Neural Networks Course in Ahmedabad
Underneath the big names, a neural network does something simple: multiply, add, squash, pass it on, and adjust when it is wrong. Here you will see every part, then follow one small network through a prediction and a training step you can check by hand. We help you go from that to real deep learning in Python.
At Computer Education And Cybernetics, neural networks are part of our AI path at Maninagar, Nikol and Vatva in Ahmedabad. Counseling comes first, at the centre — we warmly welcome you and your parents to visit before admission.
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+91 75740 10176 · info@cecyours.org
One forward pass, with real numbers
Inputs
- 0.80Soil dryness
- 0.60Sunlight
Hidden (ReLU)
- 0.64Neuron A
- 0.00Neuron B
Output (sigmoid)
- 0.46Needs water?
The right answer is yes (1). One training step later, the same input gives about 0.60.
- Language
- Python
- Practical time
- 80%
- Libraries
- TensorFlow, Keras
- First step
- Counseling
How does a neural network work?
Seven parts. Once each one is clear, larger networks are the same ideas repeated.
A neural network is a model built from layers of simple units. Each unit multiplies its inputs by learned weights, adds a bias and applies an activation function such as ReLU or sigmoid. Forward propagation passes an example through the layers to make a prediction; training measures the loss, uses backpropagation to find each weight’s effect, and adjusts the weights by gradient descent. At Computer Education And Cybernetics (CEC) in Ahmedabad, neural networks are taught as part of the AI learning path in Python.
1. Inputs
Numbers that describe one example, such as soil dryness and hours of sunlight, usually scaled to similar ranges.
2. Weights and bias
Each connection has a weight that says how much an input matters. The bias shifts the result up or down. These are what the network learns.
3. Layers
Neurons are grouped in layers. Each neuron multiplies its inputs by weights, adds them with the bias, and passes the result on.
4. Activation functions
A simple function applied after each sum, such as ReLU or sigmoid. Without it, many layers would act like one straight-line model.
5. Forward propagation
Passing an example through the layers, from inputs to output, to get a prediction.
6. Training
Measure how wrong the prediction is (the loss), work out how each weight affected it (backpropagation), and nudge every weight to reduce it (gradient descent).
7. Prediction
After training on many examples, one forward pass on new data gives the answer. No learning happens at this stage.
Practice task
Write a Python function for one neuron: multiply each input by its weight, add the bias, apply ReLU. Test it with inputs 0.8 and 0.6, weights 0.5 and 0.4, and bias 0. You should get 0.64. What we do with you here: a trainer checks your function against the hand calculation before you move to libraries.
Which activation function goes where?
The hidden layers and the output layer usually need different ones. The output choice depends on the question you are asking.
| Function | What it does | Typical place |
|---|---|---|
| ReLU | Keeps positive values, turns negatives into 0 | Hidden layers in most networks |
| Sigmoid | Squashes any value into 0 to 1 | Output for yes-or-no questions |
| Softmax | Turns several values into probabilities that add to 1 | Output when choosing one of many classes |
| None (linear) | Passes the value through unchanged | Output when predicting a number |
A worked example: one prediction and one training step by hand
Two inputs, two hidden neurons, one output. Every number below can be checked with a calculator.
| Unit | Weighted sum | After activation |
|---|---|---|
| Neuron A | 0.5 × 0.8 + 0.4 × 0.6 + 0 = 0.64 | ReLU → 0.64 |
| Neuron B | −0.6 × 0.8 + 0.3 × 0.6 + 0.1 = −0.20 | ReLU → 0 |
| Output | 0.7 × 0.64 + 0.9 × 0 − 0.6 = −0.152 | Sigmoid → 0.46 |
The question
A tiny network decides whether a plant needs water. One example: soil dryness 0.8, sunlight 0.6, and the right answer is yes (1).
Forward pass
Using the starting weights in the table, the network predicts 0.46. That leans towards “no”, so it is wrong.
What you look at
The loss is about 0.77. Neuron B output 0, so it played no part in this prediction and its weights get no update from this example.
One training step
With a learning rate of 0.5, the output weight from neuron A rises from 0.70 to about 0.87, the output bias from −0.60 to about −0.33, and neuron A’s own weights from 0.50 and 0.40 to about 0.65 and 0.51.
Forward pass again
The same example now gives about 0.60. One step moved the prediction the right way. Real training repeats this over thousands of examples.
What you write down
The starting weights, each forward value, the loss, the updated weights, the new prediction, and a note to check whether neuron B is zero for every example. If it is, it never learns.
When should you use a neural network?
Neural networks shine on some kinds of data and are unnecessary on others. Choosing well is part of the skill.
| Kind of data | Usual choice |
|---|---|
| Images, audio, handwriting | Neural networks usually work best |
| Free text and language | Neural networks, often a pre-trained one |
| Tables of numbers and categories | Often simpler models do as well, and are easier to explain |
| Very few examples | Usually a simpler model; networks need a lot of data |
The simpler models for tables are covered on the machine learning course in Ahmedabad page. What we do with you here: you compare a neural network with a simpler model on the same data and explain which you would use.
Where are neural networks heading?
Bigger networks, used more through pre-trained models and APIs. The mechanics stay the same. What we do with you here: you adapt a pre-trained network after building a small one yourself.
Pre-trained networks are the starting point
Instead of training from scratch, most practical work now adapts an existing network or calls one through an API. Knowing what a layer and a loss are tells you what you are adjusting.
Language models are very large neural networks
The models behind AI chat tools are neural networks with a design called a transformer. The same ideas on this page, weights, layers and training by reducing loss, apply at a much larger scale.
Where AI assistants still fail with neural networks
- It writes training code that runs but never checks whether the loss is actually falling
- It cannot tell you why a particular prediction came out the way it did
- It suggests a neural network for small tables of data where a simpler model would do
Neural network work is moving towards adapting pre-trained networks and using them through APIs, and the language models behind AI chat tools are very large neural networks called transformers. The lasting foundation is understanding weights, layers, activation functions, loss and gradient descent, which apply at every scale. Computer Education And Cybernetics (CEC) teaches these by hand and in code before learners use large pre-trained models.
How we teach neural networks
Paper first, then plain Python, then libraries, so nothing is a black box.
1. Counseling checks your foundation
If Python or basic machine learning is new, we plan that first. Neural networks make sense only on top of it.
2. One step by hand first
You calculate a forward pass and a training step on paper, like the example above, before writing any code.
3. Then the same network in Python
You rebuild it with plain Python and NumPy and check your code gives the same numbers as your paper.
4. Then real networks in TensorFlow and Keras
You train on real data and watch the loss curve, and trainers ask you to explain what each layer is doing.
5. AI assistants used openly
You may use them to write model code. You must still explain the layers, the activations and the loss.
Which CEC courses cover neural networks?
Neural networks are part of our AI courses rather than a separate course. Counseling helps you choose.
Data Science & AI Expert with Python
Machine learning, then deep learning with TensorFlow and Keras.
View courseArtificial Intelligence & Machine Learning
A focus on how models learn and how to evaluate them.
View courseGenerative AI & LLM Development
For working with large language models, the biggest neural networks in everyday use.
View courseSee the whole path on the artificial intelligence course in Ahmedabad page, or where large networks are used on the generative AI course page.
Who should learn neural networks?
Anyone who wants to understand, not just use, today’s AI.
College students
If neural networks felt like formulas on a slide, working one step by hand makes the theory click.
Career changers
We start with Python and basic machine learning first, so neural networks build on solid ground.
Working professionals
Understand what is inside the AI tools you already use, so you can judge where they fit and where they fail.
Placement support and certificates
Being able to explain how a network learns is a strong answer in any AI interview. We help you practise it.
How we stay with you
- We stay with you until you get a job, based on your performance in training, projects and interviews
- Our 5-step placement preparation covers your resume, portfolio, communication and mock interviews
- Course completion certificates are issued after you finish the practical requirements
Our three centres in Ahmedabad
Our AI path runs at Maninagar, Nikol and Vatva. For classroom admission, we warmly invite you and your parents to visit, meet the mentors and see the lab before you decide. Outside Ahmedabad? We offer online admission with a counseling call.
CEC Maninagar
~2 minutes from Maninagar Railway Station
2nd floor, Gopal Tower, Computer Education And Cybernetics, near Maninagar Railway Station Road, Maninagar, Ahmedabad, Gujarat 380008+91 75740 10176CEC Nikol
Near / opposite New DMart, Nikol (Satyam Plaza)
S -25/26, D-mart, Satyam Plaza, Raspan Cross Rd, opp. Suketu Residency, near Nikol, Ankur Chokadi, New India Colony, Nikol, Ahmedabad, Gujarat 382350+91 91049 37871CEC Vatva
Near Vatva Lake Garden; opposite Kashiben Hospital
1st Floor, Computer Education And Cybernetics, Opposite Kashiben Hospital Beside Khodiayar Vav, Near Vatva Lake Garden, Vinzol Crossing Rd, Deriya Para, Vatva, Ahmedabad, Gujarat 382440+91 91571 90839
Related guides
- Machine learning course in AhmedabadThe steps before neural networks
- Artificial intelligence course in AhmedabadThe full AI path
- Generative AI course in AhmedabadWhere large networks are used
- Computer vision course in AhmedabadNeural networks applied to images
- How to start learning AIIf you are just beginning
- Machine learning projects with guidanceTurning models into evidence
- Book a counseling sessionCheck your starting point
Questions about neural networks
What is a neural network?
A model made of layers of simple units. Each unit multiplies its inputs by weights, adds a bias, applies an activation function and passes the result on. Training adjusts the weights so predictions become less wrong.
What is forward propagation?
Passing one example through the network from inputs to output to get a prediction. In our example, inputs 0.8 and 0.6 produced a prediction of 0.46.
Why are activation functions needed?
Without them, any number of layers behaves like a single straight-line model. Functions such as ReLU and sigmoid let the network learn curved, more complex patterns.
How does a neural network learn?
It measures how wrong a prediction is (the loss), works out how each weight contributed (backpropagation), and nudges every weight to reduce the loss (gradient descent). In our example one step moved the prediction from 0.46 to about 0.60.
Is a neural network the same as deep learning?
Deep learning means using neural networks with many layers. The mechanics are the same; there are simply more layers and far more weights.
Do I need machine learning before neural networks?
It helps a lot. Ideas such as training data, loss, overfitting and evaluation come from machine learning and apply directly to neural networks.
Does CEC teach neural networks?
Yes. At Computer Education And Cybernetics (CEC), neural networks and deep learning with TensorFlow and Keras are part of the AI path, mainly in Data Science & AI Expert with Python. Counseling decides the right course for you.
Are neural networks always the best choice?
No. For images, audio and text they usually are. For tables of numbers or small datasets, simpler models often do as well and are easier to explain.
How does CEC help me learn neural networks?
You calculate a step by hand, rebuild it in Python, then train real networks in TensorFlow and Keras, about 80% practical, guided by our 25+ full-time trainers. We stay with you until you get a job, based on your performance in training, projects and interviews.
Where can I learn neural networks in Ahmedabad?
At CEC’s Maninagar, Nikol and Vatva centres. We warmly welcome you and your parents to visit your nearest centre for counseling and admission. Learning from outside Ahmedabad? We guide you on a counseling call and complete your admission online. Call +91 75740 10176 or WhatsApp us with any question.
Understand neural networks from the first weight up
Tell us where you are with Python and machine learning. We will plan your path into neural networks and deep learning.