Top Deep Learning Classes Near Noida 2026

# baseline_nn.py — a real deep learning starting point
# Every deep learning course in Noida should teach this.

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset

# 1. Define a small neural network for tabular/classification data
class BaselineNet(nn.Module):
    def __init__(self, input_dim: int, num_classes: int):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(input_dim, 64),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(64, 32),
            nn.ReLU(),
            nn.Linear(32, num_classes),
        )

    def forward(self, x):
        return self.net(x)

# 2. Prepare data (in a real course, this would be a real dataset)
X = torch.randn(1000, 20)          # 1000 samples, 20 features
y = torch.randint(0, 2, (1000,))   # binary classification

dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, shuffle=True)

# 3. Instantiate model, loss, optimizer
model = BaselineNet(input_dim=20, num_classes=2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-3)

# 4. Train
for epoch in range(5):
    model.train()
    total_loss = 0.0
    for xb, yb in loader:
        optimizer.zero_grad()
        logits = model(xb)
        loss = criterion(logits, yb)
        loss.backward()
        optimizer.step()
        total_loss += loss.item() * len(xb)
    avg_loss = total_loss / len(dataset)
    print(f"Epoch {epoch+1}: loss={avg_loss:.4f}")

# Why this matters:
# - Every real deep learning interview tests whether you understand
#   training loops, loss functions, and optimization.
# - This is the foundational pattern that every architecture
#   (CNNs, RNNs, Transformers) builds on.
# - A course that skips this and goes straight to pre-trained models
#   is not teaching deep learning — it is teaching model usage.
#
# What interviewers look for:
# - Do you understand what loss.backward() actually does?
# - Can you explain why we call optimizer.zero_grad()?
# - Do you know when to use CrossEntropyLoss vs BCEWithLogitsLoss?
# - Can you read the training curve and know if the model is overfitting?

# train_loop.py — the training loop every deep learning course must teach
# This is the piece most beginners skip and most interviews test.

import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from sklearn.metrics import accuracy_score

def train_one_epoch(model, loader, optimizer, criterion, device):
    model.train()
    total_loss = 0.0
    for xb, yb in loader:
        xb, yb = xb.to(device), yb.to(device)

        optimizer.zero_grad()
        logits = model(xb)
        loss = criterion(logits, yb)
        loss.backward()
        optimizer.step()

        total_loss += loss.item() * len(xb)
    return total_loss / len(loader.dataset)

@torch.no_grad()
def evaluate(model, loader, device):
    model.eval()
    all_preds, all_targets = [], []
    for xb, yb in loader:
        xb, yb = xb.to(device), yb.to(device)
        logits = model(xb)
        preds = logits.argmax(dim=1)
        all_preds.append(preds.cpu())
        all_targets.append(yb.cpu())
    preds = torch.cat(all_preds)
    targets = torch.cat(all_targets)
    return accuracy_score(targets, preds)

# In a real course, the data and model come from real projects.
# The point is the shape of the loop:
#   1. train() mode → forward → loss → backward → step
#   2. eval() mode → no_grad → forward → metrics
#   3. Track train loss and val accuracy every epoch
#
# What a great course teaches:
# - Why we switch between train() and eval()
# - Why we wrap evaluation in @torch.no_grad()
# - Why we track both loss and accuracy
# - What overfitting looks like on the curves
# - When to add dropout, weight decay, or early stopping
#
# What a weak course teaches:
# - A pre-written notebook cell that "just works"
# - No explanation of what backward() actually computes
# - No debugging when the loss explodes

# Why this matters for Noida jobs:
# - Interviewers at Paytm, Adobe, and Noida GCCs will ask you to
#   explain a training loop from memory.
# - If your course only taught you to load a pre-trained model,
#   you cannot pass that round.

# deploy_model.py — from notebook to production
# Every deep learning course in Noida should include deployment.

from fastapi import FastAPI
from pydantic import BaseModel
import torch
import torch.nn as nn
import numpy as np

# Load the trained model (saved from the training notebook)
class BaselineNet(nn.Module):
    def __init__(self, input_dim: int, num_classes: int):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(input_dim, 64),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(64, 32),
            nn.ReLU(),
            nn.Linear(32, num_classes),
        )
    def forward(self, x):
        return self.net(x)

model = BaselineNet(input_dim=20, num_classes=2)
model.load_state_dict(torch.load("baseline_model.pt", map_location="cpu"))
model.eval()

app = FastAPI(title="Churn Prediction API")

class CustomerFeatures(BaseModel):
    features: list  # list of 20 floats

@app.post("/predict")
def predict(customer: CustomerFeatures):
    x = torch.tensor([customer.features], dtype=torch.float32)
    with torch.no_grad():
        logits = model(x)
        prob = torch.softmax(logits, dim=1)[0, 1].item()
    return {
        "churn_probability": round(prob, 4),
        "risk_band": "high" if prob > 0.6 else "low",
    }

# Run: uvicorn deploy_model:app --reload
#
# Why this matters:
# - Noida companies don't hire for notebooks — they hire for
#   models that serve real traffic.
# - Deployment is what separates a portfolio project from
#   a homework submission.
# - If your course never covers deployment, your portfolio is
#   missing the single piece that impresses engineering managers.
#
# What interviewers look for:
# - Do you know how to save and load a PyTorch model?
# - Do you understand eval() vs train() mode in production?
# - Do you know why no_grad() matters for inference?
# - Can you handle model versioning and input validation?