TensorFlow / Machine Learning Course

Introduction

• Course covers TensorFlow, machine learning, and AI.
• Aimed at beginners with a basic knowledge of programming and Python.

Course Structure

1. Introduction to ML and AI

   • Differences between AI, ML, and neural networks.
   • Different types of machine learning (supervised, unsupervised).
   • Overview of TensorFlow as a scientific computing tool.

2. Core Learning Algorithms

   • Introduction to various algorithms including neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
   • Each algorithm's purpose and how they are applied in real-world applications.

3. Resources for Learning

   • Use Google Collaboratory for coding environment.
   • Links to guiding notebooks for code and references in the description.

Definitions

• Artificial Intelligence: Automating intellectual tasks typically done by humans.
• Machine Learning: A subset of AI that allows systems to learn from data.
• Neural Networks: Models that mimic the human brain structure to recognize patterns.

Types of Learning

• Supervised Learning: Learns from labeled data by comparing to actual outcomes.
• Unsupervised Learning: Identifies patterns from data without labeled outcomes.
• Reinforcement Learning: Learns by interacting with an environment to maximize cumulative reward.

Key Concepts in Neural Networks

• Weights and Biases: Parameters that are adjusted during training to minimize loss function output.
• Activation Function: Defines the output of a node in the neural network (e.g., sigmoid, ReLU).
• Loss Function: A measure of how well the neural network is performing in predicting outcomes (mean squared error, cross-entropy).
• Gradient Descent: Optimization algorithm to minimize loss by iteratively adjusting weights and biases.
• Epochs: Number of times the training process goes through the entire dataset.

Working with Data

• Use of train-test splits to evaluate model accuracy without bias.
• Importance of data preprocessing (normalization, reshaping).
• Utilizing data augmentation techniques to increase dataset size responsibly.

Building Neural Networks with Keras

• Utilizing sequential models to build the structure of neural networks.
• Understanding how to compile and fit models with the necessary parameters.

Convolutional Neural Networks (CNN)

• Finding local patterns while maintaining the generalization of models.
• Understanding how layers and filters in CNNs work for image classification tasks.

Recurrent Neural Networks (RNN)

• Capable of learning sequences; retains information over time using internal loops.
• Long Short Term Memory networks (LSTM) help retain information effectively over longer sequences.

Applications

• Examples include sentiment analysis of text, character generation, image recognition, etc.
• Reinforcement learning applications in game AI, robotics, and more.

Next Steps

• Explore more on the TensorFlow website for guides, tutorials, resources, and advanced topics.
• Engage in hands-on projects to get practical experience in ML and refine skills.