Hosted by OCDevel · EN-US · 30 episodes
Established thought leaders with verified media credentials.
This series aims to teach you the high level fundamentals of machine learning from A to Z. I'll teach you the basic intuition, algorithms, and math. We'll discuss languages and frameworks, deep learning, and more. Audio may be an inferior medium to task; but with all our exercise, commute, and chores hours of the day, not having an audio supplementary education would be a missed opportunity. And where your other resources will provide you the machine learning trees, I’ll provide the forest. Additionally, consider me your syllabus. At the end of every episode I’ll provide the best-of-the-best resources curated from around the web for you to learn each episode’s details.
OCDevel hosts Machine Learning Guide, a how to show with 30 episodes published.
Introducing a new podcast series on Patreon: Machine Learning Applied MLG: I'm rebooting this series to fix mistakes & add more shallows (Bayesian methods, Tree methods, etc). I'm adding Patreon rewards, including access
Introduction to reinforcement learning concepts ## Resources - Hands-On Machine Learning with Scikit-Learn and TensorFlow (https://www.amazon.com/dp/1491962291?tag=ha0d2-20) `book:medium` (last chapter) - undefined - AI
Hyperparameters part 2: hyper-search, regularization, SGD optimizers, scaling ## Episode - Hyper optimization ** GridSearch, RandomSearch ** Bayesian Optimization (https://thuijskens.github.io/2016/12/29/bayesian-optimis
Hyperparameters part 1: network architecture ## Episode - Hypers future & meta-learning ** We're always removing hypers. DL removed feature-engineering ` - Model selection ** Unsupervised? K-means Clustering => DL ** Lin
Community project & intro to Bitcoin/crypto + trading ## Resources - TForce BTC Trader (https://github.com/lefnire/tforce_btc_trader) (podcast project) ## Episode - Project: Trading Crypto ** Reasons ** Get rich ** Hot t
Convnets or CNNs. Filters, feature maps, window/stride/padding, max-pooling. ## Resources - Stanford cs231n: Convnets (https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC) `course:medium` - Hands-On
TensorFlow, Pandas, Numpy, Scikit-Learn, Keras, TensorForce. ## Resources - Hands-On Machine Learning with Scikit-Learn and TensorFlow (https://www.amazon.com/dp/1491962291?tag=ha0d2-20) `book:medium` - The usual DL reso
RNN review, bi-directional RNNs, LSTM & GRU cells. ## Resources - Overview Articles: ** Unreasonable Effectiveness of RNNs (http://karpathy.github.io/2015/05/21/rnn-effectiveness/) `article:easy` ** Deep Learning, NLP, a
Recurrent Neural Networks (RNNs) and Word2Vec. ## Resources - Overview Articles: ** Unreasonable Effectiveness of RNNs (http://karpathy.github.io/2015/05/21/rnn-effectiveness/) `article:easy` ** Deep Learning, NLP, and R
Update on Patreon and resources. Keep the podcast alive, donate on Patreon (https://www.patreon.com/machinelearningguide)
Natural Language Processing classical/shallow algorithms. ## Resources - Speech and Language Processing (https://www.amazon.com/dp/0131873210?tag=ha0d2-20) `book:hard` comprehensive classical-NLP bible - Stanford NLP You
Natural Language Processing classical/shallow algorithms. ## Resources - Speech and Language Processing (https://www.amazon.com/dp/0131873210?tag=ha0d2-20) `book:hard` comprehensive classical-NLP bible - Stanford NLP You
Introduction to Natural Language Processing (NLP) topics. ## Resources - Speech and Language Processing (https://www.amazon.com/dp/0131873210?tag=ha0d2-20) `book:hard` comprehensive classical-NLP bible - Stanford NLP You
Checkpoint - learn the material offline! 45m/d ML - Coursera (https://www.coursera.org/learn/machine-learning) `course:hard` - Python (http://amzn.to/2mVgtJW) `book:medium` - Deep Learning Resources (http://ocdevel.com/p
Can AI be conscious? ## Resources - Philosophy of Mind: Brains, Consciousness, and Thinking Machines (Audible (http://amzn.to/2kQGgk5), TGC (https://goo.gl/fDteyi)) `audio:easy` ## Episode Inspirations for AI - economic
Performance evaluation & improvement ## Episode Performance evaluation - Performance measures: accuracy, precision, recall, F1/F2 score - Cross validation: split your data into train, validation, test sets - Training set
Speed run of Anomaly Detection, Recommenders(Content Filtering vs Collaborative Filtering), and Markov Chain Monte Carlo (MCMC) ## Resources - Andrew Ng Week 9 (https://www.coursera.org/learn/machine-learning/resources/s
Speed run of Support Vector Machines (SVMs) and Naive Bayes Classifier. ## Resources - Andrew Ng Week 7 (https://www.coursera.org/learn/machine-learning/resources/Es9Qo) - Hands-On Machine Learning with Scikit-Learn and
Speed-run of some shallow algorithms: K Nearest Neighbors (KNN); K-means; Apriori; PCA; Decision Trees ## Resources - Andrew Ng Week 8 (https://www.coursera.org/learn/machine-learning/resources/kGWsY) - Tour of Machine L
Checkpoint - start learning the material offline! 45m/d ML - Coursera (https://www.coursera.org/learn/machine-learning) `course:hard` - Python (http://amzn.to/2mVgtJW) `book:medium` - Deep Learning Resources (http://ocde
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Machine Learning Guide is hosted by OCDevel. The show is categorised under How To (Technology) and has published 30 episodes.
Machine Learning Guide has published 30 episodes.
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