2023: Deep Learning Mastery With Tensorflow & Keras

Tensorflow & Keras + FFN, CNN, RNN, LSTM, GRU, GAN, Autoencoders, Transfer Learning, Data Augmentation, Text/Image Model

What you'll learn

DEEP LEARNING

TENSORFLOW

KERAS

AUTOENCODER

convolutional neural network (CNN)

recurrent neural network (RNN)

LSTM (Long Short-Term Memory)

Gated Recurrent Unit (GRU)

Keras Callbacks / Checkpoints /early stopping

Generative adversarial networks (GANs)

KERAS Preprocessing layers

Data Augmentation

Image and Data generators

Word Embeddings

Text Classification

Image labelling classification

Image caption Generation

Transfer Learning

Requirements

Machine Learning Basics

Python

Description

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!This course is designed for ML practitioners who want to enhance their skills and move up the ladder with Deep Learning!This course is made to give you all the required knowledge at the beginning of your journey so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips, and tricks you would require to work in the Deep Learning space.It gives a detailed guide on Tensorflow and Keras along with in-depth knowledge of Deep Learning algorithms. All the algorithms are covered in detail so that the learner gains a good understanding of the concepts. One needs to have a clear understanding of what goes behind the scenes to convert a good model to a great model. This course will enable you to develop complex deep-learning architectures with ease and improve your model performance with several tips and tricks.Deep Learning Algorithms Covered:1. Feed Forward Networks (FFN)2. Convolutional Neural Networks (CNN)3. Recurring Neural Networks (RNN)4. Long Short-Term Memory Networks (LSTMs)5. Gated Recurrent Unit (GRU)6. Autoencoders7. Transfer Learning8. Generative Adversarial Networks (GANs)Our exotic journey will include the concepts of:1. The most important concepts of Tensorflow and Keras from very basic.2. The two ways of model building i.e. Sequential and Functional API.3. All the building blocks of Deep Learning models are explained in detail to enable students to make decisions while training their model and improving model performance.4. Hands-on learning of Deep Learning algorithms from the beginner level so that everyone can build simple to complex model architectures with clear problem-solving vision and approach with ease.5. All concepts that you would need for model building lifecycle and problem-solving approach.6. Data augmentation and generation using Keras preprocessing layers and generators with all the real-life tips and tricks to give you an edge over someone who has just the introductory knowledge which is usually not provided in a beginner course.7. Hands-on practice on a large number of Datasets to give you a quick start and learning advantage of working on different datasets and problems.8. Assignments with detailed explanations and solutions after all topics allow you to evaluate and improve yourself on the go.9. Advance level project so that you can test your skills.Grab expertise in Deep Learning in this amazing journey with us! We'll see you inside the course!

Overview

Section 1: Introduction

Lecture 1 Course Introduction

Lecture 2 02 Introduction to Tensorflow and Keras

Lecture 3 03 Google Collab setup

Section 2: Tensorflow

Lecture 4 04 Tensors Intuition

Lecture 5 05 Tensors Code it!

Lecture 6 06 Tensors Basics Code

Lecture 7 07 Tensorflow Variables

Lecture 8 08 Tensors & Variables Exercise & Solutions

Lecture 9 09 Eager Vs Graph execution

Lecture 10 10 Tf_function Decorator

Section 3: Deep Learning Model Development Basics

Lecture 11 11 Intuition Neural Networks

Lecture 12 12_NeuralNetworks

Lecture 13 13 Approach to Deep Learning problems

Lecture 14 14 Lifecycle of model 5 steps

Lecture 15 15 Sequential Vs Functional API

Section 4: How to implement First Deep Learning Model?

Lecture 16 16 Sequential API

Lecture 17 17 Functional API

Lecture 18 18_ML problem_Cost_Gradient_CV

Lecture 19 19 Activation Functions

Lecture 20 20 Optimizers

Lecture 21 21 Loss functions

Lecture 22 22 Performance Metrics

Lecture 23 23 Tips for Improving Model Performance

Section 5: Feed Forward Networks

Lecture 24 24 Feed Forward Network Implementation and Keras Callbacks

Section 6: CONVOLUTIONAL NEURAL NETWORK (CNN)

Lecture 25 25 Intro to CNN

Lecture 26 26 CNN implementation

Lecture 27 27 CNN Exercise -2 Problem

Lecture 28 28 CNN Exercise -2 Solution

Lecture 29 29 CNN Exercise -3 Problem

Lecture 30 30 CNN Exercise -3 Solution

Section 7: Keras Preprocessing Layers

Lecture 31 31_Keras Preprocessing Layers Intro

Lecture 32 32_Keras Preprocessing Layers Image Augmentation Code

Lecture 33 33_Keras Preprocessing Layers Text Preprocessing Code

Lecture 34 34 Keras Preprocessing Layers Exercise

Lecture 35 35 Keras Preprocessing Layers Solution

Section 8: Transfer Learning

Lecture 36 36 Transfer Learning

Lecture 37 37 Transfer Learning code

Lecture 38 38 Transfer Learning Exercise Xray Dataset

Lecture 39 39 Transfer Learning Solution XrayDataset

Section 9: Sequential Models (Numeric Data)

Lecture 40 RNN Explained

Lecture 41 LSTM & GRU Explained

Lecture 42 41 RNN LSTM Univariate Time Series

Lecture 43 42 RNN LSTM Multiple Time Series

Section 10: Sequential Models (Text Data)

Lecture 44 43 types of Text embeddings

Lecture 45 44 Text embeddings importing

Lecture 46 45 RNN LSTM Text embedding for classification

Section 11: Autoencoders

Lecture 47 46 Autoencoder

Lecture 48 47 Autoencoder Dimensionality Reduction

Lecture 49 48 Autoencoder Anomaly detection exercise

Lecture 50 49 Autoencoder Anomaly detection solution

Section 12: GENERATIVE ADVERSARIAL NETWORKS (GANs)

Lecture 51 50 GANs Introduction

Lecture 52 51 GANs components

Lecture 53 52 GANs Training

Lecture 54 53 GANs Applications Pros & Cons

Lecture 55 54 GANs implementation

Section 13: CAPSTONE Project

Lecture 56 Project Image Captioning Problem

Lecture 57 Project Image Captioning Solution Part-1

Lecture 58 Project Image Captioning Solution Part-2

Lecture 59 Project Image Captioning Solution Part-3

Section 14: Datasets

Beginner ML practitioners eager to learn Deep Learning,Python Developers with basic ML knowledge,Deep Learning practitioners looking to use Tensorflow and Keras,Anyone who wants to learn about deep learning algorithms