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MLG 010 Languages & Frameworks
7 maart 2017 · 45 min
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Full notes at ocdevel.com/mlg/10
Topics:
Recommended Languages and Frameworks:
Python and TensorFlow are top recommendations for machine learning.
Python's versatile libraries (NumPy, Pandas, Scikit-Learn) enable it to cover all areas of data science including data mining, analytics, and machine learning.
Language Choices:
C/C++: High performance, suitable for GPU optimization but not recommended unless already familiar.
Math Languages (R, MATLAB, Octave, Julia): Optimized for mathematical operations, particularly R preferred for data analytics.
JVM Languages (Java, Scala): Suited for scalable data pipelines (Hadoop, Spark).
Framework Details:
TensorFlow: Comprehensive tool supporting a wide range of ML tasks; notably improves Python's performance.
Theano: First in symbolic graph framework, but losing popularity compared to newer frameworks.
Torch: Initially favored for image recognition, now supports a Python API.
Keras: High-level API running on top of TensorFlow or Theano for easier neural network construction.
Scikit-learn: Good for shallow learning algorithms.
Comparisons:
C++ vs Python in ML: C++ offers direct GPU access for performance, but Python streamlined performance with frameworks that auto-generate optimized C code.
R and Python in Data Analytics: Python's Pandas and NumPy rival R with a strong general-purpose application beyond analytics.
Considerations:
Python's Ecosystem Benefits: Single programming ecosystem spans full data science workflow, crucial for integrated projects.
Emerging Trends: Keep an eye on Julia for future considerations in math-heavy operations and industry adoption.
Additional Notes:
Hardware Recommendations:
Utilize Nvidia GPUs for machine learning due to superior support and integration with CUDA and cuDNN.
Learning Resources:
TensorFlow's documentation and tutorials are highly recommended for learning due to their thoroughness and regular upd…
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MLG 009 Deep Learning
4 maart 2017 · 51 min
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Full notes at ocdevel.com/mlg/9
Key Concepts:
Deep Learning vs. Shallow Learning: Machine learning is broken down hierarchically into AI, ML, and subfields like supervised/unsupervised learning. Deep learning is a specialized area within supervised learning distinct from shallow learning algorithms like linear regression.
Neural Networks: Central to deep learning, artificial neural networks include models like multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Neural networks are composed of interconnected units or "neurons," which are mathematical representations inspired by biological neurons.
Unique Features of Neural Networks:
Feature Learning: Neural networks learn to combine input features optimally, enabling them to address complex non-linear problems where traditional algorithms fall short.
Hierarchical Representation: Data can be processed hierarchically through multiple layers, breaking down inputs into simpler components that can be reassembled to solve complex tasks.
Applications:
Medical Cost Estimation: Neural networks can handle non-linear complexities such as feature interactions, e.g., age, smoking, obesity, impacting medical costs.
Image Recognition: Neural networks leverage hierarchical data processing to discern patterns such as lines and edges, building up to recognizing complex structures like human faces.
Computational Considerations:
Cost of Deep Learning: Deep learning's computational requirements make it expensive and resource-intensive compared to shallow learning algorithms. It's cost-effective to use when necessary for complex tasks but not for simpler linear problems.
Architectures & Optimization:
Different Architectures for Different Tasks: Specialized neural networks like CNNs are suited for image tasks, RNNs for sequence data, and DQNs for planning.
Neuron Types: Neurons in neural networks are referred t…
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MLG 008 Math for Machine Learning
23 februari 2017 · 28 min
Mathematics essential for machine learning includes linear algebra, statistics, and calculus, each serving distinct purposes: linear algebra handles data representation and computation, statistics underpins the algorithms and evaluation, and calculus enables the optimization process. It is recommended to learn the necessary math alongside or after starting with practical machine learning tasks, using targeted resources as needed. In machine learning, linear algebra enables efficient manipulation of data structures like matrices and tensors, statistics informs model formulation and error evaluation, and calculus is applied in training models through processes such as gradient descent for optimization. Lees meer
Links
Notes and resources at ocdevel.com/mlg/8
Try a walking desk - stay healthy & sharp while you learn & code
Come back here after you've finished Ng's course; or learn these resources in tandem with ML (say 1 day a week).
Recommended Approach to Learning Math
Direct study of mathematics before beginning machine learning is not necessary; essential math concepts are introduced within most introductory courses.
A top-down approach, where one starts building machine learning models and learns the underlying math as needed, is effective for retaining and appreciating mathematical concepts.
Allocating a portion of learning time (such as one day per week or 20% of study time) to mathematics while pursuing machine learning is suggested for balanced progress.
Linear Algebra in Machine Learning
Linear algebra is fundamental for representing and manipulating data as matrices (spreadsheets of features and examples) and vectors (parameter lists like theta).
Every operation involving input features and learned parameters during model prediction and transformation leverages linear algebra, particularly matrix and vector multiplication.
The concept of tensors generalizes vectors (1D), matrices (2D), and higher-dimensional arrays; tensor operations are central to framew…
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MLG 007 Logistic Regression
19 februari 2017 · 35 min
The logistic regression algorithm is used for classification tasks in supervised machine learning, distinguishing items by class (such as "expensive" or "not expensive") rather than predicting continuous numerical values. Logistic regression applies a sigmoid or logistic function to a linear regression model to generate probabilities, which are then used to assign class labels through a process involving hypothesis prediction, error evaluation with a log likelihood function, and parameter optimization using gradient descent. Lees meer
Links
Notes and resources at ocdevel.com/mlg/7
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Classification versus Regression in Supervised Learning
Supervised learning consists of two main tasks: regression and classification.
Regression algorithms predict continuous values, while classification algorithms assign classes or categories to data points.
The Role and Nature of Logistic Regression
Logistic regression is a classification algorithm, despite its historically confusing name.
The algorithm determines the probability that an input belongs to a specific class, using outputs between zero and one.
How Logistic Regression Works
The process starts by passing inputs through a linear regression function, then applying a logistic (sigmoid) function to produce a probability.
For binary classification, results above 0.5 usually indicate a positive class (for example, "expensive"), and results below 0.5 indicate a negative class ("not expensive").
Multiclass problems assign probabilities to each class, selecting the class with the highest probability using the arg max function.
Example Application: Housing Spreadsheet
An example uses a spreadsheet of houses with features like square footage and number of bedrooms, labeling each as "expensive" (1) or "not expensive" (0).
Logistic regression uses the spreadsheet data to learn the pattern that separates expensive houses from less expensive ones.
Steps in Logistic Re…
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MLG 006 Certificates & Degrees
17 februari 2017 · 16 min
People interested in machine learning can choose between self-guided learning, online certification programs such as MOOCs, accredited university degrees, and doctoral research, with industry acceptance and personal goals influencing which path is most appropriate. Industry employers currently prioritize a strong project portfolio over non-accredited certificates, and while master's degrees carry more weight for job applications, PhD programs are primarily suited for research interests rather than industry roles. Lees meer
Links
Notes and resources at ocdevel.com/mlg/6
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Learner Types and Self-Guided Education
Individuals interested in machine learning may be hobbyists, aspiring professionals, or scientists wishing to contribute to research in artificial intelligence.
Hobbyists can rely on structured resources, including curated syllabi and recommended online materials, to guide their self-motivated studies.
The "Andrew Ng Coursera" course is frequently recommended as an initial step for self-learners, and advanced resources such as "Artificial Intelligence: A Modern Approach" and "Deep Learning" textbooks are valuable later.
MOOCs and Online Certificates
MOOCs (Massive Open Online Courses) are widely available from platforms such as Coursera, Udacity, edX, and Khan Academy, but only Coursera and Udacity are commonly recognized for machine learning and data science content.
Coursera is typically recommended for individual courses; its specializations are less prominent in professional discussions.
Udacity offers both free courses and paid "nano degrees" which include structured mentoring, peer interaction, and project-based learning.
Although Udacity certificates demonstrate completion and the development of practical projects, they lack widespread recognition or acceptance from employers.
Hiring managers and recruiters consistently emphasize the value of a substantial project portfolio over non-accr…
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MLG 005 Linear Regression
16 februari 2017 · 35 min
Linear regression is introduced as the foundational supervised learning algorithm for predicting continuous numeric values, using cost estimation of Portland houses as an example. The episode explains the three-step process of machine learning - prediction via a hypothesis function, error calculation with a cost function (mean squared error), and parameter optimization through gradient descent - and details both the univariate linear regression model and its extension to multiple features. Lees meer
Links
Notes and resources at ocdevel.com/mlg/5
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Linear Regression
Overview of Machine Learning Structure
Machine learning is a branch of artificial intelligence, alongside statistics, operations research, and control theory.
Within machine learning, supervised learning involves training with labeled examples and is further divided into classification (predicting discrete classes) and regression (predicting continuous values).
Linear Regression and Problem Framing
Linear regression is the simplest and most commonly taught supervised learning algorithm for regression problems, where the goal is to predict a continuous number from input features.
The episode example focuses on predicting the cost of houses in Portland, using square footage and possibly other features as inputs.
The Three Steps of Machine Learning in Linear Regression
Machine learning in the context of linear regression follows a standard three-step loop: make a prediction, measure how far off the prediction is, and update the prediction method to reduce mistakes.
Predicting uses a hypothesis function (also called objective or estimate) that maps input features to a predicted value.
The Hypothesis Function
The hypothesis function is a formula that multiplies input features by coefficients (weights) and sums them to make a prediction; in mathematical terms, fo…
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MLG 004 Algorithms - Intuition
12 februari 2017 · 23 min
Machine learning consists of three steps: prediction, error evaluation, and learning, implemented by training algorithms on large datasets to build models that can make decisions or classifications. The primary categories of machine learning algorithms are supervised, unsupervised, and reinforcement learning, each with distinct methodologies for learning from data or experience. Lees meer
Links
Notes and resources at ocdevel.com/mlg/4
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The Role of Machine Learning in Artificial Intelligence
Artificial intelligence includes subfields such as reasoning, knowledge representation, search, planning, and learning.
Learning connects to other AI subfields by enabling systems to improve from mistakes and past actions.
The Core Machine Learning Process
The machine learning process follows three steps: prediction (or inference), error evaluation (or loss calculation), and training (or learning).
In an example such as predicting chess moves, a move is made (prediction), the error or effectiveness of that move is measured (error function), and the underlying model is updated based on that error (learning).
This process generalizes to real-world applications like predicting house prices, where a model is trained on a large dataset with many features.
Data, Features, and Models
Datasets used for machine learning are typically structured as spreadsheets with rows as examples (e.g., individual houses) and columns as features (e.g., number of bedrooms, bathrooms, square footage).
Features are variables used by algorithms to make predictions and can be numerical (such as square footage) or categorical (such as "is downtown" yes/no).
The algorithm processes input data, learns the appropriate coefficients or weights for each feature through algebraic equations, and forms a model.
The combination of the algorithm (such as code in Python or TensorFlow) and the learned weights forms the model, which is then used to make fu…
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MLG 003 Inspiration
10 februari 2017 · 19 min
AI is rapidly transforming both creative and knowledge-based professions, prompting debates on economic disruption, the future of work, the singularity, consciousness, and the potential risks associated with powerful autonomous systems. Philosophical discussions now focus on the socioeconomic impact of automation, the possibility of a technological singularity, the nature of machine consciousness, and the ethical considerations surrounding advanced artificial intelligence. Lees meer
Links
Notes and resources at ocdevel.com/mlg/3
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Automation of the Economy
Artificial intelligence is increasingly capable of simulating intellectual tasks, leading to the replacement of not only repetitive and menial jobs but also high-skilled professions such as medical diagnostics, surgery, web design, and art creation.
Automation is affecting various industries including healthcare, transportation, and creative fields, where AI-powered tools are assisting or even outperforming humans in tasks like radiological analysis, autonomous vehicle operation, website design, and generating music or art.
Economic responses to these trends are varied, with some expressing fear about job loss and others optimistic about new opportunities and improved quality of life as history has shown adaptation following previous technological revolutions such as the agricultural, industrial, and information revolutions.
The concept of universal basic income (UBI) is being discussed as a potential solution to support populations affected by automation, as explored in several countries.
Public tools are available, such as the BBC's "Is your job safe?", which estimates the risk of job automation for various professions.
The Singularity
The singularity refers to a hypothesized point where technological progress, particularly in artificial intelligence, accelerates uncontrollably, resulting in rapid and irreversible changes to society.
The concept, po…
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MLG 002 Difference Between Artificial Intelligence, Machine Learning, Data Science
9 februari 2017 · 1 uur 5 min
Artificial intelligence is the automation of tasks that require human intelligence, encompassing fields like natural language processing, perception, planning, and robotics, with machine learning emerging as the primary method to recognize patterns in data and make predictions. Data science serves as the overarching discipline that includes artificial intelligence and machine learning, focusing broadly on extracting knowledge and actionable insights from data using scientific and computational methods. Lees meer
Links
Notes and resources at ocdevel.com/mlg/2
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Data Science Overview
Data science encompasses any professional role that deals extensively with data, including but not limited to artificial intelligence and machine learning.
The data science pipeline includes data ingestion, storage, cleaning (feature engineering), and outputs in data analytics, business intelligence, or machine learning.
A data lake aggregates raw data from multiple sources, while a feature store holds cleaned and transformed data, prepared for analysis or model training.
Data analysts and business intelligence professionals work primarily with data warehouses to generate human-readable reports, while machine learning engineers use transformed data to build and deploy predictive models.
At smaller organizations, one person ("data scientist") may perform all data pipeline roles, whereas at large organizations, each phase may be specialized.
Wikipedia: Data Science describes data science as the interdisciplinary field for extracting knowledge and insights from structured and unstructured data.
Artificial Intelligence: Definition and Sub-disciplines
Artificial intelligence (AI) refers to the theory and development of computer systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and …
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MLG 001 Introduction
1 februari 2017 · 8 min
Show notes: ocdevel.com/mlg/1. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc. Lees meer
MLG, Resources Guide
Gnothi (podcast project): website, Github
What is this podcast?
"Middle" level overview (deeper than a bird's eye view of machine learning; higher than math equations)
No math/programming experience required
Who is it for
Anyone curious about machine learning fundamentals
Aspiring machine learning developers
Why audio?
Supplementary content for commute/exercise/chores will help solidify your book/course-work
What it's not
News and Interviews: TWiML and AI, O'Reilly Data Show, Talking machines
Misc Topics: Linear Digressions, Data Skeptic, Learning machines 101
iTunesU issues
Planned episodes
What is AI/ML: definition, comparison, history
Inspiration: automation, singularity, consciousness
ML Intuition: learning basics (infer/error/train); supervised/unsupervised/reinforcement; applications
Math overview: linear algebra, statistics, calculus
Linear models: supervised (regression, classification); unsupervised
Parts: regularization, performance evaluation, dimensionality reduction, etc
Deep models: neural networks, recurrent neural networks (RNNs), convolutional neural networks (convnets/CNNs)
Languages and Frameworks: Python vs R vs Java vs C/C++ vs MATLAB, etc; TensorFlow vs Torch vs Theano vs Spark, etc