Deep Learning and Neural Networks
Artificial Intelligence
| Beginner
- 11 videos | 1h 20m 8s
- Includes Assessment
- Earns a Badge
Deep learning and neural networks have revolutionized various fields by enabling computers to automatically learn complex patterns from data. This led to breakthroughs in areas such as image recognition, natural language processing (NLP), and autonomous driving. In this course, you will compare and contrast traditional machine learning (ML) and deep learning models. You will see how deep learning models excel in automated feature extraction from raw data, tackling complex tasks with the power of vast datasets. You will explore the fundamental unit of deep learning, the neuron, and understand how it works. Next, you will explore the diverse neural network architectures designed for specific data types. You will learn how convolutional neural networks (CNNs) extract features from images and how recurrent neural networks (RNNs) are able to extract relationships in time-series data. Finally, you will explore how neural networks handle natural language processing. You will learn how attention-based models help models focus on crucial parts of the input data for enhanced predictions and how generative adversarial networks (GANs) work. You will also explore reinforcement learning, a machine learning technique where agents navigate uncertain environments to maximize rewards.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
Outline how deep learning models work
Summarize how neurons transform inputs
Outline how neural network models can be trained
Explain how convolutional neural networks (cnns) work
Explain how recurrent neural networks (rnns) work -
Recognize how text data is fed into neural networks
Outline the use of attention-based models
Recognize how generative adversarial networks (gans) are implemented
Outline how reinforcement learning algorithms work
Summarize the key concepts covered in this course
IN THIS COURSE
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2m 18sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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11m 2sUpon completion of this video, you will be able to outline how deep learning models work. FREE ACCESS
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3. Understanding Neurons4m 11sAfter completing this video, you will be able to summarize how neurons transform inputs. FREE ACCESS
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4. Training a Neural Network8m 14sUpon completion of this video, you will be able to outline how neural network models can be trained. FREE ACCESS
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5. Convolutional Neural Networks (CNNs)10m 6sAfter completing this video, you will be able to explain how convolutional neural networks (CNNs) work. FREE ACCESS
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6. Recurrent Neural Networks (RNNs)9m 25sUpon completion of this video, you will be able to explain how recurrent neural networks (RNNs) work. FREE ACCESS
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7. Natural Language Processing (NLP)10m 23sAfter completing this video, you will be able to recognize how text data is fed into neural networks. FREE ACCESS
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8. Attention-based Models and Transformers7m 48sUpon completion of this video, you will be able to outline the use of attention-based models. FREE ACCESS
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9. Generative Adversarial Networks (GANs)7m 40sAfter completing this video, you will be able to recognize how generative adversarial networks (GANs) are implemented. FREE ACCESS
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10. Reinforcement Learning6m 21sUpon completion of this video, you will be able to outline how reinforcement learning algorithms work. FREE ACCESS
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11. Course Summary2m 39sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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