
Course Features
- Lectures 11
- Quizzes 0
- Duration 2 day
- Skill level Mid Level
- Language English
- Students 0
- Assessments Yes
About Two Days AI & Deep Learning workshop
We will conduct two days AI & Deep Learning workshop is a fast-paced program designed to give the students hands-on experience in AI. This workshop is primarily targeted at students who wish to discover the new technology by hands on training session. In this workshop, The objective of the workshop is to introduce fundamentals of artificial intelligence, convolutional neural networks (CNN), perceptron in CNN, TensorFlow, TensorFlow code, transfer learning, graph visualization, recurrent neural networks (RNN), Deep Learning libraries, GPU in Deep Learning, Keras and TFLearn APIs, backpropagation, and hyperparameters via hands-on projects.
- Pattern recognition, Image classification techniques.
- Understand the intuition behind Artificial Neural Networks
- Apply Artificial Neural Networks in practice
- Understand the intuition behind Convolutional Neural Networks
- Apply Convolutional Neural Networks in practice
Two Days AI Course Outlines
Introduction Artificial Intelligence
- AI Applications, Types of Learning, Supervised,
- Unsupervised and Reinforcement learning.
- Artificial Neural Networks (ANNs) Concept,
- Feed Forward Neural Networks and Back Propagation
Getting Started with python
- Environment Setup
- Setting up Anaconda, Spyder/VS Code
- Installing Libraries
- Working with Python Framework for Data Science
- Python for Data Analysis-NumPy
Artificial Neural Networks
- The Neuron Diagram Neuron Models & Neural Network
- Functioning of Neurons Activation functions Gradient Descent,
- Stochastic Descent, ramp function, sigmoid function, Gaussian function
- single-layer feed-forward, Multi-layer feed-forward,
CNNs (Convolutional Neural Networks)
- What is a convolutional neural network?
- understanding the architecture and use-cases of CNN,
- how to visualize using CNN,
- how to fine-tune a convolutional neural network,
- understanding recurrent neural networks
- deploying convolutional neural networks in TensorFlow.
RNNs (Recurrent Neural Networks)
- Introduction to the RNN model
- Use cases of RNN,
- training RNNs with back propagation,
- long short-term memory (LSTM),
- Recursive Neural Tensor Network theory,
- the basic RNN cell, unfolded RNN, RNN training, dynamic RNN, and time-series predictions.
Artificial Intelligence Projects
- For recognizing handwritten digits (MNIST dataset)
- Image Recognition with TensorFlow
- Ecommerce Product Recommendation
- Modern Face Recognition with Deep Learning
- Case Study: Cancer Detection, Character Recognition, Iris Clustering
- Case Study: Intelligent Washing Machine Design
- Develop a predictive analytics model for a complex dataset
Introduction to Python crash course
Introduction to AI
Creating First ANN
Convolutional Neural Networks
Hands-on AI Projects
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