each input (x) is multiplied by a weight (w), and these products are summed: Σ(xi * wi). This is equivalent to w^Tx.
2. Activation Function: An activation function is applied to the weighted sum. One example is the sigmoid function, which outputs a value between 0 and 1. This will show whether the neuron should be activated or deactivated.

   The sigmoid function is often used for binary classification. Values higher than 0.5 are converted to 1, and lower to 0.

Forward Propagation and Backward Propagation

Forward Propagation

Forward Propagation involves passing the input data through the neural network, performing calculations at each layer, and ultimately generating a prediction.

Think of this process: Input > Multiply by Weights > Add Bias > Activate

Loss Function

The loss function measures the difference between the predicted value (ŷ) and the actual value (y). The goal is to minimize this difference.

The difference will not be exactly zero, we try to minimize it.

Backward Propagation

Backward propagation involves updating the weights to reduce the loss. It helps the model to learn and make better predictions.

The main aim is to update weights as we are updating those weights then only we will be able to get this predicted output matched to the real output.

Optimizers: Optimizers are used to update the weights during backpropagation. One example is gradient descent, which adjusts the coefficients to reach the global minimum of the loss function.

Weight Update Formula: w_new = w_old (learning rate * derivative of loss / derivative of w_old)

This is an interative proccess, so do it thousands of time!

MultiLayer Neural Networks

Multilayer neural networks have multiple hidden layers, allowing for more complex processing. The same principles of forward and backward propagation apply to each layer.

Recap and Story Conclusion

1. Input Layer
2. Weights Added
3. Bias Added
4. Activation Function Applied

(Steps 14 = Forward Propagation)

1. Loss Function Calculation
2. Optimization and Weight Updates

(Steps 56 = Backward Propagation)

This cycle is forward and backward propagation and should be repeated over and over to train model.

Different deep learning architectures (ANN, CNN, RNN) use the same principles of forward and backward propagation with varying optimizers.