Deep Learning with Python Training

Mindmajix Deep learning with Python Training helps you master various features of Debugging concepts, Introduction to Software programmers, Language abilities and capacities, Modification of module and pattern designing, Various OS and compatibilities approaches.

This course also provides Optimizing a Simple Model in Pure Theano, Convolutional and Pooling Layers , Reduce Overfitting With Dropout Regularization, Improve Generalization With Data Augmentation. 

This Deep Learning with Python training course helps you acquire an in-depth and profound knowledge of deep learning. With this course, you can gain exposure to the best industry practices. With a hands-on practical approach, you get to work on real-time project scenarios.

The curriculum of this training course is the latest and updated as per the industry standards. Right from helping you get introduced to the schema to validating maps and a lot more can be learned from this course. 

Deep Learning with Python Training Content:

• You will be exposed to the complete Deep Learning with Python Training course details in the below sections.
• Library used in Deep Learning
• Artificial Neural Networks
• Image Preprocessing
• Convolutional Neural Networks
• Recurrent Neural Networks
• Self Organizing Maps
• Natural Language Processing (NLP with Implementation)

What Skills Will You Learn In This Deep Learning With Python Training?

• This Deep Learning with Python training can help you learn the following skills:
• Grasp everything related to applications of RNN, including human activity recognition, image captioning, machine translation, speech recognition, and more.
• Comprehend the basics of Deep Neural networks (DNN) and learn neurons and perception.
• Get introduced to RNN architecture, including fixed length memory model, infinite memory architecture, and more.
• Find out the basic and advanced aspects of gradient descent in RNN with an example setup.
• Familiarize yourself with how to vanish gradients in RNN and know about gated recurrent units, long short-term memory, and more.
• Know how to classify TensorFlow text with the help of RNNs.
• Learn everything about data mapping, modeling RNN architecture, modeling RNN model in TensorFlow, and more.
• Understand stock price prediction, problem statement, dataset, RNN model training and evaluation, and data preparation.