Deep Learning
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Training Highlights
- The course delves into the fundamentals, and applications of deep learning over an extensive 6 weekends curriculum.
- It starts with basics of TensorFlow, how deep learning models improve over traditional machine learning models, hands on application on artificial neural networks, convolutional neural networks, recurrent neural networks, with long short term memory gates for time series data.
- Learning incorporates detailed theoretical study in conjunction with live hands on exercises on TensorFlow
- Training spread over 6 weekends to give time to participants for assimilation of the class contents with assignments to practice
- Training is delivered in a “Live Online” session by an experienced Data Science professional with vast experience in training.
- Lifetime access to recorded training session videos, so learning stays with you.
- A detailed project to get your hands dirty with latest techniques in neural networks and improving model accuracy by techniques like ensemble, stacking etc.
Why this course ?
With the advent of high computation power and increasing amount of data, traditional machine learning models are not capable of producing highly accurate results. The traditional machine Learning models fail all the more when it comes to unstructured data. Deep learning has seen a rise in application is the last few years, and with new platforms coming in to make it more user friendly to implement, deep learning is the talk of the town. This comprehensive course will deep dive in the applications of neural nets and compare it with traditional models to showcase the strides that deep learning has taken over the previous approaches. It will enable the participants to understand every detail of neural nets from the very basic. The course has been crafted for professionals who would want to see the practical applications of deep learning and not just be satisfied with the theoretical aspects. Business oriented case studies will make the participants industry-ready by the end of the course. The duration of the course has been strategically set for 45 days so that the participants have enough time to complete the exercises and understand the Deep Learning concepts in depth. Training will be conducted over weekends so working professionals can easily attend this course.
Curriculum
- Advanced Machine Learning using python.
- Linear regression
- Under fitting and overfitting
- Regularisation
- Cross validation
- Maximum likelihood estimation
- Stochastic gradient descent
- Hands on exercise
- Feed forward neural networks, Basics of TensorFlow , analogy and comparison with machine learning algorithms.
Loss functions
Gradient based learning
Activation function
Back propagation algorithm
Chain rule
Basics of tensorflow
Implementing back-propagation
- Artificial neural networks and hands on with TensorFlow on a business case
Neural net architecture
Weight initialisation
Batch normalisation
Regularisation
Gradient noise
Momentum
Hands on exercise
Understanding image data, convolutions neural networks and hands on with TensorFlow on a business case
Fully connected layer vs locally connected layer
Filters
Convolutional layer, pooling, dropout
Transfer learning
Hands on exercise
Recurrent neural networks and hands on with TenserFlow on a business case
Sequence
Modelling sequence data
Back propagation through time
Exploding gradient problem
Vector to sequence RNN
Bidirectional RNNs
Encoder decoder sequence to sequence RNN
Hands on exercise
- Long short term memory Vs Gated Recurrent Unit and hands on with TensorFlow On a business case
Idea behind LSTM
Forget gate
GRU
Project