explanation
The advanced machine learning specialty course available on the Coursera website provides, looks, reads and explains the latest technologies, artificial intelligence, familiar tasks and computer programming methods to solve industrial implementation problems of games. . This set consists of seven courses that cover artificial intelligence topics as comprehensively and in detail as possible.
In this first course in the series, you will learn and work with modern neural networks in depth. A second course that teaches how to create competitions related to data science will win and learn advanced topics in this field. In the third period, we are familiar with Bayesian methods for machine learning. The fourth volume, which is related to reinforcement learning, could be the period of the fifth topic of deep learning in vision, Computer explains. The sixth course can be provided by the natural language processing course and LHC’s machine learning solution that meets the duration of the seventh challenge.
If you are teaching a course:
- Working with deep learning and neural networks
- data science
- Bayesian methods for machine learning
- reinforcement learning
- Vision, deep learning on computers
- natural language processing
- Solve LHC challenges through machine learning
Introducing the Advanced Machine Learning Specialization.
- Language: English
- Duration: 214 hours
- Number of courses: –
- Training level: Intermediate
- Instructor: Yevgeny Sokolo
- File format: mp4
this process
Introduction to Optimization
Introduction to Neural Networks
Image deep learning
You can use unsupervised representation learning
Deep learning on sequences
Introduction and Summary
Preprocessing and generating features related to the model
Final Project Description
Exploratory data analysis
Metric Optimization
Hyperparameter optimization
The competition is underway
Introduction to Bayesian inference methods and conjugate priors
Expectation maximization algorithm
Mutational inference and latent Dirichlet allocation
Markov Chain Monte Carlo
Variational autoencoder
Gaussian Process and Bayesian Inference Optimization
Introduction: Why should I care?
The Heart of RL: Dynamic Programming
Method without model
Approximate value-based method
Policy-based method
research
Introduction to Image Processing and Computer Vision
Convolutional functions for visual recognition
object detection
Object tracking and gesture recognition
Image segmentation and compositing
Introduction and text classification
Language modeling and sequence tagging
Vector space model of semantics
Sequencing tasks
conversation system
Introduction to Particle Physics for Data Scientists
particle identification
Search for new physics in rare decays
New CERN experiment searches for dark matter hints through machine learning
Detector optimization
Prerequisite subjects
Prerequisites include calculus and linear algebra (especially derivatives, matrices and operations with them), probability theory (random variables, distributions, moments), basic programming in Python (functions, loops, numpy), basic machine learning (linear models, decision making). trees, boosting and random forests). Our target audience is anyone who is already familiar with basic machine learning and wants to gain hands-on experience with research and development in the field of modern machine learning.
image
sample video
installation manual
Custom view after extraction to player.
Subtitles: English and… .
Quality: 720p
download link
Password file: free download software
file size
10.7GB