A deep understanding of deep learning (with Python intro)
Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.
Deep learning is increasingly dominating technology and has major implications for society. From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology.
But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.
Best Seller Course: Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs
What you’ll learn
- The theory and math underlying deep learning
- How to build artificial neural networks
- Architectures of feedforward and convolutional networks
- Building models in PyTorch
- The calculus and code of gradient descent
- Fine-tuning deep network models
- Learn Python from scratch (no prior coding experience necessary)
- How and why autoencoders work
- How to use transfer learning
- Improving model performance using regularization
- Optimizing weight initializations
- Understand image convolution using predefined and learned kernels
- Whether deep learning models are understandable or mysterious black-boxes!
- Using GPUs for deep learning (much faster than CPUs!)
You May Also Need This Course: PyTorch: Deep Learning and Artificial Intelligence
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