Deep Learning: Neural Networks With Tensorflow

Master different concepts of Tensorflow with a step-by-step and project-based approach

What you’ll learn
The Basics of Tensors and Variables with Tensorflow
Basics of Tensorflow and training neural networks with TensorFlow
Convolutional Neural Networks
Building more advanced Tensorflow models with Functional API, Model Subclassing and Custom Layers

Requirements
Mac / Windows / Linux – all operating systems work with this course!
No previous TensorFlow knowledge required. Basic understanding of Machine Learning is helpful

Description
Tensorflow is Google’s library for deep learning and artificial intelligence. Deep Learning has been responsible for some amazing achievements recently, such as:Generating beautiful, photo-realistic images of people and things that never existed (GANs)Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)Self-driving cars (Computer Vision)Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)Even creating videos of people doing and saying things they never did (DeepFakes – a potentially nefarious application of deep learning)Tensorflow is the world’s most popular library for deep learning, and it’s built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you gotta know Tensorflow. Deep Learning is one of the most popular fields in computer science today. It has applications in many and very varied domains. With the publishing of much more efficient deep learning models in the early 2010s, we have seen a great improvement in the state of the art in domains like Computer Vision, Natural Language Processing, Image Generation, and Signal Processing. The demand for Deep Learning engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don’t take the beginners into consideration. In this course, we shall take you on an amazing journey in which you’ll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow (the world’s most popular library for deep learning, and built by Google).

Overview
Section 1: Deep Learning: Neural Networks with TensorFlow

Lecture 1 Overview of DLUT

Lecture 2 Scenario of Perceptron

Lecture 3 Creating Neural Network Using TensorFlow

Lecture 4 Perform Multiclass Classification

Lecture 5 Initializing the Model

Lecture 6 Initializing the Model Continued

Lecture 7 Image Processing Using CNN

Lecture 8 Convolution Intuition

Lecture 9 Classifying the Photos of Dogs and Cats

Lecture 10 Deep Learning Neural Networks and its Layers

Lecture 11 Listing Directories

Lecture 12 Import Image Data Generator

Lecture 13 Advance Concept of Transfer Learning Part 1

Lecture 14 Advance Concept of Transfer Learning Part 2

Lecture 15 Advance Concept of Transfer Learning Part 3

Section 2: Project On Tensorflow: Face Mask Detection Application

Lecture 16 Introduction to Project

Lecture 17 Package Installation

Lecture 18 Load Data Pretrained Mode

Lecture 19 Train Model Fit Model

Lecture 20 Load Save Model

Lecture 21 Function to Predict

Lecture 22 Final Result

Section 3: Project on Tensorflow – Implementing Linear Model with Python

Lecture 23 Introduction to Tensorflow with Python

Lecture 24 Installation of Tensorflow

Lecture 25 Basic Data Types for Tensorflow

Lecture 26 Implementing Simple Linear Model

Lecture 27 Creating a Python File

Lecture 28 Optimization of Variable

Lecture 29 Implementing the Constructor Variable

Lecture 30 Printing the Variable Result

Lecture 31 Naming the Variable

Section 4: Deep Learning: Automatic Image Captioning For Social Media With Tensorflow

Lecture 32 Introduction to Course

Lecture 33 Import the Libraries

Lecture 34 Accessing the Caption Dataset for Training

Lecture 35 Accessing the Image DataSet for Trainingb

Lecture 36 Preprocessing the Text Data

Lecture 37 Pre-Process and Load Captions Data

Lecture 38 Loading the Captions for Training and Test Data

Lecture 39 Preprocessing of Image Data

Lecture 40 Loading Features for Train and Test Dataset

Lecture 41 Text Tokenization and Sequence Text

Lecture 42 Data Generators

Lecture 43 Define the Model

Lecture 44 Evaluation of Model

Lecture 45 Test the Model

Lecture 46 Create Streamlit App

Lecture 47 Streamlit Prediction

Lecture 48 Test Streamlit App

Lecture 49 Deploy Streamlit on AWS EC2 Instance

Anyone who wants to pass the TensorFlow Developer exam so they can join Google’s Certificate Network and display their certificate and badges on their resume, GitHub, and social media platforms including LinkedIn, making it easy to share their level of TensorFlow expertise with the world