Details
- This course is a strong foundation in concepts and applications of deep learning, it's built with both deep theory understanding and hands on applications in mind.
- It starts with the basic mathematics you need to jump into machine learning and also a review for advanced topics in programming which is algorithms to help students with their practical side of the course.
- After introduction, the course is divided into two tracks the first is computer vision and the second NLP .
- Computer vision with Deep learning (advanced)
- This course focuses on the applications of deep learning in the field of machine vision, it covers a lot of the advanced topics and state of the art solutions (image classification, object detection, semantic segmentation, transfer learning).
- The second part of the course is an introduction to natural language processing in which we cover the basic steps to continue in NLP.
Outline
Deep Learning:
- Algorithms 1
- Algorithms 2
- Calculus 1
- Stochastic gradient descent
- Backpropagation
- Neural Networks Part 1: Setting up the Architecture
- model of a biological neuron
- activation functions
- neural net architecture
- representational power
- Neural Networks Part 2: Setting up the Data and the Loss
- Preprocessing
- Weight initialization
- Batch normalization
- Regularization (L2/dropout)
- Loss functions
- Neural Networks Part 3: Learning and Evaluation
- Gradient checks
- Momentum
- Adagrad/RMSprop
- hyperparameter optimization
- model ensembles
- Deep Learning for Computer Vision
- Images processing 1
- Image processing 2
- Convolutional Neural Networks
- layers, spatial arrangement, layer patterns, layer sizing patterns
- AlexNet/ZFNet/VGGNet case studies
- computational considerations
- Understanding and Visualizing Convolutional Neural Networks
- tSNE embeddings
- Deconvnets
- data gradients
- fooling ConvNets
- human comparisons
- Transfer Learning and Fine-tuning Convolutional Neural Networks
- Object detection
- Intro to semantic segmentation, instance segmentation
- U-net architecture
- Post processing
- Deep Learning for NLP (intro)
- Regular expressions & word tokenization
- Simple topic identification
- Named-entity recognition
- Project
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Since 1995, CLS Learning solutions is leading the technology learning market in Egypt, the Middle East, and Africa. With our wide network of international partners, trainers, instructors, and technology leaders; we are able to deliver top notch training programs to our students and technology professionals.
25 Years in the market.
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