Published on: 2020-11-26 12:18:06
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Advanced Machine Learning Specialization courses provided from the website Coursera is to provide you with the newest techniques, artificial intelligence, familiar works and how to program the computer to solve the problems of industrial implementation of the game., seeing, reading, and talking to explains. This set consists of 7 courses is that the topics of artificial intelligence to comprehensively and detailed as you can.
The first course of this series you will learn deep and work with neural networks, modern meet. A second course to teach you how to make a competition related to data science will win, and advanced topics, this field learn. In the third period with methods Bayesian for machine learning are familiar. The fourth volume related to reinforcement learning can be and the period of the fifth topics of deep learning in vision, computer explains. Course sixth you with natural language processing to meet, and the period of the seventh challenge, the LHC’s machine learning solution can provide.
Introduction to optimization
Introduction to neural networks
Deep Learning for images
We can use unsupervised representation learning
Deep learning for sequences
Introduction & Recap
Feature Preprocessing and Generation with Respect to Models
Final Project Description
Exploratory Data Analysis
Metrics Optimization
Hyperparameter Optimization
Competitions go through
Introduction to Bayesian inference methods & Conjugate priors
Expectation-Maximization algorithm
Variational Inference & Latent Dirichlet Allocation
Markov chain Monte Carlo
Variational Autoencoder
Gaussian processes & Bayesian inference optimization
Intro: why should I care?
At the heart of RL: Dynamic Programming
Model-free methods
Approximate Value Based Methods
Policy-based methods
Exploration
Introduction to image processing and computer vision
Convolutional features for visual recognition
Object detection
Object tracking and action recognition
Image segmentation and synthesis
Intro and text classification
Language modeling and sequence tagging
Vector Space Models of Semantics
Sequence to sequence tasks
Dialog systems
Introduction into particle physics for data scientists
Particle identification
Search for New Physics in Rare Decays
Search for Dark Matter Hints with Machine Learning at the new CERN experiment
Detector optimization
As prerequisites, we assume 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 trees, boosting and random forests). Our intended audience are all people who are already familiar with basic machine learning and want to get a hand-on experience of research and development in the field of modern machine learning.
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Subtitles: English and ….
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