Deep Learning with Keras & TensorFlow Certification - eLearning
450,00 EUR
- 34 hours
This Deep Learning course with TensorFlow certification training is developed by industry leaders and aligned with the latest best practices. You’ll master deep learning concepts and models using Keras and TensorFlow frameworks through this TensorFlow course. Learn to implement deep learning algorithms with our TensorFlow training and prepare for a career as a Deep Learning Engineer. Achieve our deep learning certification and gain a competitive edge over your peers in your next interview. Demand for skilled Deep Learning Engineers is booming across a wide range of industries, making this Deep Learning course with Keras and Tensorflow certification training well-suited for professionals at the intermediate to advanced level. We recommend this deep learning. Certification Training, particularly for Software Engineers, Data Scientists, Data Analysts, and Statisticians with an interest in deep learning.
Course timeline
Course Introduction
Lesson 01
- Course Introduction
AI and Deep learning introduction
Lesson 02
- What is AI and Deep Learning
- Brief History of AI
- Recap: SL, UL and RL
- Deep Learning: Successes Last Decade
- Demo and Discussions: Self-Driving Car Object Detection
- Applications of Deep Learning
- Challenges of Deep Learning
- Demo and Discussions: Sentiment Analysis Using LSTM
- Full Cycle of a Deep Learning Project
- Key Takeaways
- Knowledge Check
Acritical Neutral Network
Lesson 03
- Biological Neuron vs Perceptron
- Shallow Neutral Network
- Training a Perception
- Demo Code #1: Perception (Linear Classification)
- Backpropagation
- Role of Activation, Functions and Backpropagation
- Demo code #2: Activation Function
- Demo code #3: Backprop Illustration
- Optimizing
- Regularization
- Dropout layer
- Demo code #4: Dropout Illustration, Lesson- end Exercise (Classification Kaggle Dataset).
- Key Takeaways
- Knowledge Check
- Lesson - end project
Deep Neutral Network & Tools
Lesson 04
- Deep Neural Network: Why and Applications
- Designing a Deep Neural Network
- How to Choose Your Loss Function?
- Tools for Deep Learning Models
- Keras and its Elements
- Demo Code #5: Build a Deep Learning Model - - - Using Keras
- TensorFlow and Its Ecosystem
- Demo Code #6: Build a Deep Learning Model - - - Using Tensorflow
- TFlearn
- Pytorch and its Elements
- Demo Code #7: Build a Deep Learning Model - - - Using Pytorch
- Demo Code #8: Lesson-end Exercise
- Key Takeaways
- Knowledge Check
- Lesson-end Project
Deep Neutral Net optimization, tuning, interpretability
Lesson 05
- Optimization Algorithms
- SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
- Demo code #9: MNIST Dataset
- Batch Normalization
- Demo Code #10
- Exploding and Vanishing Gradients
- Hyperparameter Tuning
- Demo Code #11
- Interpretability
- Demo Code#12: MNIST– Lesson-end
- Project with Interpretability Lessons
- Width vs Depth
- Key Takeaways
- Knowledge Check
- Lesson-end Project
Convolutional Neural Net
Lesson 06
- Success and History
- CNN Network Design and Architecture
- Demo Code #13: Keras
- Demo Code #14: Two Image Type Classification (Kaggle), Using Keras
- Deep Convolutional Models
- Key Takeaways
- Knowledge Check
- Lesson-end Project
Recurrent Neural Networks
Lesson 07
- Sequence Data
- Sense of Time
- RNN Introduction
- Demo Code #5: Share Price Prediction with RNN
- LSTM (Retail Sales Dataset Kaggle)
- Demo Code #16: Word Embedding and LSTM
- Demo Code #17: Sentiment Analysis (Movie Review)
- Key Takeaways
- Knowledge Check
- Lesson - end project
Autoencoders
Lesson 08
- Introduction and Autoencoders
- Applications of Autoencoders
- Autoencoder for Anomaly Detection
- Demo Code #19: Autoencoder Model for MNIST Data
- Knowledge Check
- Lesson - end Project
Project: Pet Classification Model Using CNN
Project 01
The course includes a real-world, industry-based project. Successful evaluation of the following
project is a part of the certification eligibility criteria:
In this project, you build a CNN model that classifies the given pet images correctly into dog and cat images. The code template is given with essential code blocks. TensorFlow can be used to train the data and calculate the accuracy score on the test data.
Learning Outcomes
At the end of this Deep Learning met Keras & TensorFlow eLearning Course, you will be able to:
Understand the concepts of Keras and TensorFlow, its main functions, operations, and the execution pipeline
Implement deep learning algorithms, understand neural networks, and traverse the layers of data abstraction
Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces
Build deep learning models using Keras and TensorFlow frameworks and interpret the results
Understand the language and fundamental concepts of artificial neural networks, application of autoencoders, and Pytorch and its elements
Troubleshoot and improve deep learning models
Build your deep learning project
Differentiate between machine learning, deep learning, and artificial intelligence
Key Features
34 hours of blended learning
One Industry-based course-end project
Interactive learning with Jupyter notebooks integrated labs
Dedicated mentoring session from faculty of industry experts
Who Should Enroll in this Program?
Learners need to possess an undergraduate degree or a high school diploma. Familiarity with programming fundamentals, a fair understanding of the basics of statistics and mathematics, and a good understanding of machine learning concepts.
AI Engineer
Data Scientist
Software Engineer
Students in UG/ PG programs
Data Analyst
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