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### Learn how to apply Deep Learning with PyTorch and TensorFlow

- The course will teach you how to develop Deep Learning models using Pytorch while providing the necessary deep-learning background.

- We'll start off with PyTorch's tensors and its Automatic Differentiation package. Then we'll cover different Deep Learning models in each section, beginning with fundamentals such as Linear Regression and logistic/softmax regression.
- We'll then move on to Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers.

- Explain and apply knowledge of Deep Neural Networks and related machine learning methods;
- Know how to use Python, and Python libraries such as Numpy and Pandas along with the PyTorch library for Deep Learning applications;
- Build Deep Neural Networks using PyTorch.

- Explain foundational TensorFlow concepts such as the main functions, operations, and execution pipelines.
- Describe how TensorFlow can be used in curve fitting, regression, classification, and minimization of error functions.
- Understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Autoencoders.
- Apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.