Deep Learning and Reinforcement Learning Training is a 2-day course offered by 5gworldpro.com. The course aims to provide participants with a comprehensive understanding of deep learning and reinforcement learning. The program covers the basics of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. Additionally, the course delves into reinforcement learning, including its applications and algorithms such as Q-learning, SARSA, and deep reinforcement learning. The training is intended for individuals seeking to gain practical knowledge and hands-on experience in machine learning applications.
Deep Learning and Reinforcement Learning Training
- Created by 5GWORLDPRO
- Last updated 12/2022
- 67,197 students enrolled
Course available in Live Virtual classroom TrainingÂ
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This course includes:
- 7 hours on-demand video
- Downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
Learning Path
- What is deep learning?
- Applications of deep learning
- Basic terminology (neural networks, layers, activation functions, etc.)
- Neural Networks and Activation Functions
- Types of layers (input, hidden, output)
- Common activation functions (sigmoid, ReLU, etc.)
- Hands-on exercise: Implementing a simple neural network from scratch
- Loss functions and optimization algorithms (gradient descent, backpropagation)
- Regularization techniques (dropout, weight decay)
- Tips for effective training (learning rate, batch size, etc.)
- Hands-on exercise: Training a neural network using a deep learning framework (e.g.,
TensorFlow, PyTorch)
- Motivation and architecture of CNNs
- Convolutional layers, pooling layers
- Common CNN architectures (LeNet, AlexNet, etc.)
- Hands-on exercise: Implementing a CNN for image classification
- Introduction to sequential data processing
- Structure and functioning of RNNs
- Applications of RNNs (sequence generation, language modeling, etc.)
- Hands-on exercise: Building a simple RNN for text generation
- Leveraging pretrained models for transfer learning
- Fine-tuning and feature extraction
- Hands-on exercise: Using a pretrained model for a different task
- Introduction to generative models
- GAN architecture and training process
- Generating realistic images with GANs
- Hands-on exercise: Creating a basic GAN for image generation
- Overview of deep learning applications in various domains (computer vision, natural
- language processing, etc.)
o Recent advancements and future trends in deep learning
- What is RL and why is it important?
- Key concepts: agent, environment, states, actions, rewards
- RL terminology (Markov Decision Processes, policy, value functions, etc.)
- Formal definition of MDPs
- Value iteration and policy iteration algorithms
- Hands-on exercise: Solving a simple MDP problem
- Introduction to Q-Learning
- Exploration vs. exploitation trade-off
- TD learning and the Q-Learning algorithm
- Hands-on exercise: Implementing Q-Learning for a simple RL problem
- Challenges of using Q-Learning with large state spaces
- Introduction to DQNs and the role of deep neural networks
- Experience replay and target networks
- Hands-on exercise: Building a DQN for a RL task using a deep learning framework
- Introduction to policy gradients
- Policy parameterization and the policy gradient theorem
- REINFORCE algorithm
- Hands-on exercise: Implementing a basic policy gradient
Overview
Deep Learning and Reinforcement Learning Training is a 2-day course offered by 5gworldpro.com. The course aims to provide participants with a comprehensive understanding of deep learning and reinforcement learning. The program covers the basics of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. Additionally, the course delves into reinforcement learning, including its applications and algorithms such as Q-learning, SARSA, and deep reinforcement learning. The training is intended for individuals seeking to gain practical knowledge and hands-on experience in machine learning applications.
Who is the course for?
The professionals who want to expand their knowledge and skills in machine learning and artificial intelligence. This course is suitable for those who are interested in developing machine learning applications and those who wish to pursue a career in machine learning. The training is also useful for data analysts, data scientists, software engineers, and researchers who want to leverage deep learning and reinforcement learning algorithms in their work.
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