Artificial Neural Networks, Machine Learning, Deep Thinking Training

Artificial Neural Networks, Machine Learning, Deep Thinking

Unravel the intricacies of Artificial Neural Networks and delve deep into the realms of Machine Learning and Deep Thinking.

This intensive course offers a comprehensive introduction to cutting-edge neural network models, the foundational principles of machine learning, and advanced deep learning concepts. Ideal for those with a strong mathematical foundation, it promises a transformative journey from theory to application.

What will you learn?

Elevate Your Understanding of Neural Networks, Machine Learning, and Deep Thinking. In this intensive three-day course, participants with a passion for AI will:

Demystify Neural Networks: Understand the biological inspirations and the artificial adaptations.

Foundation in Machine Learning: Delve into the core principles, from the PAC Learning Framework to Support Vector Machines.
Deep Dive into Deep Learning: Transition from basic neural network models to advanced deep learning techniques, including convolution, pooling, and sparse coding.
Applications Galore: Discover practical applications and see these concepts come alive in real-world scenarios.
Practical Insights: Get equipped with best practices and design considerations to implement these models in real-world tasks.

Requirements:

Strong grasp of mathematics.
Basic understanding of statistics.
Optional: Familiarity with programming concepts will be beneficial.

Course Outline*:

*We know each team has their own needs and specifications. That is why we can modify the training outline per need.

1. Foundations of Artificial Neural Networks (ANN)

Biological vs. Artificial Neurons: A Comparative Analysis
Core Components and Activation Functions of ANNs
Exploring Various Network Architectures and Their Uses

2. Deep Dive into Learning Mechanisms

Vector, Matrix Algebra, and State-Space Concepts
Techniques of Error-Correction, Memory-Based, Hebbian, and Competitive Learning

3. Unraveling the Layers: From Perceptrons to Feedforward ANNs

Perceptron Convergence and Limitations
Understanding the Multi-Layer Feedforward Networks
The Backpropagation Algorithm: Training, Convergence, and Practical Insights

4. Radial Basis Function Networks and Competitive Learning

Pattern Separability, Regularization, and Interpolation Techniques
Clustering, Learning Vector Quantization, and Feature Maps

5. Fuzzy Neural Networks: Bridging Uncertainty

Foundations of Fuzzy Sets, Logic, and ANN Designs

6. Machine Learning Essentials

PAC Learning, Deterministic vs. Stochastic Scenarios, and Model Selection
Dive into Support Vector Machines, Kriging, PCA, and Kernel PCA
The Role and Impact of Reinforcement Learning

7. Deep Learning: Advanced Concepts and Techniques

Logistic Regression, Sparse Autoencoders, and Vectorization
Delve into Convolution, Pooling, Sparse Coding, and Independent Component Analysis

8. Applications: Real-World Implementation, Benefits, and Challenges of Neural Network Models.

Hands-on learning with expert instructors at your location for organizations.

Level:

Intermediate

Duration:

Hours (days:

)

Training customized to your needs

Immersive hands-on experience in a dedicated setting

*Price can range depending on number of participants, change of outline, location etc.

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Master new skills guided by experienced instructors from anywhere.

Level:

Intermediate

Duration:

Hours (days:

)

Training customized to your needs

Reduced training costs

*Price can range depending on number of participants, change of outline, location etc.

Get Quote

You can participate in a Public Course with people from other organisations.

/per trainee

Thanks for the numbers, they could be going to your emails. But they're going to mine... Thanks ;D

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Level:

Intermediate

Duration:

Hours (days:

)

Fits ideally for individuals and small groups

Networking opportunities with fellow participants.

*Price can range depending on number of participants, change of outline, location etc.

Get Quote

Artificial Neural Networks, Machine Learning, Deep Thinking

What will you learn?

Requirements:

Course Outline*:

1. Foundations of Artificial Neural Networks (ANN)

2. Deep Dive into Learning Mechanisms

3. Unraveling the Layers: From Perceptrons to Feedforward ANNs

4. Radial Basis Function Networks and Competitive Learning

5. Fuzzy Neural Networks: Bridging Uncertainty

6. Machine Learning Essentials

7. Deep Learning: Advanced Concepts and Techniques

8. Applications: Real-World Implementation, Benefits, and Challenges of Neural Network Models.

Get Quote for:

Artificial Neural Networks, Machine Learning, Deep Thinking

Start your training experience here!