What to Learn Next in AI – Neural Networks, CNN, RNN, Optimizers & Backpropagation

Explore the next steps in AI and machine learning, including artificial neural networks, activation functions, loss functions, optimizers, backpropagation, CNNs for images, and RNN/LSTM for sequences. Ideal for beginners looking to advance in deep learning.

1. Introduction

After mastering Python, data handling, math for AI, and core ML algorithms, the next step is deep learning. Deep learning uses neural networks to model complex patterns in data, enabling applications like image recognition, natural language processing, and AI assistants.

2. Artificial Neural Networks (ANN)

Concept

  1. Inspired by the human brain, composed of neurons in layers.
  2. Each neuron receives inputs, applies weights, sums them, and passes through an activation function.
  3. Layers: Input Layer → Hidden Layers → Output Layer

Example Applications:

  1. Image classification
  2. Speech recognition
  3. Predictive analytics

3. Activation Functions

  1. Determines whether a neuron should be activated.
  2. Introduces non-linearity to model complex relationships.

Common Functions:

  1. Sigmoid: Maps values to (0,1), used in binary classification.
  2. ReLU: Rectified Linear Unit, output = max(0, x), used widely in hidden layers.
  3. Tanh: Maps values to (-1,1), centers data around zero.

Python Example (NumPy):


import numpy as np

def relu(x):
return np.maximum(0, x)

x = np.array([-2, -1, 0, 1, 2])
print("ReLU:", relu(x))

4. Loss Functions

  1. Measures difference between predicted and actual values.
  2. Guides model optimization.

Common Loss Functions:

  1. Mean Squared Error (MSE): For regression problems.
  2. Cross-Entropy Loss: For classification problems.

5. Optimizers

  1. Algorithms to update weights in neural networks during training.
  2. Goal: Minimize the loss function.

Popular Optimizers:

  1. SGD (Stochastic Gradient Descent): Updates weights using one batch at a time.
  2. Adam: Adaptive learning rate optimizer, widely used for faster convergence.

6. Backpropagation

  1. Core algorithm to train ANNs.
  2. Works by computing the gradient of the loss function with respect to each weight using chain rule.
  3. Updates weights via an optimizer (SGD, Adam).

Process:

  1. Forward pass: Compute predictions.
  2. Compute loss.
  3. Backward pass: Calculate gradients.
  4. Update weights.

7. Convolutional Neural Networks (CNN)

  1. Specialized for image data.
  2. Components:
  3. Convolutional Layers: Extract features.
  4. Pooling Layers: Reduce spatial size.
  5. Fully Connected Layers: Make predictions.

Applications:

  1. Image recognition (cats vs dogs)
  2. Object detection
  3. Medical image analysis

8. Recurrent Neural Networks (RNN) & LSTM

  1. Specialized for sequence data (time series, text, speech).
  2. RNNs: Have memory to capture sequential dependencies.
  3. LSTM (Long Short-Term Memory): Addresses RNN’s vanishing gradient problem.

Applications:

  1. Language modeling and translation
  2. Stock price prediction
  3. Speech recognition

9. Summary

After completing the core ML roadmap, learners should explore:

  1. Artificial Neural Networks: Build deeper models.
  2. Activation Functions & Loss Functions: Key for model learning.
  3. Optimizers & Backpropagation: Train models efficiently.
  4. CNNs: Process and classify image data.
  5. RNN & LSTM: Handle sequential and time-series data.

Outcome:

  1. Gain understanding of deep learning foundations.
  2. Prepare for building advanced AI applications in vision, NLP, and predictive modeling.
  3. Transition from classical ML to modern AI techniques.