- Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit.
- Course Curriculum & Colab Toolkit
- BONUS: 10 advantages of TensorFlow
- BONUS: Learning Path
Online
₹ 3,499
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
A large neural network library that integrates with TensorFlow is called Keras. TensorFlow is an open-sourced end-to-end framework and library for many tasks involving machine learning. A Complete Guide on TensorFlow 2.0 using Keras API certification course is developed by Hadelin de Ponteves - AI Entrepreneur, Ligency I Team - an educational organization that helps data scientists in acquiring skills & Luka Anicin AI Engineer & Entrepreneur and is made amiable by Udemy.
A Complete Guide on TensorFlow 2.0 using Keras API online training comprises 13 hours of digital lessons accompanied by 13 articles and 3 downloadable resources and is arranged to cover every topic from neural networks and training to put it in use. With A Complete Guide on TensorFlow 2.0 using Keras API online course, individuals will learn about data validation, dataset preprocessing, machine learning, CNN, ANN, and RNN as well as how to create a stock trading bot using reinforcement learning.
The highlights
- Certificate of completion
- Self-paced course
- 13 hours of pre-recorded video content
- 13 articles
- 3 downloadable resources
Program offerings
- Online course
- Learning resources
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
certificate availability
certificate providing authority
What you will learn
After completing A Complete Guide on TensorFlow 2.0 using Keras API online certification, individuals will obtain a better understanding of the functionalities and techniques associated with Keras and Tensorflow for data science and machine learning operations. In this TensorFlow course, individuals will explore the functionalities of the convolutional neural network, artificial neural networks, recurrent neural networks, and Deep-Q networks as well as will acquire the skills to create Fashion API with Flask and TensorFlow 2.0. In this TensorFlow certification, individuals will learn about the difference between Tensorflow 1.0 and Tensorflow 2.0 as well as will acquire the skills to create a stock market trading bot using reinforcement learning. Individuals will also learn about concepts involved with data validation, and dataset preprocessing.
The syllabus
Introduction
TensorFlow 2.0 Basics
- From TensorFlow 1.x to TensorFlow 2.0
- Constants, Variables, Tensors
- Operations with Tensors
- Strings
Artificial Neural Networks
- Project Setup
- Data Preprocessing
- Building the Artificial Neural Network
- Training the Artificial Neural Network
- Evaluating the Artificial Neural Network
- Artificial Neural Network Quiz
- HOMEWORK: Artificial Neural Networks
- HOMEWORK SOLUTION: Artificial Neural Networks
Convolutional Neural Networks
- Project Setup & Data Preprocessing
- Building the Convolutional Neural Network
- Training and Evaluating the Convolutional Neural Network
- Convolutional Neural Networks Quiz
- HOMEWORK: Convolutional Neural Networks
- HOMEWORK SOLUTION: Convolutional Neural Networks
Recurrent Neural Networks
- Project Setup & Data Preprocessing
- Building the Recurrent Neural Network
- Training and Evaluating the Recurrent Neural Network
- Recurrent Neural Network Quiz
Transfer Learning and Fine Tuning
- What is Transfer Learning?
- Project Setup
- Dataset preprocessing
- Loading the MobileNet V2 model
- Freezing the pre-trained model
- Adding a custom head to the pre-trained model
- Defining the transfer learning model
- Compiling the Transfer Learning model
- Image Data Generators
- Transfer Learning
- Evaluating Transfer Learning results
- Fine Tuning model definition
- Compiling the Fine Tuning model
- Fine Tuning
- Evaluating Fine Tuning results
- Transfer Learning quiz
Deep Reinforcement Learning Theory
- What is Reinforcement Learning?
- The Bellman Equation
- Markov Decision Process (MDP)
- Q-Learning Intuition
- Temporal Difference
- Deep Q-Learning Intuition - Step 1
- Deep Q-Learning Intuition - Step 2
- Experience Replay
- Action Selection Policies
Deep Reinforcement Learning for Stock Market trading
- Project Setup
- AI Trader - Step 1
- AI Trader - Step 2
- AI Trader - Step 3
- AI Trader - Step 4
- AI Trader - Step 5
- Dataset Loader function
- State creator function
- Loading the dataset
- Defining the model
- Training loop - Step 1
- Training loop - Step 2
Data Validation with TensorFlow Data Validation (TFDV)
- Project Setup
- Loading the pollution dataset
- Creating dataset Schema
- Computing test set statistics
- Anomaly detection with TensorFlow Data Validation
- Preparing Schema for production
- Saving the Schema
- What's next?
Dataset Preprocessing with TensorFlow Transform (TFT)
- Project Setup
- Initial dataset preprocessing
- Dataset metadata
- Preprocessing function
- Dataset preprocessing pipeline
- What's next?
Fashion API with Flask and TensorFlow 2.0
- Project Setup
- Importing project dependencies
- Loading a pre-trained model
- Defining the Flask application
- Creating classify function
- Starting the Flask application
- Sending API requests over internet to the model
Image Classification API with TensorFlow Serving
- What is the TensorFlow Serving?
- TensorFlow Serving architecture
- Project setup
- Dataset preprocessing
- Defining, training and evaluating a model
- Saving the model for production
- Serving the TensorFlow 2.0 Model
- Creating a JSON object
- Sending the first POST request to the model
- Sending the POST request to a specific model
TensorFlow Lite: Prepare a model for a mobile device
- What is the TensorFlow Lite?
- Project setup
- Dataset preprocessing
- Building a model
- Training, evaluating the model
- Saving the model
- TensorFlow Lite Converter
- Converting the model to a TensorFlow Lite model
- Saving the converted model
- What's next?
Distributed Training with TensorFlow 2.0
- What is the Distributed Training?
- Project Setup
- Dataset preprocessing
- Defining a non-distributed model (normal CNN model)
- Setting up a distributed strategy
- Defining a distributed model
- Final evaluation - Speed test: normal model vs distributed model
Annex 1 - Artificial Neural Networks Theory
- Plan of Attack
- The Neuron
- The Activation Function
- How do Neural Networks Work?
- How do Neural Networks Learn?
- Gradient Descent
- Stochastic Gradient Descent
- Backpropagation
Annex 2 - Convolutional Neural Networks Theory
- Plan of Attack
- What are Convolutional Neural Networks?
- Step 1 - Convolution
- Step 1 Bis - ReLU Layer
- Step 2 - Max Pooling
- Step 3 - Flattening
- Step 4 - Full Connection
- Summary
- Softmax & Cross-Entropy
Annex 3 - Recurrent Neural Networks Theory
- Plan of Attack
- What are Recurrent Neural Networks?
- Vanishing Gradient
- LSTMs
- LSTM Practical Intuition
- LSTM Variations
Bonus Lectures
- SPECIAL COVID-19 BONUS
- ***YOUR SPECIAL BONUS***
- **FREE LEARNING RESOURCES FOR YOU**
Instructors
Mr Hadelin de Ponteves
Co-founder
Udemy
Mr Luka Anicin
AI Engineer
Freelancer