2023: Deep Learning Mastery With Tensorflow & Keras

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1. Introduction

1. Course Introduction.mp4

06:58

2. 02 Introduction to Tensorflow and Keras.mp4

07:15

3. 03 Google Collab setup.mp4

07:19

2. Tensorflow

1. 04 Tensors Intuition.mp4

07:16

2.1 01 tensorflow basics class tensors variables.zip

2. 05 Tensors Code it!.mp4

26:30

3. 06 Tensors Basics Code.mp4

31:33

4. 07 Tensorflow Variables.mp4

18:24

5.1 exercise 1 problems solutions.zip

5. 08 Tensors & Variables Exercise & Solutions.mp4

25:07

6.1 02 graphs and tf.functions.zip

6. 09 Eager Vs Graph execution.mp4

07:40

7. 10 Tf function Decorator.mp4

17:09

3. Deep Learning Model Development Basics

1. 11 Intuition Neural Networks.mp4

08:55

2. 12 NeuralNetworks.mp4

46:30

3. 13 Approach to Deep Learning problems.mp4

07:13

4. 14 Lifecycle of model 5 steps.mp4

05:14

5. 15 Sequential Vs Functional API.mp4

14:13

4. How to implement First Deep Learning Model

1.1 03 model building - seq vs functional api.zip

1. 16 Sequential API.mp4

58:23

2. 17 Functional API.mp4

24:40

3. 18 ML problem Cost Gradient CV.mp4

28:03

4. 19 Activation Functions.mp4

24:46

5. 20 Optimizers.mp4

36:48

6. 21 Loss functions.mp4

17:52

7. 22 Performance Metrics.mp4

02:11

8. 23 Tips for Improving Model Performance.mp4

24:18

5. Feed Forward Networks

1.1 04 feed forward networks.zip

1. 24 Feed Forward Network Implementation and Keras Callbacks.mp4

56:15

6. CONVOLUTIONAL NEURAL NETWORK (CNN)

1. 25 Intro to CNN.mp4

39:05

2.1 05 cnn with cifar10.zip

2. 26 CNN implementation.mp4

47:32

3. 27 CNN Exercise -2 Problem.mp4

01:01

4.1 exercise 2. mnist classifier using cnn.zip

4. 28 CNN Exercise -2 Solution.mp4

36:21

5. 29 CNN Exercise -3 Problem.mp4

00:32

6.1 exercise 3. fashion-mnist classifier using cnn.zip

6. 30 CNN Exercise -3 Solution.mp4

16:33

7. Keras Preprocessing Layers

1. 31 Keras Preprocessing Layers Intro.mp4

10:23

2.1 06 keras preprocessing layers.zip

2. 32 Keras Preprocessing Layers Image Augmentation Code.mp4

28:18

3. 33 Keras Preprocessing Layers Text Preprocessing Code.mp4

37:17

4. 34 Keras Preprocessing Layers Exercise.mp4

00:35

5.1 exercise-4 keras preprocessing layer cats n dogs.zip

5. 35 Keras Preprocessing Layers Solution.mp4

09:24

8. Transfer Learning

1. 36 Transfer Learning.mp4

15:29

2.1 07 transfer learning on cats and dogs dataset.zip

2. 37 Transfer Learning code.mp4

56:53

3. 38 Transfer Learning Exercise Xray Dataset.mp4

00:53

4.1 exercise 5- transfer learning chest xray dataset.zip

4. 39 Transfer Learning Solution XrayDataset.mp4

25:12

9. Sequential Models (Numeric Data)

1. RNN Explained.mp4

33:30

2. LSTM & GRU Explained.mp4

17:01

3.1 08 lstm time series univariate.zip

3. 41 RNN LSTM Univariate Time Series.mp4

39:32

4.1 09 lstm multiple time series.zip

4. 42 RNN LSTM Multiple Time Series.mp4

33:57

10. Sequential Models (Text Data)

1. 43 types of Text embeddings.mp4

20:13

2.1 10 text embeddings.zip

2. 44 Text embeddings importing.mp4

40:16

3.1 11 text embeddings lstm classifier.zip

3. 45 RNN LSTM Text embedding for classification.mp4

40:41

11. Autoencoders

1. 46 Autoencoder.mp4

16:32

2.1 12 autoencoders dimensionality reduction.zip

2. 47 Autoencoder Dimensionality Reduction.mp4

34:19

3. 48 Autoencoder Anomaly detection exercise.mp4

02:16

4.1 exercise 6 anomaly detection using autoencoders.zip

4. 49 Autoencoder Anomaly detection solution.mp4

26:59

12. GENERATIVE ADVERSARIAL NETWORKS (GANs)

1. 50 GANs Introduction.mp4

15:12

2. 51 GANs components.mp4

15:56

3. 52 GANs Training.mp4

18:27

4. 53 GANs Applications Pros & Cons.mp4

09:23

5.1 13 gan fashion mnist generator.zip

5. 54 GANs implementation.mp4

47:55

13. CAPSTONE Project

1. Project Image Captioning Problem.mp4

07:15

2.1 14 project 1 image captioning.zip

2. Project Image Captioning Solution Part-1.mp4

24:43

3. Project Image Captioning Solution Part-2.mp4

39:38

4. Project Image Captioning Solution Part-3.mp4

25:37

Description

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

Who is this for?

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

What You Need to Know?

Machine Learning Basics

Python

More details

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 worldâ€™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 donâ€™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. Autoencoders
7. Transfer Learning
8. 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!
Who this course is for:
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

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I have done B.tech in Computer Science Engineering and 10 + years of experience as a professional instructor and trainer for Data Science and programming. During the course of my career I have developed a skill set in analyzing data and I love sharing my knowledge to help other people learn the power of programming, the ability to analyze data, as well as present the data in clear and beautiful visualizations.I am a Data Scientist and have experience in python, Deep learning, NLP and Big Data. I provide in-person data science, Machine Learning and Deep Learning training to Data science enthusiasts with 0 to 30+ years of Experience. I believe in learning by doing, hence all of my courses will give an in-depth knowledge of concepts followed by detailed explanations of codes, tips and tricks which I have learnt over years. The sample problems and examples will allow you to explore more and give you enough practice to gain confidence at each and every concept. I am here to help you stay on the cutting edge of Data Science and Technology.To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you!

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