Deep Learning with TensorFlow 2 Certification Training

Deep learning with TensorFlow training is a course that teaches you to build and train neural networks with TensorFlow 2 and Keras. It covers everything from perceptrons to CNNs, object detection, GANs and LSTMs, so you can take on deep learning and AI roles and grow your career.

If you know Python and want a career in data science or AI, this course is a great fit:

• It covers deep learning algorithms built with TensorFlow 2.
• Keras is built into TensorFlow 2, which makes it more powerful and easier to use.
• Code in TensorFlow 2 is much simpler than in earlier versions.
• TensorFlow is one of the most widely used deep learning libraries.
• You learn both image processing and text processing.

After this course, you will be able to:

• Work confidently with TensorFlow 2
• Implement single-layer and multi-layer perceptrons
• Explain how CNNs work and classify images with a trained model
• Use transfer learning, R-CNN, Fast R-CNN, RoI pooling, Faster R-CNN and Mask R-CNN
• Explain Boltzmann machines and autoencoders
• Implement a generative adversarial network (GAN)
• Build an emotion and gender detection project with OpenCV and CNNs
• Explain RNN, GRU and LSTM networks
• Generate image captions automatically with CNN and LSTM

This course is for professionals who want a career as a deep learning engineer or data scientist, including:

• Developers moving into data science
• Analytics managers who lead teams of analysts
• Business analysts who want to understand deep learning
• Information architects building predictive analytics skills
• Analysts who want to learn data science methods

You should know Python programming and basic machine learning concepts. Knowledge of statistics also helps.

You need a computer with a good internet connection. If you have any setup questions, our 24x7 support team will help.

The course includes these case studies and demos:

• Classify handwritten digits with TensorFlow 2
• Classify Fashion-MNIST images with a multi-layer perceptron
• Classify dogs and cats with a CNN
• Classify faces by facial expression with a CNN
• Apply transfer learning
• Detect objects with R-CNN
• Remove noise from images with autoencoders
• Detect emotion and gender with OpenCV and a CNN
• Generate image captions automatically with CNN and LSTM

You do all the practicals and projects in a lab environment set up for this deep learning course.