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Deep Learning MasterClass [Free Online Course] - TechCracked

Deep Learning MasterClass

Learn about Complete Life Cycle of a Deep Learning Project. Implement different Neural networks using Tensorflow & Keras

This course includes:

  • 4.5 hours on-demand video
  • 43 downloadable resources
  • Access on mobile and TV
  • Full lifetime access
  • Certificate of completion


What you'll learn

  • You will learn the complete life cycle of a Data Science Project with Machine Learning and Deep Learning.
  • Learn about different Neural Networks like ANN, CNN and RNN.
  • Learn about pandas, numpy, matplotlib, sklearn, tensorflow that are some of the most important python libraries used in Data Science, ML and DL.
  • You will build practical projects like Gold Price Prediction, Image Class Prediction and Stock Price Prediction using different Neural networks.


Description

Deep learning is a subfield of machine learning that is focused on building neural networks with many layers, known as deep neural networks. These networks are typically composed of multiple layers of interconnected "neurons" or "units", which are simple mathematical functions that process information. The layers in a deep neural network are organized in a hierarchical manner, with lower layers processing basic features and higher layers combining these features to represent more abstract concepts.

Deep learning models are trained using large amounts of data and powerful computational resources, such as graphics processing units (GPUs). Training deep learning models can be computationally intensive, but the models can achieve state-of-the-art performance on a wide range of tasks, including image classification, natural language processing, speech recognition, and many others.

There are different types of deep learning models, such as feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and many more. Each type of model is suited for a different type of problem, and the choice of model will depend on the specific task and the type of data that is available.


Also See : Data Science and Machine Learning Basic to Advanced

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