Logistic Regression and Gradient Descent
In this project, you will perform logistic regression and gradient descent on a given dataset using Python.
Computer Science · Course
Advanced Deep Learning using Python
Gain an in-depth understanding about deep learning algorithms and their development using Python.
- Duration
- 12 weeks
- Content
- 22 hours
- Projects
- 2
- Mode
- Online
OverviewWhat you'll
What you'll
be able to do.
Automating machines to process information and replicate knowledge-levels like humans is the primary focus of deep learning and neural networks. Deep learning simplifies the process of gathering, assessing, and interpreting data sets for engineers, and offers a proactive solution to foreseeing events by studying patterns. Engineers can develop and study versatile deep learning codes using Python - making it a fundamental skill in the job industry.
This course is focused on enriching engineers with theoretical and practical knowledge on the following:
- Feed forward neural networks
- Activation functions
- Deep learning algorithms
- Convolutional neural networks
- Recurrent neural networks
- Natural language processing
You will also get to complete projects requiring extensive hands-on work with Python according to modern trends in the industry.
Tools you'll use
Python
Curriculum12 modules,
12 modules,
built with industry.
01Advanced Deep Learning using Python
- Artificial Neural Network (Feed Forward Neural Network)W01
This week will cover
- Neural networks
- Different architectures of Neural Networks
- Importance of Neural Networks
- Hyperparameters in Neural Networks
- Different types of Gradient descent methods
- Activation Functions in Neural NetworksW02
This week will cover
- Conic sections
- Hyperbolic trigonometric functions
- Sigmoid activation function
- Tanhx activation function
- Relu activation function
- Softmax activation function
- Deep LearningW03
This week will cover
- Deep learning terminologies
- Nomenclature
- Order of vectorized forms
- Forward propagation derivation with 1 layer
- Back propagation derivation with 1 layer
- Batch size, iteration and epoch
- Evaluation of ModelsW04
This week will cover
- Underfitting
- Overfitting
- Lasso regularization
- Ridge regularization
- Elastic Net regularization
- Improvising the ModelW05
This week will cover
- Ensemble methods
- Sparse and convex functions
- Bagging to avoid overfitting
- Boosting to avoid underfitting
- Stacking to avoid underfitting
- OptimizersW06
This week will cover
- Frobenius norm regularization
- Data augmentation
- Early stopping
- Adam optimizer
- Tensorflow 2.0
- Convolutional Neural Network (CNN) - Part 1W07
This week will cover
- Basics of CNN
- Edge detection
- Padding
- Stride
- Simple CNN
- Difference between CNN & ANN
- Convolutional Neural Network (CNN)- Part 2W08
This week will cover
- Pooling layers
- Transfer learning
- Examples of CNN architecture
- Combination of different Neural network architecture
- CNN in Python
- Recurrent Neural Network (RNN) - Part 1W09
This week will cover
- RNN Model
- Different types of RNN
- Gradients in RNN
- Back propagation
- Difference between RNN & ANN
- Recurrent Neural Network (RNN) - Part 2W10
This week will cover
- Gated Recurrent Unit (RNN)
- Long short term memory (LSTM)
- Bidirectional RNN
- RNN Implementation in Python
- Basics of Natural Language Processing (NLP)W11
This week will cover
- Stop words
- Stemming
- Lemmatization
- Word2vec
- Implementation of word2vec in Python
- End-to-End ML Project StepsW12
This week will cover
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
- Artificial Neural Network (Feed Forward Neural Network)W01
Industry projects2 projects
2 projects
you'll ship.
Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 2 Projects that are available in this program.
ANN, CNN and RNN
In this project, you will have to work on prediction of machine failure for a given dataset using ANN (Hyperparameters is completely dependent on individuals to come up with the be…
FeeOne fee.
One fee.
Everything included.
Become job ready with our comprehensive industry focused curriculum for freshers & early career professionals
Premium
₹40,000
Inclusive of all charges- 1 Year Access
- Personalized Page
- Live Individual & Group Sessions
- Personalized & Hands-On
- Job-Oriented Industry Relevant Curriculum
FAQQuestions,
Questions,
answered.
What software skills do this certification course include?
This certification course will include the fundamentals of Python and machine learning algorithms in Python.
Will the students get a certificate after this Deep Learning Course completion?
No, students will get a final certificate of achievement after doing the course.
What is the duration of this Advanced deep learning course?
The duration of this certification course is around three months.
Will this course help me get a job?
Yes, this course will help earn a handsome job.
Which companies use deep learning technology?
Companies like Lam Research, Mercedes Benz, Renault & Nissan, Hyundai, and Rolls Royce use this technology.
Who can opt for this course?
Any person who is willing to know this industry better.
Why did deep learning become such a popular skill in such a brief period?
The reason deep learning has become such a popular skill in such a brief period is that companies in the country and across the world are combining deep learning and artificial intelligence into their extant systems to make them intelligent and more productive.
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Next stepNot sure it's
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the right fit?
A counsellor will walk you through the syllabus, projects and fee options — and tell you honestly if another course suits you better.