Course Information
Course Overview
Tensorflow-2 & Keras FFN, CNN, RNN, LSTM, GRU, GAN, Autoencoders, Transfer Learning, Data Augmentation, Text/Image Model
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. 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!
Course Content
- 10 section(s)
- 62 lecture(s)
- Section 1 Introduction
- Section 2 Tensorflow
- Section 3 Deep Learning Model Development Basics
- Section 4 How to implement First Deep Learning Model?
- Section 5 Feed Forward Networks
- Section 6 CONVOLUTIONAL NEURAL NETWORK (CNN)
- Section 7 Keras Preprocessing Layers
- Section 8 Transfer Learning
- Section 9 Sequential Models (Numeric Data)
- Section 10 Sequential Models (Text Data)
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
Reviews
-
FFrancis Cawich
very nice and detailed videos
-
MMustafa A.
This course is an excellent value and successfully provides a high-level overview of TensorFlow and machine learning concepts. For a CS graduate like myself, this course would have been a perfect supplement to university lectures, offering a clear and concise way to understand. From my expertise,the instructor is knowledgeable and on top of the subject.
-
AAndrei
The course suffers from several significant flaws in its design, delivery, and execution. The instructor struggles with English proficiency and lacks the technical vocabulary needed to explain concepts clearly. Students may find the content difficult to follow without prior knowledge of core ideas in AI, deep learning, mathematics, and related fields. Additionally, the instructor would greatly benefit from rehearsing sessions beforehand to improve clarity, confidence, and the overall flow of explanations. The visuals are poorly designed, often appearing as an unorganized collection of images that do not align with the flow of the course. They fail to utilize screen space effectively, leading to a cluttered and overwhelming experience. The exercise sections are incomplete, as they do not provide the datasets required, leaving students without the necessary context to practice effectively. Video editing is another weak point, with mistakes and moments of unpreparedness left unedited. These interruptions detract from the professional feel of the course. Audio quality is also poor, with distracting echoes and background noise throughout. Finally, the chapter titles are vague and unhelpful, making it challenging to navigate the course effectively. To improve, the course creators should focus on: - Enhancing the instructor's delivery through better preparation, rehearsal, and use of appropriate technical terminology. - Designing visuals that align with the course content and utilize screen space effectively. - Providing all necessary materials for exercises to ensure a complete learning experience. - Improving video and audio editing to create a polished, professional presentation. - Creating clear and descriptive chapter titles for easier navigation. While the course covers relevant topics, these issues significantly hinder the overall learning experience.
-
AArnov Paul
It was a complet course for basic of ML and DL. The course material was very good.