https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/259391928/

1. Meetup Special Edition
TensorFlow Dev Summit Viewing Party
19 Marzo 2019, LUISS EnLabs

2. Agenda
19:00-19:15 Ingresso e Registrazione
19:15-19:30 Le principali novità del TF Dev Summit
(Simone Scardapane & Norman Di Palo)
19:30-19:50 Viewing Party
19:50-20:15 In Codice Ratio – Replica!
(Elena Nieddu)
20:15-21:00 News, Open Mic, Pizza, Birra & Networking!

3. TF in Retrospettiva
41,000,000 50,000+ 9,900+ 1,800+
downloads commits pull requests contributors

4. TF in Retrospettiva

5. TF oggi

6. TF 2.0
pip install -U --pre tensorflow

7. Keras for the win

8. Eager execution by default
data = tf.data.TFRecordDataset(['file1', 'file2’])
data = data.map(parse_fn).batch(32)
for row in data.take(3):
print(row)
>>> (<tf.Tensor: id=38, shape=(32, 28, 28),
dtype=float64,
numpy= array([[ 0.070588, …, 0.498039]])>
...
model.fit(data, epochs=1)

9. DistributedStrategy
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(64, input_shape=[10]),
tf.keras.layers.Dense(64, activation='relu’),
tf.keras.layers.Dense(10, activation='softmax’)
])
model.compile(optimizer='adam’,
loss='categorical_crossentropy’,
metrics=['accuracy'])

10. Swift For TensorFlow

11. Swift For TensorFlow
import Python
let np = Python.import("numpy")
let x = np.array([[1, 2], [3, 4]])
let y = np.array([11, 12])
print(x.dot(y)) // [29 67]

12. Swift For TensorFlow
import TensorFlow
struct MyModel: Layer {
var conv = Conv2D<Float>(filterShape: (5, 5, 3, 6))
var maxPool = MaxPool2D<Float>(poolSize: (2, 2), strides: (2, 2))
var flatten = Flatten<Float>()
var dense = Dense<Float>(inputSize: 16 * 5 * 5, outputSize: 10)
@differentiable
func applied(to input: Tensor<Float>, in context: Context) -> Tensor<Float> {
return input.sequenced(in: context, through: conv, maxPool, flatten, dense)
}
}

13. Altre novità
• Autograph
• TensorFlow Lite
• TensorFlow.js 1.0
• TensorFlow datasets! (finalmente)
• Novità in TensorBoard
• Molte novità per le community (TF SIG)
• …

14. Nuovi corsi

15. Se volete sperimentare
https://iaml.it/blog/novita-tensorflow-2
https://colab.research.google.com/drive/14j1skr2TSh3g4oDaT2DAUxg08jkqBJoo

16. Viewing Party!
https://www.youtube.com/watch?v=b5Rs1ToD9aI&t=1641s

17. Open Mic
Open Mic!

18. Seguici!
https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/
https://www.facebook.com/groups/datascienceroma/
http://www.iaml.it/member
https://www.linkedin.com/company/iaml/
https://www.facebook.com/machinelearningitalia/
https://www.slideshare.net/MeetupDataScienceRoma
https://twitter.com/iaml_it