Trending February 2024 # Twitter Releases Study Showing Exposure To Brand Tweets Drives Action Online And Offline # Suggested March 2024 # Top 6 Popular

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Twitter released a study this week, in partnership with The Advertising Research Foundation, FOX and DB5, called “Discovering the Value of Earned Audiences — How Twitter Expressions Activate Consumers.”

The study was conducted to determine how exposure to a brand mention in a Tweet affects the actions of consumers online and offline. To conduct this study, a representative sample of over 12,000 people were recruited including male and female Twitter users, across all age groups and devices

As a result of the study, Twitter arrived at three key findings.

1. Brands are an integral part of regular conversation on Twitter.

According to the study, people don’t just follow brands, they talk about them a lot:

80% of the Twitter users surveyed had mentioned a brand in their Tweets during the measurement period of September 2013 through March 2014.

50% had mentioned brands in their Tweets 15 times or more over a seven-month period

99% of Twitter users in the study were exposed to a brand-related Tweet in the month of January alone.

Twitter provides the following key takeaways from these stats:

2. Consumers take action both online and offline after seeing brand mentions in Tweets.

54% of Twitter users reported that they have taken action after seeing brand mentions in Tweets. 23% took action by visiting the brand’s website, while 20% took action by visiting the brand’s Twitter page.

18% of study respondents retweet Tweets mentioning brands.

19% of Twitter users in the study said they’d consider trying the brand after seeing a Tweet from them.

20% conducted a online search for the brand after seeing a brand-related Tweet.

Younger respondents reported the highest likelihood to take some action after being exposed to brand-related Tweets.

Twitter provides the following key takeaways from these stats:

Since Tweet exposure drives actions across platforms including searching, engagement and purchase, integrate Tweet messages and calls to action with campaigns on other media.

3. The source of the Tweet containing a brand mention affects consumer actions.

45% of respondents took brand-related action after seeing Tweets originating from the brand itself, while 63% of respondents took brand-related action after seeing tweets from a non-brand source.

79% of respondents who viewed brand-related tweets both from the brand and a non-brand source reported taking some kind of action online or offline.

Twitter provides the following key takeaways from these stats:

Complement earned media with owned and paid messages as the combination tends to drive greater consumer action and maximize return on your efforts.

For more details from this study, including an infographic illustrating the stats, please see this post on Twitter’s blog.

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Twitter Is A Mobile Whale: 45% Of Daily Tweets From Mobile Devices

Yesterday, at the 2011 Mobilize conference, Twitter’s VP of engineering Michael Abbot spoke with Om Malik regarding Twitter’s overall growth and the role mobile devices play in that growth. Prior to joining Twitter 16 months ago, Abbot led the software and services team at Palm.

Since Abbot joined the company a little over one year ago, Twitter has seen exponential growth. Last summer, Twitter had just over 60 million total daily tweets and that number has increased to over 230 million daily tweets. Amazingly, approximately 45% of the 230 million daily tweets originate on mobile devices.

While discussing whether Twitter is concerned regarding the additional traffic the iOS integration will provide, Abbot indicated he is confident Twitter can handle the traffic volume:

“During the last nine months, there’s been more infrastructure changes at Twitter than there had been in the previous five years at the company. So that whether it be the death of bin Laden, or someone announces a pregnancy, we can handle those issues and you’re not seeing a fail whale.”

Later in the interview, when Abbot was asked if Twitter had plans to change its basic service, he responded:

“We have been and continue to be very focused on that simplified experience of Twitter. I think that’s one of the reasons we’ve seen the growth. People get it, so people use it.”

One of the highlights of Abott’s interview was when he stated Facebook “sucks at everything” and indicated that Twitter is not focused on what its competition is doing.  Abbot said Twitter is committed to keeping their service simple and providing users with an optimal experience. He believes that Twitter’s success is due to the simplified, easy to understand nature of the service.

[Sources Include: All Things D & GigaOM]

Making Ethical Decisions And Taking Action

Recent years have seen much interest in how humans make judgments. Such interest focuses exclusively on the prevalence of dubious decisions and the reasons why people who ought to stay informed execute them. The main goal is to pinpoint the origins of erroneous conclusions that could endanger our clients and us. If we have been working as mental health professionals for any time, we have almost likely encountered at least one ethical challenge that either directly affected us or concerned a colleague we know well.

Self-Deception Red Flags

This somewhat new interest in the decider rather than just the choice aids in explaining a behavior we frequently noticed while sitting on ethics committees. Many of the psychologists who appeared before the committee seemed to be improbable ethical breakers, even if some of them merited to be criminally prosecuted. Warning indicators frequently remained unnoticed because of justifications, intense stress, incapacity in a particular circumstance, or negligence. Therefore, we are likely to act by forces we need not entirely recognize if we digest important information without clear recognition. However, if a situation indicating possible risk is obvious, it is vital to consider it carefully and make necessary adjustments in the following phase.

Making Role-Blending Decisions

A significant fraction of therapists’ worst or most careless decisions result from role mixing. Amid self-serving conditions, frontiers become flimsy and cross a line if not recognized and corrected promptly. Roles become incongruous when one role’s expectations call for actions or behavior that conflicts with another. There are three criteria to measure the harm caused by role merging. First, there is a greater chance of harm as the ideals of professionals and individuals they serve diverge. Second, there is a greater chance of losing objectivity and having divided loyalties as job responsibilities diverge. Third, the risk of exploitation increases when the therapist’s influence and reputation outweigh the client’s needs.

Making Decisions When There is Lead Time

We must emphasize right away that using ethical decision-making techniques does not result in a decision being made. However, a thorough analysis of the circumstance will significantly impact the choice.

Strategies for Decision-Making Ethical Decision-Making Under Behavioural Emergencies and Crisis Conditions

Frenetic communications from clients or their families, threats made by clients to hurt themselves or others, unanticipated client behavior or requests, and startling disclosures throughout a session are not uncommon events. Consequently, ethical conundrums requiring a quick solution can and do emerge abruptly.

Therapists may understandably feel anxious and become more inclined to act less than adequately if they need more time to formulate a properly considered conclusion utilizing a technique like the one we just described. It is also feasible that anxiety can induce unethical, self-serving, or even protective choices. Although behavioral catastrophes and emergencies are frequently used identically, differentiating between the two may be important for making decisions.

A behavioral emergency demands an urgent response and intervention to prevent potential injury. Suicidal or violent behavior, as well as interpersonal victimization, are behavioral crises. The client’s state must be assessed first, and then an intervention to lower the risk of damage must be made. Interventions might be as simple as listening without judgment or as complex as directing inpatient hospitalization.

Finally, a strategy for the subsequent steps needs to be developed for an outside occurrence that upsets a person’s psychological balance and makes it difficult for them to cope. These can range from less serious but stressful situations, including reacting to a spouse who abruptly asks for a separation or losing a job, to the anguish brought on by a life-or-death circumstance. The person may request help or, at the very least, greet it.

When deciding and responding amidst emergency or critical settings, persons in the mental health profession rate highly among those in occupations subject to ethical and statutory constraints. These circumstances apply when therapists are worried about a client’s health, when the appropriate action to take is ambiguous, when the scenario is impassioned, when the clock is ticking or when a bad outcome happens, and the stakes are great. Adapting and both decision-making abilities must be used.

Even though disclosing would undermine trust in the process, alerting the proper authorities would be permissible. Irrespective of the real or potential danger, therapists may experience anxiety or distress if there is an oncoming emergency and they are forced to make several difficult decisions at once. In times of potential disaster, especially when it comes to affairs of life and death, the most socially responsible course of action might entail comforting grieving family members, divulging information that would have remained private under ordinary situations, exercising more patience, or touching clients or their partners more frequently than usual, or even particularly searching for them.

Preparation for Emergencies in Advance Conclusion

Making moral choices can help us maintain our integrity, create a positive image of ourselves in professional situations, and produce work we are pleased with. It can be difficult and time-consuming to properly incorporate ethics into our decision-making properly, yet doing so can improve our reputation and sense of value. By prioritizing ethics in our working practice, we can improve our capacity to act in a manner that reflects our underlying values.

Knowledge Distillation: Theory And End To End Case Study

This article was published as a part of the Data Science Blogathon

on a business problem to classify x-ray images for pneumonia detection.

Image Source: Alpha Coders

What is Knowledge Distillation?

Knowledge Distillation aims to transfer knowledge from a large deep learning model to a small deep learning model. Here size is in the context of the number of parameters present in the model which directly relates to the latency of the model.

Knowledge distillation is therefore a method to compress the model while maintaining accuracy. Here the bigger network which gives the knowledge is called a Teacher Network and the smaller network which is receiving the knowledge is called a Student Network.

 (Image Source: Author, Inspired from Reference [6])

Why make the Model Lighter?

In many applications, the model needs to be deployed on systems that have low computational power such as mobile devices, edge devices. For example, in the medical field, limited computation power systems (example: POCUS – Point of Care Ultrasound) are used in remote areas where it is required to run the models in real-time. From both time(latency) and memory (computation power) it is desirable to have ultra-lite and accurate deep learning models.

But ultra-lite (a few thousand parameters) models may not give us good accuracy. This is where we utilize Knowledge Distillation, taking help from the teacher network. It basically makes the model lite while maintaining accuracy.

Knowledge Distillation Steps

Below are the steps for Knowledge distillation:

3) Train the student network intelligently in coordination with the teacher network: The student network is trained in coordination with the fully trained teacher network. Here forward propagation is done on both teacher and student networks and backpropagation is done on the student network. There are two loss functions defined. One is student loss and distillation loss function. These loss functions are explained in the next paragraph of this article.


Knowledge Distillation Mathematical Equations:

(Image Source: Author, Inspired from Reference [7])

Loss Functions for teacher and student networks are defined as below:

Teacher Loss LT: (between actual lables and predictions by teacher network)

LT = H(p,qT)

Total Student Loss LTS :

LTS =  α * Student Loss + Distallation Loss

LTS =  α* H(p,qs) + H(q̃T, q̃S) 


Distillation Loss = H(

q̃T, q̃S


Student Loss = H(p,qS)



: Loss function (Categorical Cross Entropy or KL Divergence)



and z


: pre-softmax logits

q̃T : softmax(zT/t)

q̃S: softmax(zS/t)

alpha (α) and temperature (t) are hyperparameters.

Temperature t is used to reduce the magnitude difference among the class likelihood values.

These mathematical equations are taken from reference [3].

End to End Case Study

Here we will look at a case study where we will implement the knowledge distillation concept in an image classification problem for pneumonia detection.

About Data:

The dataset contains chest x-ray images. Each image can belong to one of three classes:



Let’s get started!!

Importing Required Libraries:

import numpy as np import matplotlib.pyplot as plt import os import pandas as pd import glob import shutil import tensorflow as tf from tensorflow.keras.models import Model from tensorflow.keras.layers import Conv2D, Dropout, MaxPool2D, BatchNormalization, Input, Conv2DTranspose, Concatenate from tensorflow.keras.losses import SparseCategoricalCrossentropy, CategoricalCrossentropy from tensorflow.keras.optimizers import Adam from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint import matplotlib.pyplot as plt from tensorflow.keras.preprocessing.image import ImageDataGenerator import cv2 from sklearn.model_selection import train_test_split import random import h5py from IPython.display import display from PIL import Image as im import datetime import random from tensorflow.keras import layers

Downloading the data

The data set is huge. I have randomly selected 1000 images for each class and kept 800 images in train data, 100 images in the validation data, and 100 images in test data for each of the classes. I had zipped this and uploaded this selected data into my google drive.

S. No. Class Train Test Validation

1. Normal 800 800 800

2. BACTERIA 100 100 100

3. VIRUS 100 100 100

Downloading the data from google drive to google colab:

#downloading the data and unzipping it from google.colab import drive drive.mount('/content/drive') !unzip "/content/drive/MyDrive/" -d "/content/"

Visualizing the images

We will now look at some images from each of the classes.

for i, folder in enumerate(os.listdir(train_path)): for j, img in enumerate(os.listdir(train_path+"/"+folder)): filename = train_path+"/"+folder + "/" + img img= ax = plt.subplot(3,4,4*i+j+1) ax.set_xlabel(folder+ ' '+ str(img.size[0]) +'x'+ str(img.size[1])) plt.imshow(img, 'gray') ax.set_xlabel(folder+ ' '+ str(img.size[0]) +'x'+ str(img.size[1])) ax.axes.xaxis.set_ticklabels([]) ax.axes.yaxis.set_ticklabels([]) #plt.axis('off') img.close() break

So above sample images suggest that each x-ray image can be of a different size.

Creating Data Generators

We will use Keras ImageDataGenerator for image augmentation. Image augmentation is a tool to get multiple transformed copies of an image. These transformations can be cropping, rotating, flipping. This helps in generalizing the model. This will also ensure that we get the same size (224×224) for each image. Below are the codes for train and validation data generators.

def trainGenerator(batch_size, train_path): datagen = ImageDataGenerator(rescale=1. / 255, rotation_range=5, shear_range=0.02, zoom_range=0.1, brightness_range=[0.7,1.3], horizontal_flip=True, vertical_flip=True, fill_mode='nearest') train_gen = datagen.flow_from_directory(train_path, batch_size=batch_size,target_size=(224, 224), shuffle=True, seed=1, class_mode="categorical" ) for image, label in train_gen: yield (image, label)

def validGenerator(batch_size, valid_path):

datagen = ImageDataGenerator(rescale=1. / 255, )

valid_gen = datagen.flow_from_directory(valid_path, batch_size=batch_size, target_size=(224, 224),shuffle=True, seed=1 )

for image, label in valid_gen:

yield (image, label)

Model 1: Teacher Network

Here we will use the VGG16 model and train it using transfer learning (based on the ImageNet dataset).

We will first define the VGG16 model.

from tensorflow.keras.applications.vgg16 import VGG16

base_model = VGG16(input_shape = (224, 224, 3), # Shape of our images

weights = ‘imagenet’)

Out of the total layers, We will make the first 8 layers untrainable:


for layer in base_model.layers[:8]:

layer.trainable = False

x = layers.Flatten()(base_model.output) # Add a fully connected layer with 512 hidden units and ReLU activation x = layers.Dense(512, activation='relu')(x) #x = layers.BatchNormalization()(x) # Add a dropout rate of 0.5 x = layers.Dropout(0.5)(x) x = layers.Dense(3)(x) #linear activation to get pre-soft logits model = tf.keras.models.Model(base_model.input, x) opti = Adam(learning_rate=1e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.001) model.summary()

As we can see, there are 27M parameters in the teacher network.

One important point to note here is that the last layer of the model does not have any activation function (i.e. it has default linear activation). Generally, there would be a softmax activation function in the last layer as this is a multi-class classification problem but here we are using the default linear activation function to get pre-softmax logits. Because these pre-softmax logits will be used along with the student network’s pre-softmax logits in the distillation loss function.

Hence, we are using from_logits = True in the CategoricalCrossEntropy loss function. This means that the loss function will calculate the loss directly from the logits. If we had used softmax activation, then it would have been from_logits = False.

We will now define a callback for the early stopping of the model and run the model.

Running the model:

earlystop = EarlyStopping(monitor='val_acc', patience=5, verbose=1) filepath="model_save/weights-{epoch:02d}-{val_accuracy:.4f}.hdf5" checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max') callbacks = [earlystop ] vgg_hist =, validation_data = validation_generator, validation_steps=10, steps_per_epoch = 90, epochs = 50, callbacks=callbacks)

Checking the accuracy and loss for each epoch:

import matplotlib.pyplot as plt plt.figure(1) # summarize history for accuracy plt.subplot(211) plt.plot(vgg_hist.history['acc']) plt.plot(vgg_hist.history['val_acc']) plt.title('teacher model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'valid'], loc='lower right') # summarize history for loss plt.subplot(212) plt.plot(vgg_hist.history['loss']) plt.plot(vgg_hist.history['val_loss']) plt.title('teacher model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'valid'], loc='upper right')

Now we will evaluate the model on the test data:

# First, we are going to load the file names and their respective target labels into a numpy array!

from sklearn.datasets import load_files import numpy as np test_dir = '/content/test' def load_dataset(path): data = load_files(path) files = np.array(data['filenames']) targets = np.array(data['target']) target_labels = np.array(data['target_names']) return files,targets,target_labels x_test, y_test,target_labels = load_dataset(test_dir) from keras.utils import np_utils y_test = np_utils.to_categorical(y_test,no_of_classes) # We just have the file names in the x set. Let's load the images and convert them into array. from keras.preprocessing.image import array_to_img, img_to_array, load_img def convert_image_to_array(files): images_as_array=[] for file in files: # Convert to Numpy Array images_as_array.append(tf.image.resize(img_to_array(load_img(file)), (224, 224))) return images_as_array x_test = np.array(convert_image_to_array(x_test)) print('Test set shape : ',x_test.shape) x_test = x_test.astype('float32')/255 # Let's visualize test prediction. y_pred_logits = model.predict(x_test) y_pred = tf.nn.softmax(y_pred_logits) # plot a raandom sample of test images, their predicted labels, and ground truth fig = plt.figure(figsize=(16, 9)) for i, idx in enumerate(np.random.choice(x_test.shape[0], size=16, replace=False)): ax = fig.add_subplot(4, 4, i + 1, xticks=[], yticks=[]) ax.imshow(np.squeeze(x_test[idx])) pred_idx = np.argmax(y_pred[idx]) true_idx = np.argmax(y_test[idx]) ax.set_title("{} ({})".format(target_labels[pred_idx], target_labels[true_idx]), color=("green" if pred_idx == true_idx else "red"))

Calculating the accuracy of the test dataset:

print(model.metrics_names)  loss, acc = model.evaluate(x_test, y_test, verbose = 1) print('test loss = ', loss)  print('test accuracy = ',acc)


Model 2 –S

The student network defined here has a series of 2D convolutions and max-pooling layers just like our teacher network VGG16. The only difference is that number of Convolutions filters in the student network is very less in each layer as compared to the teacher network. This would make us achieve our goal to have a very less number of weights (parameters) to be learned in the student network during training.

Defining the student network:

# import necessary layers  

from tensorflow.keras.layers import Input, Conv2D 

from tensorflow.keras.layers import MaxPool2D, Flatten, Dense, Dropout from tensorflow.keras import Model # input input = Input(shape =(224,224,3)) # 1st Conv Block x = Conv2D (filters =8, kernel_size =3, padding ='valid', activation='relu')(input) x = Conv2D (filters =8, kernel_size =3, padding ='valid', activation='relu')(x) x = MaxPool2D(pool_size =2, strides =2, padding ='valid')(x) # 2nd Conv Block x = Conv2D (filters =16, kernel_size =3, padding ='valid', activation='relu')(x) x = Conv2D (filters =16, kernel_size =3, padding ='valid', activation='relu')(x) x = MaxPool2D(pool_size =2, strides =2, padding ='valid')(x) # 3rd Conv block x = Conv2D (filters =32, kernel_size =3, padding ='valid', activation='relu')(x) x = Conv2D (filters =32, kernel_size =3, padding ='valid', activation='relu')(x) #x = Conv2D (filters =32, kernel_size =3, padding ='valid', activation='relu')(x) x = MaxPool2D(pool_size =2, strides =2, padding ='valid')(x) # 4th Conv block x = Conv2D (filters =64, kernel_size =3, padding ='valid', activation='relu')(x) x = Conv2D (filters =64, kernel_size =3, padding ='valid', activation='relu')(x) #x = Conv2D (filters =64, kernel_size =3, padding ='valid', activation='relu')(x) x = MaxPool2D(pool_size =2, strides =2, padding ='valid')(x) # 5th Conv block x = Conv2D (filters =64, kernel_size =3, padding ='valid', activation='relu')(x) x = Conv2D (filters =64, kernel_size =3, padding ='valid', activation='relu')(x) #x = Conv2D (filters =64, kernel_size =3, padding ='valid', activation='relu')(x) x = MaxPool2D(pool_size =2, strides =2, padding ='valid')(x) # Fully connected layers x = Flatten()(x) #x = Dense(units = 1028, activation ='relu')(x) x = Dense(units = 256, activation ='relu')(x) x = Dropout(0.5)(x) output = Dense(units = 3)(x) #last layer with linear activation # creating the model s_model_1 = Model (inputs=input, outputs =output) s_model_1.summary()

Note that the number of parameters here is only 296k as compared to what we got in the teacher network (27M).

Now we will define the distiller. Distiller is a custom class that we will define in Keras in order to establish coordination/communication with the teacher network.

This Distiller Class takes student-teacher networks, hyperparameters (alpha and temperature as mentioned in the first part of this article), and the train data (x,y) as input. The Distiller Class does forward propagation of teacher and student networks and calculates both the losses: Student Loss and Distillation Loss. Then the backpropagation of the student network is done and weights are updated.

Defining the Distiller:

class Distiller(keras.Model): def __init__(self, student, teacher): super(Distiller, self).__init__() self.teacher = teacher self.student = student def compile( self, optimizer, metrics, student_loss_fn, distillation_loss_fn, alpha=0.5, temperature=2, ): """ Configure the distiller. Args: optimizer: Keras optimizer for the student weights metrics: Keras metrics for evaluation student_loss_fn: Loss function of difference between student predictions and ground-truth distillation_loss_fn: Loss function of difference between soft student predictions and soft teacher predictions alpha: weight to student_loss_fn and 1-alpha to distillation_loss_fn temperature: Temperature for softening probability distributions. Larger temperature gives softer distributions. """ self.student_loss_fn = student_loss_fn self.distillation_loss_fn = distillation_loss_fn self.alpha = alpha self.temperature = temperature def train_step(self, data): # Unpack data x, y = data # Forward pass of teacher teacher_predictions = self.teacher(x, training=False) #model = ... # create the original model teacher_predictions = self.teacher(x, training=False) with tf.GradientTape() as tape: # Forward pass of student # Forward pass of student student_predictions = self.student(x, training=True) # Compute losses student_loss = self.student_loss_fn(y, student_predictions) distillation_loss = self.distillation_loss_fn( tf.nn.softmax(teacher_predictions / self.temperature, axis=1), tf.nn.softmax(student_predictions / self.temperature, axis=1), ) loss = self.alpha * student_loss + distillation_loss # Compute gradients trainable_vars = self.student.trainable_variables gradients = tape.gradient(loss, trainable_vars) # Update weights self.optimizer.apply_gradients(zip(gradients, trainable_vars)) # Update the metrics configured in `compile()`. # Return a dict of performance results = { m.result() for m in self.metrics} results.update( {"student_loss": student_loss, "distillation_loss": distillation_loss} ) return results def test_step(self, data): # Unpack the data x, y = data # Compute predictions y_prediction = self.student(x, training=False) # Calculate the loss student_loss = self.student_loss_fn(y, y_prediction) # Update the metrics. # Return a dict of performance results = { m.result() for m in self.metrics} results.update({"student_loss": student_loss}) return results

Now we will initialize and compile the distiller. Here for the student loss, we are using the Categorical cross-entropy function and for distillation loss, we are using the KLDivergence loss function.

KLDivergence loss function is used to calculate the distance between two probability distributions. By minimizing the KLDivergence we are trying to make student network predict similar to teacher network.

Compiling and Running the Student Network Distiller:

# Initialize and compile distiller distiller = Distiller(student=s_model_1, teacher=model) optimizer=Adam(learning_rate=1e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.001), metrics=['acc'], student_loss_fn=CategoricalCrossentropy(from_logits=True), distillation_loss_fn=tf.keras.losses.KLDivergence(), alpha=0.5, temperature=2, ) # Distill teacher to student distiller_hist =, validation_data = validation_generator, epochs=50, validation_steps=10, steps_per_epoch = 90)

Checking the plot of accuracy and loss for each epoch:

import matplotlib.pyplot as plt plt.figure(1) # summarize history for accuracy plt.subplot(211) plt.plot(distiller_hist.history['acc']) plt.plot(distiller_hist.history['val_acc']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'valid'], loc='lower right') # summarize history for loss plt.subplot(212) plt.plot(distiller_hist.history['student_loss']) plt.plot(distiller_hist.history['val_student_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'valid'], loc='upper right') plt.tight_layout()

Checking accuracy on the test data:

print(distiller.metrics_names) acc, loss = distiller.evaluate(x_test, y_test, verbose = 1) print('test loss = ', loss) print('test accuracy = ',acc)

We have got 74% accuracy on the test data. With the teacher network, we had got 77% accuracy. Now we will change the hyperparameter t, to see if we can improve the accuracy in the student network.

Compiling and Running the Distiller with t = 6:

# Initialize and compile distiller distiller = Distiller(student=s_model_1, teacher=model) optimizer=Adam(learning_rate=1e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.001), metrics=['acc'], student_loss_fn=CategoricalCrossentropy(from_logits=True), #distillation_loss_fn=CategoricalCrossentropy(), distillation_loss_fn=tf.keras.losses.KLDivergence(), alpha=0.5, temperature=6, ) # Distill teacher to student distiller_hist =, validation_data = validation_generator, epochs=50, validation_steps=10, steps_per_epoch = 90)

Plotting the loss and accuracy for each epoch:

import matplotlib.pyplot as plt plt.figure(1)

# summarize history for accuracy  




plt.title('model accuracy')  



plt.legend(['train', 'valid'], loc='lower right')  

 # summarize history for loss  




plt.title('model loss')  



plt.legend(['train', 'valid'], loc='upper right')


Checking the test accuracy:

print(distiller.metrics_names) acc, loss = distiller.evaluate(x_test, y_test, verbose = 1) print('test loss = ', loss) print('test accuracy = ',acc)

With t = 6, we have got 75% accuracy which is better than what we got with t = 2.

This way, we can do more iterations by changing the values of hypermeters alpha (α) and temperature (t) in order to get better accuracy.

Model 3: Student Model without Knowledge Distillation

Now we will check the student model without Knowledge Distillation. Here there will be no coordination with the teacher network and there will be only one loss function i.e. Student Loss.

The student model remains the same as the previous ithout distillation.

Compiling and running the model:

opti = Adam(learning_rate=1e-4, beta_1=0.9, beta_2=0.999, ep , decay=0.001) earlystop = EarlyStopping(monitor='val_acc', patience=5, verbose=1) filepath="model_save/weights-{epoch:02d}-{val_accuracy:.4f}.hdf5" checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max') callbacks = [earlystop ] s_model_2_hist =, validation_data = validation_generator, validation_steps=10, steps_per_epoch = 90, epochs = 50, callbacks=callbacks)

Our model stopped in 13 epochs as we had used early stop callback if there is no improvement in validation accuracy in 5 epochs.

Plotting the loss and accuracy for each epoch:

import matplotlib.pyplot as plt plt.figure(1) # summarize history for accuracy plt.subplot(211) plt.plot(s_model_2_hist.history['acc']) plt.plot(s_model_2_hist.history['val_acc']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'valid'], loc='lower right') # summarize history for loss plt.subplot(212) plt.plot(s_model_2_hist.history['loss']) plt.plot(s_model_2_hist.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'valid'], loc='upper right') plt.tight_layout()

Checking the Test Accuracy:


loss, acc = s_model_2.evaluate(x_test, y_test, verbose = 1)

print(‘test loss = ‘, loss)

print(‘test accuracy = ‘,acc)

Here we are able to achieve 64% accuracy on the test data.

Result Summary:

Below is the comparison of all four models that are made in this case study:

S. No. Model No. of Parameters Hyperparameter Test Accuracy

1 Teacher Model 27 M – 77%

2 Student Model with Distillation 296 k α = 0.5, t = 2 74%

3 Student Model with Distillation 296 k α = 0.5, t = 6 75%


Student Model without Distillation

296 k – 64%

As seen from the above table, with Knowledge distillation, we have achieved 75% accuracy with a very lite neural network. We can play around with the hypermeters α and t to improve it further.


In this article, we saw that Knowledge Distillation can compress a Deep CNN while maintaining the accuracy so that it can be deployed on embedded systems that have less storage and computational power.

We used Knowledge Distillation on the Pneumonia detection problem from x-ray images. By distilling Knowledge from a Teacher Network having 27M parameters to a Student Network having only 0.296M parameters (almost 100 times lighter), we were able to achieve almost the same accuracy. With more hyperparameter iterations and ensembling of multiple students networks as mentioned in reference [3], the performance of the student model can be further improved.


1) Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning 2023.

2) Dataset: Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2024), “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification”, Mendeley Data, V2, doi: 10.17632/rscbjbr9sj.2

3) Designing Lightweight Deep Learning Models for Echocardiography View Classification 2023.

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion. 


30 Best Offline Games For Ipad (Free And Paid)

Gamers love to play every chance they get on any device they can get their hands on. If you’re a regular iPad user, we have no doubt you use that big screen and massive battery to play all the games you can. However, every once in a while you might have found yourself pulled away from the experience because you lost the Internet for a minute. Thankfully, the iPad has a host of some amazing offline games that require no Internet. So whether you’re an iPad gamer looking for some games for commute time or you’re simply tired of the always-online mode, we have something for you. We have compiled a list of the best offline games you can find on the iPad. Get them all and game away.

Best iPad Offline Games

The offline games listed below not only contain the latest releases that you should check out but old classics that you might have missed. If you’re hoping for a particular game, use the table below to find it.

1. Shadow of Death 2

If you love hack and slash combat games then you are sure to fall in love with Shadow of Death 2. The sequel to its popular predecessor continues the story of Maximus, a warrior who has lost his memory. As Maximus, you must continue the fight for your home Aurora to bring back its light. Explore the new six maps of Shadow of Death 2 as you battle your way through demons while equipped with new hack and slash mechanics. Check out Death 2 and show them what you got.

Price: Free (Offers In-App Purchases)

2. Memory Stamps

This one is for the puzzlers among you. Memory Stamps is a self-described elegant puzzle game that is a combination of a few proven methods that enhance memory. Hence instead of solving classic puzzles, you will be shown detail-rich illustrations which will then disappear leaving you to recreate them. The game presents a good combination of aesthetic beauty combined with a challenge. Check this offline game out on the iPad and have fun.

Price: $1.99

Get Memory Stamps 

3. Stickman Revenge – Ninja Game

A mix of roguelike and classic RPG games, Stickman Revenge puts you in charge of battling shadow monsters and defeating them all. Made by the same studios as Shadow of Death 2, Stickman Revenge has graphics focused in a different direction while still remaining mysterious. You can choose from three different Ninjas and win battles to unlock ninja powers. So when you’re in the car and in the mood for some ninja action, consider this offline game for your iPad.

Get Stickman Revenge – Ninja Game

4. Infinite Tanks WWII

Infinite Tanks WWII is an action-packed tank battle offline game for the iPad. Combining elements of card-driven mechanics, the game allows players to mix and match parts of different tanks to build a beast. You can even find the most famous tanks from the WWII era like Sherman M4A1, M18 Hellcat, M26 Pershing, Type 1 Chi-He in this game and strip them apart for customization. Once you have your desired build, drive it away to the open environments. You can choose to take part in 12 different single-player missions or various 7 v 7 online multiplayer matches. Get Infinite Tanks WWII and get building.

Price: $9.99

Get Infinite Tanks WWII 

5. Beyond a Steel Sky

Price: Apple Arcade

Get Beyond a Steel Sky

6. Minecraft Pocket Edition

We don’t think we need to tell you a lot about Minecraft to get you hooked. This massively popular game is available on the iPad and works both online and offline. As a player in Minecraft, you have the ultimate freedom to build to your heart’s fancy and explore infinite worlds. If you have a passion for creating architectural masterpieces and want to get into what is considered one the best building games, get Minecraft right now. Oh, and did we mention it also has a survival mode for when you feel like combat?

Price: $6.99

7. Journey

Journey is a game we have trouble describing. Not because it is not interesting but because it’s too much of it. The world of Journey takes you away into dreamland and makes you a traveler. You can choose to soar above the ruins of what once was and explore the place to discover secrets. Considered App Store’s Editor’s choice game, Journey is an experience that combines enthralling visuals with the calmness of an exploration game. While you can play it offline on your iPad, you can choose to pair up with a stranger if you connect to the Internet.

Price: $4.99

Get Journey

8. Sid Meier’s Civilization VI

Get Sid Meier’s Civilization VI

9. Plague Inc.

Price: $0.99

Get Plague Inc.

10. Asphalt 8 Airborne

Price: Free (Offers In-App Purchases)

Get Asphalt 8 Airborne 

11. Gris

Price: $4.99

12. Monument Valley Series

If you loved Minecraft for its architectural capabilities, you will love the Monument Valley series. Centered around the manipulation of environments, Monument Valley sees players guide various characters through architecture that seems impossible. As the player, it is your responsibility to change the geometry around and make sure the character gets to the other side without any harm. Based on a combination of puzzle and creation, the Monument Valley series is an excellent one. While Monument Valley + focuses on the silent princess Ida, its sequel has a mother and a child as the central characters. Get this offline game for your iPad and traverse away.

Price: $6.99 (Bundle)

Get Monument Valley 1 & 2 

13. The Room Series

While the world of Room might seem there’s a jumpscare around the corner, believe us, there isn’t. This BAFTA Award-winning puzzler series revolves around a single room and the various puzzles that reside in it. As the protagonist, you must follow a string of mysterious letters to an undisclosed location full of cryptic machinery. The Room has the players study and solves various pieces of machinery that are in fact complex puzzles. Set in an environment that eerily resembles horror games, The Room is one game series you can take offline on your iPad and be spooked and intrigued at the same time. Find the entire series below.

Get The Room Series

14. Real Racing 3

Being in the same vein as Asphalt Airborne, Real Racing 3 is a fellow racing game for the iPad with more realism elements blended in. Published by EA, this racing game features over 40 officially licensed tracks at 19 real-world locations. Real Racing 3 offers players plenty of variety when it comes to cars by having over 250 real-world cars including the likes of Bugatti, Aston Martin, Audi, and more. While a bit heavy on iPad’s resources, Real Racing 3 is an excellent racing game you can take offline. Get this game and take part in over 4,000 events including Formula 1.

Price: Free (Offers In-App Purchases)

Get Real Racing 3

15. The Badland Series

Price: $0.99 to 2.99

Get Bandland, Bandland 2 

16. Stardew Valley

If your childhood was spent playing Farmville, Stardew Valley will fit you like a glove. This RPG slash-building game places farming as the central point while also subtly tacking the problems of mental health. As a grandson getting his grandad’s farming land in the will, you move to the countryside to try and salvage a new more peaceful life. Stardew Valley is a farming RPG that breaks the process down to its bits and pieces. From using the right type of tool to till the soil to various seasons that change, Stardew Valley is very detailed. Get this offline farming simulator for the iPad and enjoy cultivating.

Price: $4.99

17. Into the Dead 2

Into the Dead 2 is a step in a different albeit horrifying direction. This shooting slash action Zombie game is based on a unique mechanic. As a survivor in the Zombie Apocolypse, you have to get home to your family which is slowly being overrun. However, the path to get there is dangerous. Into the Dead 2 has players running in a single direction towards their goal. However, the only thing you can do is either turn left and right or jump to avoid the zombies. But you’re not helpless as the game arms the players with various powerful weapons and throwables like hand grenades. And if you feel alone during this scary journey, take a trusty dog along.

Price: Free (Offers In-App Purchases)

Get Into the Dead 2 

18. Grand Theft Auto: San Andreas

Get GTA San Andreas 

19. Alto’s Series

The first sporting game on this list, the Alto series is a beautiful and immersive set of games that you can play offline on the iPad. The story of the series relies on Alto and his friends as they traverse across the various levels. Being a combination of snowboarding and gliding, Alto’s Odyssey and Journey send players flying across landscapes collecting coins, and enjoying the beautiful level design. Accompanied by peaceful soundtracks, Alto’s series is a different type of sports game you should try. Check it out.

Price: $4.99

Get Alto’s Adventure, Alto’s Odyssey

20. Sniper 3D: Gun Shooting Games

Price: Free (Offers In-App Purchases)

Get Sniper 3D

21. Plants Vs Zombies 2

You must have heard of this ever-popular strategy game. Plants vs Zombies 2 is a strategy game that takes a zombie apocalypse and puts a twist on it. Instead of strapping guns to protect your home, you rely on your good old plants for protection. Plants vs Zombies 2 has a wide variety of plants available that you can use to defend your home including Sunflower, Peashooter, Lava Guava, Laser Bean, and more. You must strategically place your plants across the battlefield to stop the Zombies trying to infiltrate your home. The good news is you don’t need the internet to play this offline game on your iPad. Simply download it once and engage in the battle between Plants vs Zombies.

Price: Free (Offers In-App Purchases)


This interesting action game takes an interesting phenomenon and turns it into a fun game. You are a black hole hungry for everything you see. You must consume everything in sight and keep expanding. Consider chúng tôi an alternative version of Snake combined with destruction. While you can play this game offline on the iPad, try going online when you can to compete with other players too. Check it out.

Price: Free (Offers In-App Purchases)


23. Crossy Road

Get Crossy Road 


Limbo refers to the act of not knowing what to do next and being suspended in indecision. This scary and mysterious game on iPad is just that. You play a young boy whose sister has been taken away. As the main character, you find yourself in an overly quiet and dark forest with nobody to rely on. Limbo is a sidescroller game and has gamers walking toward their objective while battling foes in the form of various monsters including giant spiders. The game’s design has been beautifully drawn and is equal shades of terror and mystery. If you’ve been looking for a horror puzzle game, this is it.

Price: $3.99


25. Subway Surfers and Temple Run 2

Price: Free (Offers In-App Purchases)

Get Subway Surfers, Temple Run 2

26. Cover Fire: Gun Shooting

If you’re all about shooting and sniping then you’ll love Cover Fire. Based on making you lead every battle, Cover Fire puts you in the shoes of a soldier who is a shooter and a sniper. As part of the game, you must choose your weapons wisely and build your own team to lead them into war. The game has various areas that serve as battlefields including deserts and forests. A recent update to Cover Fire has also added a challenging story mode for even more action. And the best part is that is game is fully available offline for the iPad. Get to shooting.

Price: Free (Offers In-App Purchases)

27. Jetpack Joyride

Despite being as old as time itself, Jetpack Joyride remains one of the best offline games for the iPad. This simple-to grasp sidescroller game has players piloting a jetpack as they try to escape a laboratory. The controls are super easy to get. All you need to do is control how high or low the jetpack goes by holding your finger on the screen. The player goes through the various levels on the jetpack while dodging multiple threats including guided missiles and lasters. But like Temple Run 2, Jetpack Joyride proceeds to get harder the longer you’re playing it. The only question is, can you keep up?

Price: Free (Offers In-App Purchases)

Get Jetpack Joyride 

28. Table Tennis Touch

If you’ve been looking to play some Table Tennis but the pandemic has stopped you, this game is your answer. A fully offline game for the iPad, Table Tennis touch features realistic graphics that make you feel you’re standing in front of the table. The game has a career mode with full-fledged leagues and tournaments to keep you busy. And if you get tired of playing offline, you can join cross-platform multiplayer matches too. Check out Table Tennis Touch.

Get Table Tennis Touch

29. Mini Metro

This offline iPad game is especially fun to play when you’re traveling in the Metro yourself. Mini Metro is a strategy game focused on designing a subway map. As the designer of the map, it’s your job to draw lines between stations and get your trains running. You will have limited resources to get it done so be prepared to improvise as new stations open up. A simplistic yet challenging game, Mini Metro will keep you entertained for hours on end.

Price: Free (Offers In-App Purchases)

Get Mini Metro

30. Valleys Between

Price: $2.99

Get Valleys Between

Enjoy These Best Offline Games for Your iPad

Ultimate Guide To Ssds (Plus Reviews Of 7 New Drives!)

Installing an SSD in your PC, be it a laptop or a desktop, is one of the easiest and most effective ways to boost the machine’s overall performance. The change won’t be merely noticeable—it will startle you. Your system will boot more quickly, windows and menus will jump open, and programs and data will load much, much faster.

To get the skinny on state-of-the-art consumer SSDs, we brought seven drives from five vendors into the PCWorld Labs and put them through the wringer. We tested Corsair’s Neutron and Neutron GTX drives; Kingston’s HyperX 3K; OCZ’s Vertex 4 and Vector drives; Samsung’s 840 Pro; and the SanDisk Extreme. We also retested Intel’s 240GB Series 335 SSD using our new benchmarking procedure (if you’re curious, read our original review). Each drive delivers either 240GB or 256GB of storage, which is the current sweet spot in terms of price and performance. Each drive we tested proved to be a solid performer that will offer a significant boost over whatever conventional drive your machine has now. Some drives, however, are definitely faster than others.

If you’d like to upgrade a computer equipped with an older second-generation SATA interface (which maxes out at 3 gigbits per second), note that we also checked out the Apricorn Velocity Solo x2, an add-in card that upgrades any computer with an available PCIe 2.0 x2 slot to the newer SATA 6-gbps standard.

But before we dive into those reviews, here’s a primer on SSDs that will tell you everything you need to know about this technology.


The memory/interface controller proved to be a major factor in determining each SSD’s performance. Three of the drives we tested use a SandForce SF-2281 controller: the Kingston HyperX 3K, the SanDisk Extreme, and the Intel Series 335 (the controller firmware on this drive is tweaked to Intel’s specifications). OCZ’s Vector and Vertex 4 drives both use OCZ’s proprietary IndiLinx controllers, namely the Everest 2 in the Vertex 4 and the Barefoot 3 in the Vector. Corsair is blazing a path with its Neutron series drives (the GTX and Neutron) by using Link A Media’s LM87800 controller. Samsung’s 840 Pro utilizes the company’s proprietary MDX controller.

As you’ll see in our performance chart, drives with the IndiLinx, Link A Media, and Samsung MDX controllers boasted significantly faster write speeds than the SandForce-based competition. In fact, counterintuitively, each of the five drives using those controllers wrote faster than they read. The SandForce-based drives were all good readers, but their comparatively slower write speeds dragged down their overall scores.

On the next page (scroll down past product-reviews for the link), I’ll discuss memory types, interfaces, and how we measured performance.


Although the controller plays a big role in determining an SSD’s performance, the type of flash memory inside an SSD is also a huge factor. The SSDs in this roundup used either synchronous or toggle-mode NAND.

You might also encounter the terms SLC (single-level cell), MLC (multi-level cell), and TLC (triple-level cell) when researching SSDs. An SLC NAND cell has two states—on or off—so it can store one bit of data. An MLC NAND cell has two states besides off, so it can store two bits of data, while a TLC NAND cell has three states in addition to off and is therefore capable of storing three bits of data.

While MLC and TLC NAND deliver more capacity in the same physical space, they also bring a trade-off in performance and endurance. SLC NAND is faster and more durable than the other two types, but it’s also more expensive; you’ll find it today only in enterprise-level drives. Very few drives use TLC NAND, because it’s not as durable—it can’t handle as many program/erase cycles (which I’ll explain in a moment) as SLC and MLC can. Each of the drives in this roundup uses MLC NAND.

A note about endurance: All types of NAND flash memory have a limited life span. The MLC memory in consumer SSDs is good for 3000 to 10,000 P/E (program/erase) cycles, which is enough to deliver several years of normal usage. Unlike a mechanical hard drive, an SSD cannot simply write (program) data on top of old data that’s no longer needed; once flash memory has been written to, it must be erased before it can be written to again. Newer SSDs running on modern operating systems (including Windows 7, Windows 8, Mac OS X 10.6.8, and Linux kernel 2.6.28) use the TRIM command (it’s not an acronym, despite the caps) to actively inform the SSD controller of memory cells that contain unneeded data, so the controller can proactively erase those cells and make them available for storage once again.

So how long should you expect an SSD to last? The manufacturers’ warranties provide a clue: Both of OCZ’s drives, Corsair’s Neutron drives, and Samsung’s 840 Pro drives carry a five-year warranty; the rest of the drives we reviewed are warrantied for three years.


While mechanical hard drives don’t come close to saturating the second-generation SATA 3-gbps bus, the latest SSDs are already bumping up the against the limit of third-gen SATA. If you’re adding an SSD to a laptop that has only a SATA 3-gbps interface, save yourself some money and go middle of the road—you’ll get very little benefit out of connecting a SATA 6-gbps drive to the older interface. If you’re upgrading to an SSD on a desktop that has only a SATA 3-gbps interface, buy either a SATA 6-gbps controller card or a SATA 6-gbps piggyback card, such as the Apricorn Velocity Solo x2 (read our review). Under any circumstance, buy a top performer, and in the future you can transfer it into a better system to realize its full potential.


We evaluated the SSDs with a series of real-world data-transfer tests (by “real world,” we mean a commonplace selection of data). Each drive was required to read and write both a 10GB mix of smaller files and folders and a single large 10GB file. To see just how fast the drives could go, we utilized a 16GB RAM disk to avoid any bottlenecks or interaction issues that a hard drive or second SSD might cause.

Our test bed consisted of an Asus P8Z77-V Pro/Thunderbolt motherboard, an Intel Core i7-2600K CPU, and 32GB of Corsair Vengeance 1600MHz DDR3 memory. The operating system was Microsoft Windows 8 (64-bit).

If you’re not looking for the absolute fastest SSD, any of these models will embarrass a mechanical drive—or even last-year’s SSD crop.

When it came to reading data, every drive we tested turned in good numbers. Oddly enough, the 256MB OCZ Vertex 4, which took fourth place overall with its combined reading and writing, was the slowest reader at 393.5 MBps (file mix and large file combined). The highest combined mark, on the other hand, wasn’t tremendously higher: Samsung’s 240GB 840 Pro delivered 450.8 MBps (about 14 percent faster).

Overall, the aforementioned Samsung 840 Pro and OCZ’s 256MB Vector were the stars of the roundup, finishing first and second respectively. The 840 Pro delivered an overall combined read/write speed of 496.2 MBps, and the Vector delivered 489.1 MBps. The 840 Pro finished first in every test except for writing our mix of smaller files and folders, where the Vector bested it. The Corsair Neutron GTX (240GB) placed third with a speed of 459.1 MBps, the OCZ Vertex 4 took fourth place at 449.4 MBps, and the 240GB Corsair Neutron finished a rather distant fifth at 414.3 MBps.

The Kingston HyperX was the most capable of the SandForce-based drives, posting a combined read/write rate of 407 MBps. The 240GB SanDisk Extreme finished next at 385.8 MBps, followed by the 240GB Intel 335 Series at 368.4 MBps.

To establish a baseline, we also tested an older SSD (a 90GB Corsair Force Series 3) and two mechanical hard drives: Seagate’s Barracuda 7200.12 and Western Digital’s VelociRaptor, both of which offer capacity that no current SSD can match: 1TB. The WD VelociRaptor is a very fast enterprise-class hard drive that spins its platters at 10,000 rpm. Note, however, that the Corsair and Seagate products do not represent the respective manufacturers’ latest and greatest technology; we selected them as representative of the boot drives that consumers might be upgrading from. The Seagate 7200.12, for instance, has half as much cache as the newer 7200.14 model with the same total capacity. And the Corsair drive uses slower asynchronous NAND paired with a SandForce 2200 controller that predates the SandForce 2281 used in the newer drives we reviewed.

The Corsair Force Series 3 SSD managed an overall read/write rate of only 190.7 MBps. Seagate’s 7200-rpm hard drive delivered 117.7 MBps, while the VelociRaptor achieved 213 MBps. In plain language, the bargain SSD smoked the Seagate hard drive, but it couldn’t keep pace with the VelociRaptor.

On the next page (scroll down past product-reviews for the link), I’ll tackle the issue of pricing, bundles, and the bottom line.


Though you’ll see the manufacturer’s suggested retail price quoted in our charts, it’s not always indicative of how much you’ll pay. Some vendors provide MSRPs that are actually street prices, while other vendors offer loftier MSRPs that end up being heavily discounted at retail. The 240GB SanDisk Extreme, for example, is list priced at $399, but we saw it at several online retailers for much less than half that amount. On the other hand, Intel priced its 335 series model at $184, but that drive was selling for more at several online retailers.

Based on street prices, the price per gigabyte ranged from about 69 cents to $1.08 per gigabyte for the 240/256GB models we reviewed. Although that’s expensive compared with the 6 cents per gigabyte the Seagate hard drive fetches, or the 24 cents per gigabyte that WD’s VelociRaptor commands, so is a Ferrari compared with a Volkswagen.


Be aware of what comes in the box with the drive you choose. At a minimum, you should get a bracket and screws that let you adapt the 2.5-inch drive to a 3.5-inch bay. Some manufacturers go further and offer cloning software so that you can easily migrate your operating system and software environment from your old drive to the new one. Some manufacturers sell drives under different SKUs, one with just the drive and others with the drive plus accessories. Be sure to make apples-to-apples comparisons when you’re shopping.

The bottom line

Speed is the primary motivation for upgrading to an SSD, so I recommend skipping over the bargain drives in favor of what you really want. The lone exception to that recommendation is for a laptop that has only a SATA 3-gbps interface. In that case, you should still stay away from bargain-bin drives, but make your choice based on price per gigabyte. If you’re upgrading your laptop, be mindful of drive height: Some drives are 9mm high, and many thin-and-light portables can accommodate only 7mm drives.

We reviewed seven of the very latest SSDs for this roundup. The competition was tight, but one drive managed to outperform the rest of the field. You’ll find links to our reviews below and after the jump!

Corsair Neutron (240GB): The middle of the road

Photograph by Robert Cardin

Corsair’s move to the Link A Media LM87800 controller has been a good thing. The Neutron GTX performs better thanks to its faster toggle-mode NAND, but the Neutron with its synchronous MLC NAND is still a very fast drive—fast enough to take the fifth spot among some very tough competition in our roundup.

Read our entire review.

Corsair Neutron GTX (240GB): A gamble pays off

Photograph by Robert Cardin

Corsair’s move to the Link A Media LM87800 controller has paid dividends. Though not quite as fast as the Samsung 840 Pro or the OCZ Vector, the Neutron GTX beat out the OCZ Vertex 4 to take third place overall.

See our complete hands-on review.

Kingston HyperX 3K (240GB): An excellent buy

Photograph by Robert Cardin

Kingston’s HyperX 3K was the best performer among the SandForce SF-2281 drives in our December 2012 roundup, by a fair margin: It took the sixth spot in overall performance. Kingston somehow managed to squeeze significantly better write performance out of this controller than the other vendors using the same part.

Read the rest of our review.


OCZ’s latest drive, the Vector, utilizes the company’s new IndiLinx Barefoot 3 controller in conjunction with synchronous MLC NAND. Said NAND is rated for 550-MBps sequential writing and 530-MBps writing, as well as for 95,000/100,000, 4KB write/read operations per second. Whatever the numbers, the Vector is fast.

Find the entire review.

OCZ Vertex 4 (256GB): Hitting the sweet spot

Photograph by Robert Cardin

While it’s not quite as fast as its OCZ Vector sibling, OCZ’s Vertex 4 is a very speedy SSD. It uses the company’s older IndiLinx Everest 2 controller, but contains the same synchronous MLC NAND used in the Vector. The combination proved fast enough for this drive to take fourth place in overall performance.

Read the entire review.

Samsung 840 Pro (256GB): A screaming-fast SSD

Photograph by Robert Cardin

It’s always easy to write about the best—in this case, the Samsung 840 Pro with its proprietary MDX controller. Samsung also manufactures the toggle-mode MLC memory found in the 840 Pro, and judging from the results of our tests, the company knows what to do with it. The 840 Pro finished first in overall combined reading and writing. It also placed first in three of our four individual read and write tests.

Find all the details in our review.

SanDisk Extreme (240GB): Bang for the buck

Photograph by Robert Cardin

SanDisk’s Extreme SSD is a study in extremes, at least pricewise. With the 240GB version carrying a $399 suggested retail price, you might dismiss it out of hand. That would be a mistake: We found the drive selling online for a mere $165 (as of December 18, 2012), which lowers the drive’s price per gigabyte to just 69 cents—the lowest price in the entire roundup.

Read more about this drive in our review.

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