Path Overview

Master neural networks from fundamentals to state-of-the-art architectures. This path covers the mathematical foundations, implementation techniques, and practical applications.

Phase 1: Neural Network Fundamentals (Weeks 1-2)

Module 1.1: What are Neural Networks?

• The neuron model and biological inspiration
• Forward propagation (input → output)
• Activation functions (sigmoid, ReLU, tanh)
• Network architecture (layers, neurons)
• Why deep learning works

Module 1.2: Training Neural Networks

• Loss functions (MSE, cross-entropy)
• Gradient descent algorithm
• Backpropagation (chain rule in action)
• Understanding learning rates
• Optimization algorithms (SGD, Adam)

Practice: Implement backpropagation from scratch in NumPy

Phase 2: Deep Learning with TensorFlow/Keras (Weeks 3-5)

Module 2.1: Building Your First Neural Network

• Setting up TensorFlow/Keras environment
• Creating simple neural networks
• Compiling and training models
• Evaluating model performance
• Making predictions on new data

Module 2.2: Handling Real Data

• Data preprocessing and normalization
• Dealing with overfitting (regularization)
• Using validation sets properly
• Early stopping to prevent overfitting
• Dropout and other regularization techniques

Module 2.3: Understanding Layers

• Dense layers (fully connected)
• Different layer types and when to use them
• Batch normalization
• Architecture best practices

Practice Project: Build a classifier for MNIST dataset

Phase 3: Convolutional Neural Networks (Weeks 6-7)

Module 3.1: Convolutions for Images

• How convolution works (visual understanding)
• Filters and feature maps
• Pooling operations
• Building convolutional layers

Module 3.2: Building CNN Models

• Creating CNN architectures
• Classic architectures (LeNet, AlexNet, VGG)
• Transfer learning (using pre-trained models)
• Fine-tuning pre-trained models

Project: Build Image Recognition with CNN project

Phase 4: Recurrent Neural Networks (Weeks 8-9)

Module 4.1: Sequence Modeling

• Why RNNs for sequences (text, time series)
• The RNN architecture
• Vanishing gradient problem
• LSTM (Long Short-Term Memory)
• GRU (Gated Recurrent Unit)

Module 4.2: Applications

• Sequence-to-sequence models
• Machine translation basics
• Text generation
• Time series prediction

Project: Complete Stock Prediction with LSTM project

Phase 5: Modern Architectures & Applications (Weeks 10-12)

Module 5.1: Attention & Transformers

• Attention mechanism
• The Transformer architecture
• BERT and other pre-trained transformers
• Using Hugging Face transformers

Module 5.2: Advanced Topics

• Generative models (VAE, GAN basics)
• Reinforcement learning introduction
• Model deployment and optimization
• Staying current with deep learning

Key Projects in This Path

• Week 2: Simple neural network from scratch
• Week 5: MNIST digit classification
• Week 7: Image recognition with CNN
• Week 9: Stock prediction with LSTM
• Week 12: Capstone project combining multiple architectures

Resources

• TensorFlow Official Tutorials
• Fast.ai Deep Learning Course
• Deep Learning Book (Goodfellow, Bengio, Courville)
• Hugging Face Transformers Library

Next Steps

• Specialize in a specific domain (NLP, Computer Vision, etc.)
• Learn AI Tools Mastery for production deployment
• Review AI Researcher career guide for research opportunities