Deep Learning with Real World Projects [Video]

• 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

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.

• 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