What you would learn in Advanced Deep Learning With TensorFlow course?
This course simplifies the complex Deep Learning concepts like Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short Term Memory (LSTM) and GRU, Gated Recurrent Units(GRU), and many more. TensorFlow, Keras, Google Colab, Real-World Projects, and Case Studies on topics like Regression and Classification have been explained in detail. Advanced Case Studies like Self Driving Cars will be detailedly discussed. There are currently only a handful of instances of case studies. The aim is to cover at least 20 real-world projects within the next few months.
Case studies on subjects like object detection will also be included. TensorFlow and Keras basic concepts and advanced ones were discussed in high depth. This course aims to prepare the learner to solve real-world issues with deep learning. After completing this course, the learner will also pass the TensorFlow Google Certification Exam, which is a prestigious Certification. The student will also receive a certificate of the course's completion from Udemy upon completion.
After this course, the learner will have a solid foundation in the areas of study.
A) Theoretical Deep Learning Concepts.
b) Convolutional Neural Networks
C) Long-short-term memory
D) Generative Adversarial Networks
e) Encoder-Decoder Models
f) Attention Models
g) Object detection
h) Image Segmentation
i) Transfer Learning
J) Open CV using Python
K) Establishing and installing Deep Neural Networks
"l) Professional Google Tensor Flow developer
m) Utilizing Google Colab to write Deep Learning code
(n) Python programming for Deep Neural Networks
The students are encouraged to test practicing the Tensor Flow code while watching the instructional videos on Programming in this course.