What you'll learn:
- DEEP LEARNING
- TENSORFLOW
- KERAS
- AUTOENCODER
- convolutional neural network (CNN)
- recurrent neural network (RNN)
- LSTM (Long Short-Term Memory)
- Gated Recurrent Unit (GRU)
- Keras Callbacks / Checkpoints /early stopping
- Generative adversarial networks (GANs)
- KERAS Preprocessing layers
- Data Augmentation
- Image and Data generators
- Word Embeddings
- Text Classification
- Image labelling classification
- Image caption Generation
- Transfer Learning
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!
This course is designed for ML practitioners who want to enhance their skills and move up the ladder with Deep Learning!
This course is made to give you all the required knowledge at the beginning of your journey so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips, and tricks you would require to work in the Deep Learning space.
It gives a detailed guide on Tensorflow and Keras along with in-depth knowledge of Deep Learning algorithms. All the algorithms are covered in detail so that the learner gains a good understanding of the concepts. One needs to have a clear understanding of what goes behind the scenes to convert a good model to a great model. This course will enable you to develop complex deep-learning architectures with ease and improve your model performance with several tips and tricks.
Deep Learning Algorithms Covered:
1. Feed Forward Networks (FFN)
2. Convolutional Neural Networks (CNN)
3. Recurring Neural Networks (RNN)
4. Long Short-Term Memory Networks (LSTMs)
5. Gated Recurrent Unit (GRU)
6. Autoencoders
7. Transfer Learning
8. Generative Adversarial Networks (GANs)
Our exotic journey will include the concepts of:
1. The most important concepts of Tensorflow and Keras from very basic.
2. The two ways of model building i.e. Sequential and Functional API.
3. All the building blocks of Deep Learning models are explained in detail to enable students to make decisions while training their model and improving model performance.
4. Hands-on learning of Deep Learning algorithms from the beginner level so that everyone can build simple to complex model architectures with clear problem-solving vision and approach with ease.
5. All concepts that you would need for model building lifecycle and problem-solving approach.
6. Data augmentation and generation using Keras preprocessing layers and generators with all the real-life tips and tricks to give you an edge over someone who has just the introductory knowledge which is usually not provided in a beginner course.
7. Hands-on practice on a large number of Datasets to give you a quick start and learning advantage of working on different datasets and problems.
8. Assignments with detailed explanations and solutions after all topics allow you to evaluate and improve yourself on the go.
9. Advance level project so that you can test your skills.
Grab expertise in Deep Learning in this amazing journey with us! We'll see you inside the course!
