Introduction to Data-Centric AI Neural Networks: Zero To Hero A course by Andrej Karpathy on building neural networks, from scratch, in code. We start with the basics of backpropagation and build up to modern deep neural networks, like GPT. In my opinion language models are an excellent place to learn deep learning, even if your intention is to eventually go to other areas like computer vision because most of what you learn will be immediately transferable. This is why we dive into and focus on languade models. Prerequisites: solid programming (Python), intro-level math (e.g. derivative, gaussian). 2h25m This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. 1h57m We implement a bigram character-level language model, which we will further complexify in followup videos into a modern Transformer language model, like GPT. 1h15m We implement a multilayer perceptron (MLP) character-level language model. 1h55m 56m 1h56m 2h13m ongoing...
Foundations of Machine Learning Bloomberg presents "Foundations of Machine Learning," a training course that was initially delivered internally to the company's software engineers as part of its "Machine Learning EDU" initiative. This course covers a wide variety of topics in machine learning and statistical modeling. The primary goal of the class is to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning. The 30 lectures in the course are embedded below, but may also be viewed in this YouTube playlist. Please fill out this short online form to register for access to our course's Piazza discussion board. The first lecture, Black Box Machine Learning, gives a quick start introduction to practical machine learning and only requires familiarity with basic programming concepts. The 30 lectures in the course are embedded below, but may also be viewed in this YouTube playlist.
Transformer models - Hugging Face Course Welcome to the 🤗 Course! This course will teach you about natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. It’s completely free and without ads. What to expect? Here is a brief overview of the course: Chapters 1 to 4 provide an introduction to the main concepts of the 🤗 Transformers library. This course: Requires a good knowledge of PythonIs better taken after an introductory deep learning course, such as fast.ai’s Practical Deep Learning for Coders or one of the programs developed by DeepLearning.AIDoes not expect prior PyTorch or TensorFlow knowledge, though some familiarity with either of those will help After you’ve completed this course, we recommend checking out DeepLearning.AI’s Natural Language Processing Specialization, which covers a wide range of traditional NLP models like naive Bayes and LSTMs that are well worth knowing about! Who are we?
Foundations of Machine Learning Bloomberg presents "Foundations of Machine Learning," a training course that was initially delivered internally to the company's software engineers as part of its "Machine Learning EDU" initiative. This course covers a wide variety of topics in machine learning and statistical modeling. The primary goal of the class is to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning. It is designed to make valuable machine learning skills more accessible to individuals with a strong math background, including software developers, experimental scientists, engineers and financial professionals. The 30 lectures in the course are embedded below, but may also be viewed in this YouTube playlist. Please fill out this short online form to register for access to our course's Piazza discussion board.
1.0 - Table of Contents.ipynb - Colaboratory ML - hands-on 1 : Discover pandas, a useful library for treating tabular data. Try it out with a k-nearest neighbors classifier ML - hands-on 2 : Pandas again, Polynomial regression, Decision tree ML - hands-on 3 : Pandas again, SVM, ROC-AUC curve. We did not have time to talk about that, and we probably won't. ML - hands-on 4 : Pandas again, linear regression, random forest. ML - hands-on 1 - Correction ML - hands-on 2 - Correction ML - hands-on 3 - Correction ML - hands-on 4 - Correction
Deep Learning course: lecture slides and lab notebooks | lectures-labs This course is being taught at as part of Master Datascience Paris Saclay Table of contents The course covers the basics of Deep Learning, with a focus on applications. Lecture slides Note: press “P” to display the presenter’s notes that include some comments and additional references. Lab and Home Assignment Notebooks The Jupyter notebooks for the labs can be found in the labs folder of the github repository: git clone These notebooks only work with keras and tensorflow Please follow the installation_instructions.md to get started. Direct links to the rendered notebooks including solutions (to be updated in rendered mode): Lab 1: Intro to Deep Learning Lab 2: Neural Networks and Backpropagation Lab 3: Embeddings and Recommender Systems Lab 4: Convolutional Neural Networks for Image Classification Lab 5: Deep Learning for Object Dection and Image Segmentation Lab 6: Text Classification, Word Embeddings and Language Models Lab 8: Intro to PyTorch License
Masinõpe - Kursused - Arvutiteaduse instituut I. Association rules and decision trees Given by Sven Laur Brief summary: Advantages and drawbacks of machine learning. When is it appropriate to use machine and when knowledge based modelling is more appropriate. overview of standard experiment design. Potential applications and limits of machine learning. Slides: PDF Video: UTTV(2013) Literature Lecture slides by Tom Mitchell Thomas Mitchell: Machine learning (1997) pages 52 - 80 Complementary exercises Free implementations
A Course in Machine Learning COMS W4721 Machine Learning for Data Science @ 422 Mudd BuildingSynopsis: This course provides an introduction to supervised and unsupervised techniques for machine learning. We will cover both probabilistic and non-probabilistic approaches to machine learning. Focus will be on classification and regression models, clustering methods, matrix factorization and sequential models. Methods covered in class include linear and logistic regression, support vector machines, boosting, K-means clustering, mixture models, expectation-maximization algorithm, hidden Markov models, among others. We will cover algorithmic techniques for optimization, such as gradient and coordinate descent methods, as the need arises. Prerequisites: Basic linear algebra and calculus, introductory-level courses in probability and statistics. Text: There is no required text for the course. T.
I hope more stuff on this interesting topic becomes available. by electronics Mar 24
Indeed, neural networks in the nano technology age could have lots of interesting forms--as chemistry changes at smaller scales. by electronics Mar 24
This is excellent. It is hard to find serious, intelligent work with Neural Nets and artificial intelligence. I hope to be of some use and hope that I can become part of the community
triad by triad Mar 14