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Deep Learning Fundamentals with Keras

Chapter 1: Introduction to Deep Learning Concepts

What is Deep Learning?

Artificial Neurons and Network Structure

Supervised Learning with Neural Networks

Introduction to Keras

Setting Up Your Keras Environment

Hands-on Practical: Environment Verification

Chapter 2: Building Neural Networks with Keras

The Sequential API

Common Layer Types: Dense

Activation Functions

Specifying Input Shape

The Functional API

Model Summary and Visualization

Practice: Building Your First Network with Keras

Chapter 3: Training Deep Neural Networks

The Compilation Step

Understanding Loss Functions

Optimization Algorithms

Backpropagation

The Training Loop: fit() Method

Batches and Epochs

Validation Data and Monitoring Performance

Model Evaluation: evaluate() Method

Hands-on Practical: Training a Simple Classifier

Chapter 4: Convolutional Neural Networks (CNNs)

Introduction to Convolutional Networks

Convolutional Layers (Conv2D)

Pooling Layers (MaxPooling2D)

Building a Simple CNN Architecture

Flattening and Dense Layers in CNNs

Working with Image Data in Keras

Understanding Feature Maps

Practice: Implementing a CNN for Image Classification

Chapter 5: Recurrent Neural Networks (RNNs)

Introduction to Sequence Data

Recurrent Neural Network Concepts

SimpleRNN Layer in Keras

The Vanishing Gradient Problem

Long Short-Term Memory (LSTM) Networks

LSTM Layer in Keras

Gated Recurrent Units (GRUs)

Preparing Sequence Data for RNNs

Practice: Building an RNN/LSTM for Text Classification

Chapter 6: Improving Model Performance and Workflow

Understanding Overfitting and Underfitting

Regularization Techniques: L1, L2, Dropout

Data Augmentation

Using Callbacks

Saving and Loading Models

Introduction to TensorBoard

Hyperparameter Tuning Concepts

Hands-on Practical: Applying Improvement Techniques

Deep Learning Fundamentals with Keras

Prerequisites: Python, basic ML concepts

Level: Intermediate

What You'll Learn

Neural Network Concepts
Understand the structure and function of artificial neural networks, including layers, activation functions, and weights.
Keras API Proficiency
Become proficient in using the Keras API (Sequential and Functional) to define, build, and compile deep learning models.
Model Training Process
Grasp the mechanics of training deep learning models, including loss functions, optimization algorithms, and backpropagation.
Convolutional Neural Networks (CNNs)
Implement CNNs using Keras for image recognition tasks, understanding convolutional and pooling layers.
Recurrent Neural Networks (RNNs)
Implement RNNs and LSTMs using Keras for sequence data processing tasks like text analysis.
Model Evaluation and Improvement
Learn techniques to evaluate model performance, prevent overfitting, and apply methods like regularization and callbacks.

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