English | Size: 7.03 GB
Genre: eLearning
Learn
Learn to create Deep Neural networks and machine learning models for complex real-world problems
Get comfortable with Deep Learning libraries like TensorFlow and Keras
Learn inner workings of Convolutional Networks and Computer Vision
Work with AlexNet, GoogleNet, and ResNet
Recurrent Neural Networks
About
Deep learning is an artificial intelligence function that mimics the inner workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks of interconnected nodes capable of un-supervised learning from data that is unstructured or unlabelled training data. It also enables representation of data in form of abstract features and classifies them into sub-classes which may be too complex for traditional machine learning models.
One of the most common AI techniques used for processing big data is machine learning, a self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with newly added data. As more and more sources of data-generation are coming into picture, the number of file formats is increasing as well. Now, designing one model to merge data from these many sources and extract meaningful insights is not possible with traditional hand-coded programs. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a non-linear approach. While this may sound daunting, Deep Learning algorithms handle such tasks with ease. The scope of implementation in various sectors is just limitless.
Features
Learn to implement machine learning models in Tensor Flow and Keras
Complete introduction to advance level concepts in Recurrent Neural Networks (RNN)
A Practical course with multiple real-life projects in Deep learning
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