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on
1
- Intro and outline
2
- TensorFlow.js demos + discussion
3
- AI vs ML vs DL
4
- What’s representation learning?
5
- A cartoon neural network more on this later
6
- What features does a network see?
7
- The “deep” in “deep learning”
8
- Why tree-based models are still important
9
- How your workflow changes with DL
10
- A couple illustrative code examples
11
- What’s a hyperparameter?
12
- The skills that are important in ML
13
- An example of applied work in healthcare
14
- Families of neural networks + applications
15
- Encoder-decoders + more on representation learning
16
- Families of neural networks continued
17
- Are neural networks opaque?
18
- Building up from a neuron to a neural network
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- A demo of representation learning in TF Playground
20
- Importance of activation functions
21
- What’s a neural network library?
22
- Overfitting and underfitting
23
- Autoencoders and anomaly detection screencast and demo
24
- Book recommendations
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Dive into a comprehensive 1-hour 14-minute ML Tech Talk that provides an in-depth overview of Deep Learning. Explore representation learning, various neural network families and their applications, and gain insights into the inner workings of deep neural networks. Learn through numerous code examples and key concepts from TensorFlow. Discover the differences between AI, ML, and DL, understand hyperparameters, and explore the skills crucial for machine learning. Examine real-world applications in healthcare, investigate different neural network architectures, and delve into topics like overfitting, underfitting, and autoencoders. Benefit from book recommendations and access a wealth of helpful resources, including demos, tutorials, and additional courses to further your deep learning journey.

Intro to Deep Learning - ML Tech Talks

TensorFlow
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