Deep Learning – Neural Networks With Tensorflow

Free Download Deep Learning – Neural Networks With Tensorflow
Published 1/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.10 GB | Duration: 7h 27m
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 There's so much information out there, much of which is outdated and many times don't take the beginners into consideration.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

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