بمناسبة العام الجديد : خصم 50% على دورة Deep Learning, Image Processing and Classification على البايثون !
الان ب 150 دينار اردني بدل من 300 دينار اردني !
دورة شاملة وعملية على البايثون مع دكتور مختص من المانيا ..
والخصم لنهاية شهر 2 فقط !! سجل واحجز مقعدك الان قبل اكتمال العدد !!
حيث أننا قدمنا من قبل ورشة عمل مكثفة في هذا المجال ( بإمكانكم مشاهدة الورشة كاملة على قناة اليوتيوب ATIT Academy )
*عدد ساعات الدورة : حوالي 30 ساعة
للتسجيل او الاستفسار :
00962795037290
info@atitgroup.XXX
Skype : ATITAcademy
This comprehensive course will be covered over 9 sessions as detailed below:
1) Introduction to Artificial Intelligence and Deep Learning
- What is Artificial Intelligence (AI) – What is Deep Learning (DL) – Types of DL:
• Convolution Neural Network (CNN)
• Recurrent Neural Network (RNN)
• Long Short Term Memory (LSTM)
• Reinforcement Learning (RL) and Deep Q-Network (DQN)
• Generative Adversarial Network (GAN)
- Applications on DL
- Operations of DL
- Practical Examples
2) Introduction to Python
- Python Basics
- Installing Python
- PIP packages installer
- Python Variables
- Input and Output
- If…Then…Else
- Loops
- Collections
- Functions
- Error Handling
- Practical Project
3) Python for Deep Learning and Image Processing
- Data Manipulation
- Normalizing data
- Formatting data
- Important Python Packages for Image Processing and Deep Learning:
• OpenCV
• Tensorflow
• Keras
• Dlip
- Practical Project
4) Optimization
- Optimization Overview
- DL as an optimization problem
- Types of Optimizers (Teachers)
- Optimization Approach Components
- Formulating an Objective Function
- Solving a maximization problem
- Solving a minimization problem
- Producing Convergence Curve
- Practical Project on real functions
5) DNN Layers, Activation and Loss Functions – Input Layer – Hidden Layer:
- Convolution Layers
- Max pooling Layers
- Classification Layer
- Output Layer
- Dropout Layer – Fully Connected Layers – Activation Functions:
• RELU
• Sigmoid
• Softmax
- Loss Functions:
• Mean Square Error
• Cross-Entropy Loss
- Practical Project
6) Data Preparation – Data Labeling:
• Region of Interest (Bounding Box)
• Class (Group)
• Semantic labeling
- Multi Class vs Multi Label
- Data Normalization – Batching Data – Data Splitting:
- Training Dataset
- Validation Dataset
- Testing Dataset
- Cross Validation
- Data Preparation Complete Project
7) Computer Vision and Image Processing
- Image Formation and Representation
- Geometric Transformation
- Image Registration
- Image Clustering
- Background and Foreground Objects – Edge Detection – Feature Descriptors:
• The histogram of oriented gradients (HOG)
• SIFT
• SURF
- Image Processing Complete Project
8) Clustering and Classification
- Clustering Vs Classification
- Image Classification in details
- CNN in details
- Images Classification Project 1 (General Dataset)
- Images Classification Project 2 (Medical Dataset)
9) Real-Time Face Detection
- Working with video and frames
- Viola-Jones method
- Face Detection
- Face Landmarks
- Facial Expression Recognition
- Project 1: Face Detection
- Project 2: Expression Recognition
- Project 3: Data Generation using GANs
