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What you'll learn?
- Introduction to Deep Learning and understand how Deep Learning solves problems which Machine Learning cannot.
- Understand fundamentals of Machine Learning and relevant topics of Linear Algebra and Statistics.
- Understand TensorFlow and Keras.
- Basic Python
- Machine Learning
- For all the professionals who are passionate about Deep Learning and want to go ahead and make their career as a Deep Learning Engineer
- Developers aspiring to be a 'Data Scientist'
- Business Analysts who want to understand Deep Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
Curriculum 14 Lectures 06:00:14
Section 1 : Deep Learning
- Lecture 1 :
- Lec 1 - Intro to DL Preview
- Lecture 2 :
- Lec 2 - Single Layered Perceptron
- Lecture 3 :
- Lec 3 - Multi Layered Perceptron
- Lecture 4 :
- Lec 4 - WEIGHTS
- Lecture 5 :
- Lec 5 - Back Propagation
- Lecture 6 :
- Lec 6 - Activation Functions and Bias-Variance TradeOff
- Lecture 7 :
- Lec 7 - ReLU
- Lecture 8 :
- Lec 8 - Weight Initialization
- Lecture 9 :
- Lec 9 - TensorFlow and Keras
- Lecture 10 :
- Lec 10 - ANN ( Artificial Neural Network )
- Lecture 11 :
- Lec 11 - CNN ( Convolutional Neural Network )
- Lecture 12 :
- Lec 12 - Project on CNN
- Lecture 13 :
- Lec 13 - RNN ( Reccurent Neural Networks )
- Lecture 14 :
- Lec 14 - LSTM ( Long Short Term Memory )
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