دوره آموزشی یادگیری عمیق (Deep Learning)

انجمن‌های علمی مهندسی صنایع دانشگاه علم و صنعت ایران، خوارزمی، خواجه نصیر و یزد برگزار می‌کنند:

دوره آموزشی یادگیری عمیق (Deep Learning)

مدرس: سجاد حیدری

کارشناسی ارشد مهندسی صنایع دانشگاه تربیت مدرس
تحلیلگر ارشد داده دکتر ساینا
دیتاساینتیست سابق اسنپ فود
مشاور حوزه علوم داده در صنایع مختلف

زمان برگزاری:

شروع از ۱۴ اردیبهشت
پنج شنبه ها و جمعه ها
ساعت ۱۱ تا ۱۴

۳۶ ساعت (۱۲ جلسه ۳ ساعته)

♦ تخفیف زودهنگام ♦

ثبت نام آزاد: ~~800000~~ ⇐ 700000 تومان

ثبت نام دانشجویی: ~~650000~~ ⇐ 550000 تومان

ثبت نام دانشجویان دانشگاه های همکار: ~~500000~~ ⇐ 400000 تومان

سرفصل‌های دوره آموزشی یادگیری عمیق (Deep Learning)

Section 1 (Data Science-Based Basic Python)
- Introduction to Python and applications
- Python editors
- Data Types in Python
- Lists and Tuples in Python
- Numeric and Strings in Python
- What is Booleans
- What is Sets
- Dictionary in Python
- Arrays in Python
- Iterators in Python
- Conditional structures and loops in Python
- Define Functions
- Object Oriented Programming
- Work with Numpy library
- What is Data?
- Importance of Using Data
- Work with Pandas library
- Introduction to Datasets and Basic Operations
Section 2 (Data Analysis)
- What is Data Mining
- Crisp DM Concepts (Essential for a Data Mining Project):

1. 1. Business Understanding
  2. Data Understanding
  3. Data Preparation
  4. Modeling phase
  5. Evaluation phase
  6. Deployment phase

- Pre-Processing Concept
- Data Science and Business are a connection Bridge
- Importance of Sampling and Methods
- Visualization in Python with Matplotlib, Seaborn
- Exploratory Data Analysis (EDA)
- Data Preparation Extended in Python
Section 3 (Basic Concepts of Machine Learning and Deep Learning)
- Introduction to Machine Learning
- Machine learning sections: Supervised Learning, Unsupervised Learning, Reinforcement Learning
- Introduction to Supervised Learning
- Introduction To Unsupervised Learning
- Introduction to Reinforcement Learning
- Introduction to Deep Learning
- Essential Calculus for Machine Learning
- Introduction to Deep Learning
- Calculus for Deep Learning
- Perceptron Definition
- MLP (multi-layer perceptron)
Section 4 (Deep Learning on Supervised Problems)
- Introduction to Supervised Learning
- Train and Test Splitting
- What is Classification?
- Data Preparation Concepts for Classification
- Classification Algorithms: Decision Tree, Logistic Regression, Stochastic Gradient Descent (SGD)
- Validation Concepts for Classification: Confusion Matrix, Precision and Recall, F1-Score
- What is Regression?
- Regression Algorithms: Decision Tree Regression
- Validation Concepts for Regression
- Bias and variance
- Overfitting and Underfitting Concepts
- Data Leakage
- Neural Networks concepts
- Propagations concepts
- What is Activation Function
- Convolutional Neural Networks (CNN)
- Artificial Neural Networks (ANN)
Section 5 (Deep Learning on Unsupervised Problems)
- Introduction to Unsupervised Learning
- Clustering Algorithms: K-means
- Cluster Validation methods
- Autoencoders
- Variational autoencoders
- Generative Adversarial Networks (GAN)
Section 6 (Deep Reinforcement Learning)
- Dynamic Programming concept
- What is Reinforcement Learning
- Elements of RL
- Limitations and Scope
- One-armed Bandit Problem
- K-armed (Multi-armed) Bandit Problem
- Action-Value Methods
- Incremental Implementation
- Tracking a Nonstationary Problem
- Optimistic Initial Values
- Exploration Vs Exploitation
- Optimization and Markov Decision Process
- Feature Selection methods for RL
- RL Algorithms: UCB, SARSA
- Deep Learning on Reinforcement Learning: DSN, DDPG
Section 7 (Deep Learning on Time Series Forecasting)
- Introduction to Time Series
- Time Series Algorithms: ARIMA
- Deep Learning on Time Series Forecasting: LSTM, CNN-LSTM