Advanced Deep Learning using Python
12 weeks long course | 100% Online
Learn from leading experts in the industry
Project based learning with 2 industry level projects that learners can showcase on LinkedIn.
Learn Key Tools & Technologies Python
Syllabus
This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects
Course Overview
- This course is designed based on the current industry trends and the needs of individuals who wish to learn deep learning algorithms.
- This course involves advanced algorithms such as artificial, convolutional, and recurrent neural networks.
- Also, this course deals with the practical implementation of Tensorflow in python which is used by most of the organizations.
Course Syllabus
On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.
Week - 01 Artificial Neural Network (Feed Forward Neural Network)
- In the introductory week, we will look into forward feed neural networks.
- In this week's session, we will learn about:
- Neural Network
- Different architectures of Neural Networks
- Importance of Neural Network
- Hyperparameters in Neural Network
- Different types of Gradient descent methods
Week - 02 Activation functions in neural networks
- In the second week, we will look into activation functions.
- In this week, we will learn about:
- Conic sections
- Hyperbolic trigonometric functions
- Sigmoid activation function
- Tanhx activation function
- Relu activation function
- Softmax activation function
Week - 03 Deep Learning
- This week, we will be looking into-
- Terminologies in deep learning
- Nomenclature
- Order of vectorized forms
- Forward propagation derivation with 1 layer
- Back propagation derivation with 1 layer
- Batch size, iteration and epoch
Week - 04 Evaluation of models
- In this week we will look into different topics related to the evaluation of models
- Underfitting
- Overfitting
- Lasso regularization
- Ridge regularization
- Elastic Net regularization
Week - 05 Improvise the model
- In this week, we will learn about:
- Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance, bias, or improve predictions.
- Sparse and convex functions
- Bagging to avoid overfitting
- Boosting to avoid underfitting
- Stacking to avoid underfitting
Week - 06 Optimizers
- In this week we will look into the different types of regularisers and optimizers
- Frobenius norm regularization
- Data augmentation
- Early stopping
- Adam optimizer
- Tensorflow 2.0
Week - 07 Convolutional Neural Network (CNN)-1
- In deep learning, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery.
- In this week we will look at topics related to CNN such as:
- Basics of CNN
- Edge detection
- Padding
- Stride
- Simple CNN
- Difference between CNN & ANN
Week - 08 Convolutional Neural Network (CNN)-2
- Here we will look at topics related to CNN, such as:
- Pooling layers
- Transfer learning
- Examples of CNN architecture
- Combination of different Neural network architecture
- CNN in python
- Pooling layers
Week - 09 Recurrent Neural Network (RNN)-1
- In this week's session, we will learn about:
- RNN Model
- Different types of RNN
- Gradients in RNN
- Back propagation
- Difference between RNN & ANN
Week - 10 Recurrent Neural Network (RNN)-2
- In this week, we will look at topics related to RNN:
- Gated Recurrent Unit (RNN)
- Long short term memory (LSTM)
- Bidirectional RNN
- RNN Implementation in Python
Week - 11 Basics of Natural Language Processing (NLP)
- In this week's session, we will learn about:
- Stop words
- Stemming
- Lemmatization
- Word2vec
- Implementation of word2vec in python
Week - 12 End-to-End ML Project steps
- In this week's session, we will be performing an end-to-end ML/DL project for machine failure.
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
Our courses have been designed by industry experts to help students achieve their dream careers
Industry Projects
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.
Logistic Regression and Gradient Descent
ANN, CNN and RNN
Our courses have been designed by industry experts to help students achieve their dream careers
Ratings & Reviews by Learners
Skill-Lync has received honest feedback from our learners around the globe.
Instructors profiles
Our courses are designed by leading academicians and experienced industry professionals.
1 industry expert
Our instructors are industry experts along with a passion to teach.
8 years in the experience range
Instructors with 8 years extensive industry experience.
Areas of expertise
- Machine Learning
- Deep Learning
Similar Courses
Got more questions?
Talk to our Team Directly
Please fill in your number & an expert from our team will call you shortly.