Introduction to AI and Machine Learning

Introduction to AI, Deep Learning and Machine Learning

• Artificial intelligence is a computer's ability to perform tasks typically associated with intelligent beings.
• Machine learning studies algorithms that learn from examples and experience, instead of relying on hard-coded rules. Machine learning makes predictions on new data.
• Deep learning is a subfield of machine learning that focuses on data representations, developing increasingly meaningful representations through successive layers.

What is Artificial Intelligence?

• Artificial intelligence is the ability of a computer to perform tasks commonly associated with intelligent beings.

What is Machine Learning?

• Machine learning is the study of algorithms that learn from examples and experience instead of relying on hard-coded rules. It aims to make predictions on new data.

What is Deep Learning?

• Deep learning is a subfield of machine learning that focuses on learning data representations through successive layers of increasingly meaningful representations.

Timeline of AI, Machine Learning and Deep Learning

• Early AI optimism in the 1950s led to smaller subsets like machine learning, and then deep learning, creating significant disruptions.

Traditional vs Deep "End-to-End" Machine Learning

• "Traditional" machine learning uses handcrafted features to train a classifier.
• Deep learning uses a series of learned features, from low-level to high-level, to train a classifier. This approach is known as "end-to-end" learning.

Key Milestones in Machine Learning

• The examples provided detail numerous significant milestones in the history of machine learning, with contributors and their contributions listed chronologically.

Main Types of Machine Learning

• Supervised learning, Unsupervised learning, Self-supervised learning, Reinforcement learning.

Data Fundamentals

• Humans learn through observation, an unsupervised learning process, creating a model of the world.
• Machine learning relies on large amounts of labeled data to compensate for this.

Data structures

• Tensors generalize matrices to n-dimensions.
• 1D tensor: vector
• 2D tensor: matrix
• 3D, 4D, 5D tensors
• Training data is typically split into training, validation, and test sets, sometimes with an adversarial test set.
• Minibatches are small sets of data used in machine learning for efficiency.

Optimization

• Optimization involves minimizing a quantity (e.g., loss function) to improve a model's performance. This is central to training machine learning models.

Anatomy of a Deep Neural Network

• A deep neural network has layers (that transform data), weights (learned parameters), an optimizer (to update weights based on the loss function), and a loss function (to measure the error between predictions and the actual values).

Layers

• Layers are data processing modules.
• Convolutional, recurrent and densely connected layers are common.
• Layers often perform tasks such as pooling, flattening, merging and normalization.

Input Data and Targets

• Input data is mapped to predictions.
• During training predictions are compared to true targets.

Loss Function

• The loss function assesses the error between predictions and true values. Different tasks (regression, classification) use varying loss functions.

Optimizer

• Optimizers update the weights of the network based on the loss function, typically using a learning rate.
• Stochastic gradient descent and its variants, like RMSProp, are commonly used optimizers.

Deep Learning Frameworks

• Deep learning frameworks help construct neural networks and handle the associated computations.
• Multiple deep learning frameworks (e.g., TensorFlow, PyTorch, Caffe) manage frameworks better. Frameworks utilize various tools, such as CPUs and GPUs.

Explore the foundational concepts of Artificial Intelligence, Machine Learning, and Deep Learning in this quiz. Understand how machines replicate intelligent behavior and the algorithms that enable them to learn from experiences. Test your knowledge on these essential topics in modern technology.