Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), 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.
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Notes and resources: ocdevel.com/mlg/29 Try a walking desk to stay healthy while you study or work! Reinforcement Learning (RL) is a fundamental component of artificial intelligence, different from purely being AI itself. It is considered a key aspect of AI due to its ability to learn through interactions with the environment using a system of rewards and punishments. Links: openai/baselines reinforceio/tensorforce NervanaSystems/coach rll/rllab Differential Computers Concepts and Definitions Re...
Notes and resources: ocdevel.com/mlg/28 Try a walking desk to stay healthy while you study or work! More hyperparameters for optimizing neural networks. A focus on regularization, optimizers, feature scaling, and hyperparameter search methods. Hyperparameter Search Techniques Grid Search involves testing all possible permutations of hyperparameters, but is computationally exhaustive and suited for simpler, less time-consuming models. Random Search selects random combinations of hyperparameters, ...
Full notes and resources at ocdevel.com/mlg/27 Try a walking desk to stay healthy while you study or work! Hyperparameters are crucial elements in the configuration of machine learning models. Unlike parameters, which are learned by the model during training, hyperparameters are set by humans before the learning process begins. They are the knobs and dials that humans can control to influence the training and performance of machine learning models. Definition and Importance Hyperparameters diffe...
Try a walking desk to stay healthy while you study or work! Ful notes and resources at ocdevel.com/mlg/26 NOTE . This episode is no longer relevant, and tforce_btc_trader no longer maintained. The current podcast project is Gnothi. Episode Overview TForce BTC Trader Project: Trading Crypto Special: Intuitively highlights decisions: hypers, supervised v reinforcement, LSTM v CNN Crypto (v stock) Bitcoin, Ethereum, Litecoin, Ripple Many benefits (immutable permenant distributed ledger; security; l...
Try a walking desk to stay healthy while you study or work! Notes and resources at ocdevel.com/mlg/25 Filters and Feature Maps: Filters are small matrices used to detect visual features from an input image by applying them to local pixel patches, creating a 3D output called a feature map. Each filter is tasked with recognizing a specific pattern (e.g., edges, textures) in the input images. Convolutional Layers: The filter is applied across the image to produce an output which is the feature map....
Try a walking desk to stay healthy while you study or work! Notes and resources at ocdevel.com/mlg/24 Hardware Desktop if you're stationary, as you'll get the best performance bang-for-buck and improved longevity; laptop if you're mobile. Desktops. Build your own PC, better value than pre-built. See PC Part Picker , make sure to use an Nvidia graphics card. Generally shoot for 2nd-best of CPUs/GPUs. Eg, RTX 4070 currently (2024-01); better value-to-price than 4080+. For laptops, see this post (u...
Try a walking desk to stay healthy while you study or work! Notes and resources at ocdevel.com/mlg/23 Neural Network Types in NLP Vanilla Neural Networks (Feedforward Networks) : Used for general classification or regression tasks. Examples include predicting housing costs or classifying images as cat, dog, or tree. Convolutional Neural Networks (CNNs) : Primarily used for image-related tasks. Recurrent Neural Networks (RNNs) : Used for sequence-based tasks such as weather predictions, stock mar...
Try a walking desk to stay healthy while you study or work! Notes and resources at ocdevel.com/mlg/22 Deep NLP Fundamentals Deep learning has had a profound impact on natural language processing by introducing models like recurrent neural networks (RNNs) that are specifically adept at handling sequential data. Unlike traditional linear models like linear regression, RNNs can address the complexities of language which appear from its inherent non-linearity and hierarchy. These models are able to ...
Try a walking desk to stay healthy while you study or work! Notes and resources at ocdevel.com/mlg/20 NLP progresses through three main layers: text preprocessing, syntax tools, and high-level goals, each building upon the last to achieve complex linguistic tasks. Text Preprocessing Text preprocessing involves essential steps such as tokenization, stemming, and stop word removal. These foundational tasks clean and prepare text for further analysis, ensuring that subsequent processes can be appli...
Try a walking desk to stay healthy while you study or work! Notes and resources at ocdevel.com/mlg/19 Classical NLP Techniques: Origins and Phases in NLP History : Initially reliant on hardcoded linguistic rules, NLP's evolution significantly pivoted with the introduction of machine learning, particularly shallow learning algorithms, leading eventually to deep learning, which is the current standard. Importance of Classical Methods : Knowing traditional methods is still valuable, providing a his...
Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/18 Overview: Natural Language Processing (NLP) is a subfield of machine learning that focuses on enabling computers to understand, interpret, and generate human language. It is a complex field that combines linguistics, computer science, and AI to process and analyze large amounts of natural language data. NLP Structure NLP is divided into three main tiers: parts, tasks, and goals. 1. Parts Text Pre-process...
Try a walking desk to stay healthy while you study or work! At this point, browse #importance:essential on ocdevel.com/mlg/resources with the 45m/d ML, 15m/d Math breakdown.
Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/16 Inspiration in AI Development Early inspirations for AI development centered around solving challenging problems, but recent advancements like self-driving cars and automated scientific discoveries attract professionals due to potential economic automation and career opportunities. The Singularity The singularity suggests exponential technological growth leading to a point where AI and robotics automate ...
Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/15 Concepts Performance Evaluation Metrics : Tools to assess how well a machine learning model performs tasks like spam classification, housing price prediction, etc. Common metrics include accuracy, precision, recall, F1/F2 scores, and confusion matrices. Accuracy : The simplest measure of performance, indicating how many predictions were correct out of the total. Precision and Recall : Precision : The rat...
Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/14 Anomaly Detection Systems Applications : Credit card fraud detection and server activity monitoring. Concept : Identifying outliers on a bell curve. Statistics : Central role of the Gaussian distribution (normal distribution) in detecting anomalies. Process : Identifying significant deviations from the mean to detect outliers. Recommender Systems Types : Content Filtering : Uses features of items (e.g., ...
Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/13 Support Vector Machines (SVM) Purpose : Classification and regression. Mechanism : Establishes decision boundaries with maximum margin. Margin : The thickness of the decision boundary, large margin minimizes overfitting. Support Vectors : Data points that the margin directly affects. Kernel Trick : Projects non-linear data into higher dimensions to find a linear decision boundary. Naive Bayes Classifiers...
Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/12 Topics Shallow vs. Deep Learning : Shallow learning can often solve problems more efficiently in time and resources compared to deep learning. Supervised Learning : Key algorithms include linear regression, logistic regression, neural networks, and K Nearest Neighbors (KNN). KNN is unique as it is instance-based and simple, categorizing new data based on proximity to known data points. Unsupervised Learn...
Try a walking desk to stay healthy while you study or work! 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 ...
Try a walking desk to stay healthy while you study or work! 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 (ML...
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...
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 optimi...
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 th...
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. Links...
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. Links Notes and resources at ocdevel.com/mlg/4 Try a walking desk stay healthy & sharp while you learn & code ...
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. Links Notes and resour...
The episode begins by defining data science as the overarching field, encompassing artificial intelligence and machine learning. It delves into AI's objective and subjective definitions, exploring its various sub-disciplines like NLP, perception, and planning, and introduces the Turing Test. The discussion then highlights machine learning as the dominant force currently revolutionizing these AI fields. Finally, it provides a comprehensive overview of the data science pipeline, detailing roles like data analysts and machine learning engineers, and traces the rich history of AI from ancient concepts to its modern resurgence.
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. MLG , Resources Guide Gnothi (podcast project): website , Github What is this podcast? "...
