Deep Learning with Keras and TensorFlow
About Deep Learning
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. It is a machine learning technique that teaches computers to do what comes naturally to humans. For the time being Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. Also, It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers.
In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance and these Models are trained by using a large set of labeled data and neural
network architectures that contain many layers.
“Artificial intelligence is growing up fast, as are robots whose facial expressions can elicit empathy and make your mirror neurons quiver.”
About this Course
In this course with Keras and Tensorflow certification training, you will become familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and more. After completion of this course, you will be able to build deep learning models, interpret results, and build your own deep learning project.
Moreover, at the end of this course, you will be subjected to a project where you can apply your knowledge thought in the class to solve real-world problems.
Lesson 01 – Course Introduction
Lesson 02 – AI and Deep learning introduction
- What is AI and Deep Learning
- Brief History of AI, Recap-SL, UL and RL, Deep Learning: Successes Last Decade
- Demo and Discussion of Self-Driving Car Object Detection and Applications/Challenges of Deep Learning
- Demo and Discussion of Sentiment Analysis Using LSTM and Full Cycle of a Deep Learning Project
Lesson 03 – Artificial Neural Network
- Biological Neuron Vs Perceptron and Shallow Neural Network Training a Perceptron and Demo
- Role of Activation Functions and Backpropagation
- Optimization, Regularization and Dropout layer
Lesson 04 – Deep Neural Network & Tools
- Deep Neural Network: Why and Applications
- Designing a Deep Neural Network and How to Choose Your Loss Function?
- Tools for Deep Learning Models and Keras and its Elements
- Tensorflow and Its Ecosystem and TFlearn
- Pytorch and its Elements
Lesson 05 – Deep Neural Net optimization, tuning, interpretability
- Optimization Algorithms and SGD, Momentum, NAG, Adagrad, Adadelta, RMSprop, Adam
- Batch Normalization
- Hyperparameter Tuning
- Width vs Depth
Lesson 06 – Convolutional Neural Net
- Success and History
- CNN Network Design and Architecture
- Deep Convolutional Models
Lesson 07 – Recurrent Neural Networks
- Sequence Data and Sense of Time
- RNN Introduction
- Word Embedding and LSTM
- GRUs and LSTM vs GRUs
Lesson 08 – Autoencoders
- Introduction to Autoencoders
- Applications of Autoencoders and Autoencoder for Anomaly Detection
What you will learn from this course:
When you complete this deep learning course with keras and tensorflow, you will be able to accomplish the following:
- You will learn the concepts of Keras and TensorFlow, its main functions, operations, and the execution pipeline.
- Implementations of 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, the application of autoencoders, and Pytorch and its elements.
- Troubleshoot and improve deep learning models.
- Build your own deep learning project.
- Differentiate between machine learning, deep learning, and artificial intelligence.
For learning this course one should have familiarity with programming fundamentals, a fair understanding of the basics of statistics and mathematics, and a good understanding of machine learning concepts.
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- Courses are well structured with self-learning, live classes, and assessments.
- You will find Industry-based course-end project
- Dedicated mentoring session from faculty of industry experts
- You should be familiar with programming fundamentals and Statistics Essentials
- Familiarity with concepts about Machine Learning
Specification: Deep Learning with Keras and TensorFlow