Which Deep Learning topics are most important?

The perceptron and XOR, activation functions, backpropagation, vanishing gradients, regularisation, CNN architecture with output size numericals, LSTM vs GRU and GANs are asked most often.

Do I need machine learning before deep learning?

It helps. Know the basics of training, loss, overfitting and evaluation from machine learning first, then the deep learning notes will be much easier to follow.

Are these Deep Learning handwritten notes free?

Yes. Fill in the short form on this page and the PDF downloads right away. There is no fee for the notes.

Are these official university notes?

No. They are original revision notes written by the Uncodemy trainer team in a handwritten style. They are not issued or approved by AKTU or any other university, and they do not replace your prescribed textbook or your teacher's class notes.

Which university syllabus do they follow?

They follow the common Deep Learning syllabus taught in most Indian B.Tech CSE and IT, BCA and MCA programmes. Unit order, names and depth differ between universities such as AKTU, IPU, VTU and others, so tick the topics against your own university syllabus before you start revising.

Can I print these notes?

Yes. Print them for personal study, write your own examples in the margins and carry the formula and diagram pages into your last revision before the exam.

Deep Learning Handwritten Notes PDF (Free)

Key takeaways

Conv output size, parameter count and softmax are quick numerical marks.
Learn the CNN and LSTM diagrams well; they appear in most long answers.
Each section has exam tips and formula boxes, and the last page lists the most asked practice questions.
These are original Uncodemy revision notes, not official university notes; check your own syllabus.

About these Deep Learning handwritten notes

This page offers Uncodemy's Deep Learning notes as a free handwritten-style PDF. Deep Learning uses neural networks with many layers to learn patterns from large amounts of data such as images, text and speech. The notes are written in short points with neat tables, labelled diagrams, boxed formulas and exam tips, so you can revise a full unit in one sitting instead of reading a long textbook chapter.

Please note: these are original Uncodemy revision notes. They follow the common syllabus that most Indian universities share for this subject, but they are not official university notes. Check your own university syllabus and add anything it lists that is missing here.

What's inside the Deep Learning notes

The PDF is arranged in 8 sections plus a final page of most asked practice questions. The sections follow the usual unit order of a Deep Learning syllabus, so you can map them to your own units quickly. Here is what each section covers.

Introduction to deep learning

What deep learning is, ML vs DL, and why deep models need more data and compute.

Neural network basics

Biological vs artificial neuron, the perceptron and why it cannot solve XOR, and the multi-layer perceptron.

Activation functions

Sigmoid, tanh, ReLU and its variants, and softmax, with where each one is used.

Training a network

Loss functions, backpropagation with the chain rule, batch, stochastic and mini-batch gradient descent, optimisers such as momentum and Adam, and vanishing and exploding gradients.

Regularisation

Overfitting in deep networks, L1 and L2, dropout, batch normalisation, data augmentation and early stopping.

Convolutional neural networks

Convolution, stride and padding, pooling, CNN architecture and famous architectures.

Recurrent neural networks

RNNs for sequences, the vanishing gradient problem in RNNs, and LSTM and GRU gates.

Advanced models

Autoencoders, GANs with generator and discriminator, and attention with the transformer architecture.

Most asked questions (practice)

Ten exam-style questions, from ML vs DL to GANs and self-attention.

How to use handwritten notes to revise Deep Learning

Deep Learning answers need diagrams, formulas and intuition together, so revise all three from the notes.

Compute the output size and parameter count of two convolution layers by hand.
Work one forward and backward pass on a tiny two-layer network to see how backpropagation updates weights.
Draw the LSTM cell with its forget, input and output gates until you can label it from memory.
Make a table of activation functions with range, formula and one drawback each.

A simple three-pass method works well with handwritten notes. In the first pass, read one section and underline only what you do not already know. In the second pass, close the notes and write the definitions, diagrams and steps for that section on a blank sheet, then compare and correct in a different colour. In the third pass, a day or two later, answer two or three exam-style Deep Learning questions in timed conditions using only memory. Add your own examples and doubts in the margins so the PDF becomes your personal revision copy.

Formulas, diagrams and definitions to memorise

Conv output size = ((n + 2p - f) / s) + 1 for input n, padding p, filter f and stride s.
Parameters of a conv layer = (f x f x input channels + 1) x number of filters.
Sigmoid 1 / (1 + e^-x), tanh, ReLU max(0, x) and softmax formulas with their output ranges.
The gradient descent update rule: new weight = old weight - learning rate x gradient.
LSTM gate diagram and the self-attention formula softmax(Q K^T / sqrt(d)) V.

Common mistakes that cost marks

Forgetting the bias term when counting parameters.
Using sigmoid in the hidden layers of a deep network without mentioning the vanishing gradient problem.
Applying dropout at test time; it is used only during training.
Mixing up the generator and discriminator roles in GAN answers.

Exam focus: Deep Learning topics that carry the most marks

These Deep Learning topics carry the most marks in most university papers.

Perceptron, MLP and the XOR problem.
Activation functions and backpropagation.
Vanishing gradients, regularisation, dropout and batch normalisation.
CNN architecture with output size numericals, LSTM vs GRU, GANs and transformers.

Typical long questions

Differentiate machine learning and deep learning.
Explain the perceptron and why it cannot solve XOR.
Compare sigmoid, tanh, ReLU and softmax.
Explain backpropagation using the chain rule.
What is the vanishing gradient problem and how is it solved?
Explain CNN architecture and find the output size of a conv layer.
Explain LSTM gates and compare LSTM with GRU.

Marks per question change between universities and years, so confirm the pattern on a recent official paper. In long answers, start with a definition, add a labelled diagram or table, explain the steps and finish with an example or comparison. Underline the key term in each answer and leave a line between steps, because examiners mark faster when the structure is easy to see.

A 7-day revision plan for Deep Learning

Day 1: Introduction to deep learning, Neural network basics.
Day 2: Activation functions, Training a network.
Day 3: Regularisation, Convolutional neural networks.
Day 4: Recurrent neural networks.
Day 5: Advanced models.
Day 6: redo the numericals: conv output size, parameter count, one softmax calculation and one small backpropagation step.
Day 7: answer the most asked practice questions at the end of the notes from memory, then solve one official previous year paper in three hours and mark the units that cost you time.

Keep a single sheet of formulas, definitions and diagrams from the notes for the last hour before the exam, and do not start new topics on the final day.

Who these notes are for

B.Tech CSE, IT and AI-ML students preparing for the semester Deep Learning exam.
MCA and M.Tech students with a neural networks or deep learning paper.
Data science learners who want a compact revision of neural network theory.
Freshers revising deep learning basics before ML or AI engineer interviews.

The notes are a revision aid, not a first textbook. If a Deep Learning topic is new to you, learn it from your prescribed book or class lectures first, then come back to the notes to compress and revise it. For practice, pair them with the Deep Learning Important Questions PDF page and your university's official previous year papers. If your syllabus has a topic that is not covered here, you can ask for it through the handwritten notes request page linked below.

Download Deep Learning Handwritten Notes PDF