Deep Learning: Beginner to Advanced
Deep Learning: Beginners to Advanced is a comprehensive course that covers everything from the fundamentals of neural networks to advanced architectures such as CNNs, RNNs and transformers. Taught by an experienced AI professional, the course emphasizes practical learning through hands-on exercises and real-world projects in PyTorch. By the end, you'll have the skills to confidently build, train, and deploy deep learning models for real-world applications.
No Experience
Needed
Start from scratch
and build up
Flexible
Schedule
Learn at your
own pace
Get
Job-Ready
Acquire essential
job skills
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This course includes:
- 13h:10m:01s on-demand video
- 85 Lectures
- 8 Exercises
- 12 Quizzes
- Access on any Device
- Certificate of completion
No Experience Needed
Start from scratch
and build up
Flexible Schedule
Learn at your
own pace
Get Job-Ready
Acquire essential
job skills
What you'll learn in our Deep Learning course?
-
Basics of neural networks and activation functions
-
Hands-on implementation with PyTorch
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Neural network training techniques like backpropagation and gradient descent
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Optimization techniques: Adam, RMSProp, and SGD
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Regularization methods: dropout, L1, L2, batch normalization, and early stopping
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Advanced architectures: CNNs, RNNs, transformers, and autoencoders
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Model evaluation, hyperparameter tuning, and deployment
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Real-world project for vehicle damage detection
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Practicing through Exercises, Quizzes, and Certificate Upon Course Completion
Course Curriculum
85 Lectures | 13h:10m:01s
11:
Convolutional Neural Networks (CNN)
10 Lectures
-
11.1: What is CNN? Convolution, Kernels, Pooling and Beyond
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11.2: Padding and Strides
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11.3: CIFAR10 Image Classification using CNN
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11.4: Data Augmentation
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11.5: Transfer Learning
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11.6: Pre-trained Models – ResNet, EfficientNet, MobileNet etc.
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11.7: Caltech101 Classification Using Transfer Learning
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11.8: Quiz
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11.9: Exercise
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11.10: Chapter Summary
13:
Transformers
15 Lectures
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13.1: Introduction to Transformer Architecture
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13.2: Word Embeddings
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13.3: Contextual Embeddings
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13.4: Overview of Encoder and Decoder
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13.5: Tokenization, Positional Embeddings
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13.6: Attention Mechanism
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13.7: Multi Headed Attention
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13.8: Decoder
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13.9: How Transformers are Trained?
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13.10: Hugging Face: BERT Basics
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13.11: Hugging Face: Spam Classification Using BERT
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13.12: Hugging Face: Next Word Prediction Using GPT2
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13.13: Quiz
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13.14: Exercise
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13.15: Chapter Summary
14:
Project: Car Damage Detection
10 Lectures
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14.1: AtliQ AI’s First Big Client: Vroom Cars
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14.2: Problem Statement & SOW
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14.3: Data Load and Transformation
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14.4: Model Training with CNN
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14.5: Model Training with CNN and Regularization
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14.6: Model Training using Transfer Learning
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14.7: Hyperparameter Tunning using Optuna
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14.8: Model Evaluation and Export
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14.9: Streamlit App
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14.10: FastAPI Server
What our learners experience
Our content is rated 4.9/5 from 604+ Learners
I have been following you since my college from 2018 . You are just awesome.
very good course
Course Instructor/Creator
Get Certified
When You Complete This Course
You receive a ‘Certificate of Completion’ signed and addressed personally by me, your guide and mentor. – Dhaval Patel
Add and share this certificate with your Resume/CV or on your LinkedIn profile.
Frequently Asked Questions
Q.1
What will I learn in this course?
You will learn key deep learning concepts like neural networks, CNNs, RNNs, and transformers, along with hands-on experience using PyTorch. Additionally, you'll build real-world projects and gain practical skills in model training and deployment.
Q.2
How will this course help me in my career?
It is obvious that industries are being transformed through AI. Deep learning as a crucial part of this revolution, powers advanced technologies like computer vision, natural language processing, and autonomous systems. Learning it will help you grow exponentially in your career.
Q.3
What is different in this course compared to hundreds of courses on the internet and free tutorials on YouTube?
On YouTube and online courses, many times an instructor lacks either real industry experience or teaching skills. This course addresses this issue because it is taught by Dhaval Patel who has industry experience of helping clients with AI projects through his company AtliQ Technologies. He is also a YouTuber with 1 million plus subscribers known for his teaching style. You can check our YouTube channel (codebasics) and read comments in our videos to get an understanding of our teaching quality.
Q.4
How is this different from deep learning playlist on codebasics channel?
While the deep Learning playlist on the codebasics channel provides quality learning with hands on practice, it is a few years old. Also, it uses the TensorFlow framework whereas this course uses PyTorch. PyTorch adoption in the industry is much higher than that of Tensorlow. Additionally, this course has many additional topics not covered in that playlist, quizzes, and hands-on exercises. The course provides ad-free, latest learning with hands-on exercises and projects at an affordable price.
Q.5
Will this course guarantee me a job or a promotion?
We believe “job guarantee” is a marketing gimmick ed-tech companies use to increase their sales. Whether a person gets a job or not depends on how well they learn and perform during interviews. It also depends on current job market. Honest, gimmic free teaching is our core value hence we stay away from “job gurantee” but still many of our students have got job by following taking our courses. Please read job success stories on this website to see names of those people.
Q.6
What is the mode of learning?
The sessions are recorded, so it is self-paced.
Q.7
Will there be any updates to this course?
Yes, there will be periodical updates to this course which you can enjoy until your course validity period.
Q.8
What is the validity of this course?
This course is valid for a lifetime.
Q.1
Should I know machine learning before taking this course?
Yes, fundamentals of machine learning are important to get the most out of this course.
Q.2
Who can take this course?
Anyone looking to learn fundamentals of deep learning and gain practical experience in deep learning concepts like CNNs, RNNs, and transformers. However, you should know the fundamentals of machine learning before taking this course.
Q.3
I have never done programming and belong to a non-technical background. Can I take this course?
No, you need to have knowledge of Python Programming, Maths & Statistics and basic knowledge of Machine Learning to take this course.
Q.4
What are the things I need to know before starting this course?
You need to have good knowledge of Python Programming, Maths & Statistics and basic knowledge of Machine Learning before starting this course.
Q.1
How can I help if I have a doubt and need support?
You can use our private discord channel for daily chat support & networking
Q.1
Can I add this course to my resume?
Yes, adding the learnings from this course along with certificate will add weight to you resume.
Q.1
I’m not sure if this course is good enough got me to invest some money. What can I do?
We understand that you could get confused before buying a course. To support your buying decision, we have provided all the necessary information and made several videos available for free. However, Codebasics is all about caring for the learner experience - if you think this course is not for you, you can get no questions asked refund. Checkout our refund policy here - https://codebasics.io/refund-policy
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- 13h:10m:01s on-demand videos
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- Access on any Device
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