Deep Learning Overview Classification Types Examples And Limitations
0 likes202 views
Deep learning is a subfield of machine learning focused on algorithms modeled after the brain, using artificial neural networks for tasks like classification based on diverse input types. It includes various network types such as convolutional and recurrent neural networks, with applications in image recognition, natural language processing, and healthcare. However, deep learning faces limitations concerning interpretability and the amount of data needed for effective learning.
Deep Learning Overview Classification Types Examples And Limitations
- 1. Deep Learning Overview Classification Types Examples and Limitations Yo u r C o m p a n y N a m e
- 2. 2 Deep Learning o What is Deep Learning? o Deep learning Process o Classification of Neural Networks o Types of Deep Learning Networks o Feed-forward neural networks o Recurrent neural networks (RNNs) o Convolutional neural networks (CNN) o Reinforcement Learning o Examples of deep learning applications o Why is Deep Learning Important? o Limitations of deep learning 01
- 3. What is Deep Learning? 3 Deep Learning is a subfield of machine learning that is concerned with algorithms inspired by the brain's structure and functions known as artificial neural networks. A computer model can be taught using Deep Learning to run classification actions using pictures, texts or sounds as input. Feature Extraction + ClassificationInput Output Car Not Car What is Deep Learning? This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 4. Deep Learning Process 4 Understand the Problem Identify Data Select Deep Learning Algorithms Training the Model Test the Model This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 5. Classification of Neural Networks 5 x1 x2 xn v11 v12 vpn w11 w22 wmp ym y2 y1 1 2 p 1 2 m V1n w1p Input Layer Hidden Layer Output Layer This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 6. Types of Deep Learning Networks 6 Supervised Artificial Neural Networks Used for Regression & Classification Convolutional Neural Networks Used for Computer Vision Recurrent Neutral Networks Used for Time Series Analysis Unsupervised Self-Organizing Maps Used for Feature Detection Deep Boltzmann Machines Used for Recommendation Systems AutoEncoders Used for Recommendation Systems o Artificial Neural Networks (ANN) o Convolutional Neural Networks (CNN) o Recurrent Neural Networks (RNN) o Self Organizing Maps (SOM) o Boltzmann Machines (BM) o AutoEncoders (AE) Supervised Unsupervised Deep Learning Models This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 7. Feed-forward Neural Networks 7 Input Layer Hidden Layer Output Layer Variable- #1 Variable- #2 Variable- #3 Variable- # 4 Output This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 8. Recurrent Neural Networks (RNNs) 8 x1 x2 y Input Layer Recurrent Network Output Layer Hidden Layers This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 9. Convolutional Neural Networks (CNN) 9 Take Car = A1 Truck = B1 VAN = C1 Bicycle = D1 Rest all be Same This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 10. Reinforcement Learning 10 Reinforcement Learning uses rewards and punishment to train computing models to perform a sequence of selections. Here computing faces a game-like scenario where it employs trial and error to answer. Based on the action it performs, computing gets either rewards or penalties. Its goal is to maximize the rewards. Exploration Policy Neural Networks Filters Memory Algorithm Agent Environment Action State, Reward This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 11. Examples of Deep Learning Applications 11 Image Recognition Portfolio Management & Prediction of Stock Price Movements Speech Recognition Natural Language Processing Drug Discovery & Better Diagnostics of Diseases in Healthcare Robots and Self - Driving Cars This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 12. Why is Deep Learning Important? 12 Deep Learning Other Learning Algorithms Performance Data This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 13. Limitations of Deep Learning 13 Limitations of Deep Learning This slide is 100% editable. Adapt it to your needs and capture your audience's attention. Interpretability Statistical ReasoningAmount of Data